add 6 skills to repo + update skill-review for xiaoming
- Add code-interpreter, kokoro-tts, remotion-best-practices, research-to-paper-slides, summarize, tavily-tool to source repo - skill-review: add main/xiaoming agent mapping in handler.ts + SKILL.md - tts-voice: handler.ts updates from agent workspace Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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skills/code-interpreter/SKILL.md
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skills/code-interpreter/SKILL.md
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---
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name: code-interpreter
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description: Local Python code execution for calculations, tabular data inspection, CSV/JSON processing, simple plotting, text transformation, quick experiments, and reproducible analysis inside the OpenClaw workspace. Use when the user wants ChatGPT-style code interpreter behavior locally: run Python, analyze files, compute exact answers, transform data, inspect tables, or generate output files/artifacts. Prefer this for low-risk local analysis; do not use it for untrusted code, secrets handling, privileged actions, or network-dependent tasks.
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---
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# Code Interpreter
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Run local Python code through the bundled runner.
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## Safety boundary
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This is **local execution**, not a hardened container. Treat it as a convenience tool for trusted, low-risk tasks.
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Always:
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- Keep work inside the OpenClaw workspace when possible.
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- Prefer reading/writing files under the current task directory or an explicit artifact directory.
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- Keep timeouts short by default.
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- Avoid network access unless the user explicitly asks and the task truly needs it.
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- Do not execute untrusted code copied from the web or other people.
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- Do not expose secrets, tokens, SSH keys, browser cookies, or system files to the script.
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Do not use this skill for:
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- system administration
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- package installation loops
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- long-running servers
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- privileged operations
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- destructive file changes outside the workspace
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- executing arbitrary third-party code verbatim
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## Runner
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Run from the OpenClaw workspace:
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```bash
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python3 {baseDir}/scripts/run_code.py --code 'print(2 + 2)'
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```
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Or pass a script file:
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```bash
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python3 {baseDir}/scripts/run_code.py --file path/to/script.py
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```
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Or pipe code via stdin:
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```bash
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cat my_script.py | python3 {baseDir}/scripts/run_code.py --stdin
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```
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## Useful options
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```bash
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# set timeout seconds (default 20)
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python3 {baseDir}/scripts/run_code.py --code '...' --timeout 10
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# run from a specific working directory inside workspace
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python3 {baseDir}/scripts/run_code.py --file script.py --cwd /home/selig/.openclaw/workspace/project
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# keep outputs in a known artifact directory inside workspace
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python3 {baseDir}/scripts/run_code.py --file script.py --artifact-dir /home/selig/.openclaw/workspace/.tmp/my-analysis
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# save full stdout / stderr
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python3 {baseDir}/scripts/run_code.py --code '...' --stdout-file out.txt --stderr-file err.txt
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```
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## Built-in environment
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The runner uses the dedicated interpreter at:
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- `/home/selig/.openclaw/workspace/.venv-code-interpreter/bin/python` (use the venv path directly; do not resolve the symlink to system Python)
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This keeps plotting/data-analysis dependencies stable without touching the system Python.
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The runner exposes these variables to the script:
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- `OPENCLAW_WORKSPACE`
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- `CODE_INTERPRETER_RUN_DIR`
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- `CODE_INTERPRETER_ARTIFACT_DIR`
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It also writes a helper file in the run directory:
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```python
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from ci_helpers import save_text, save_json
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```
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Use those helpers to save artifacts into `CODE_INTERPRETER_ARTIFACT_DIR`.
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## V4 automatic data analysis
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For automatic profiling/report generation from a local data file, use:
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- `scripts/analyze_data.py`
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- Reference: `references/v4-usage.md`
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This flow is ideal when the user wants a fast "analyze this CSV/JSON/Excel and give me a report + plots" result.
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## Output
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The runner prints compact JSON:
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```json
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{
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"ok": true,
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"exitCode": 0,
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"timeout": false,
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"runDir": "...",
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"artifactDir": "...",
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"packageStatus": {"pandas": true, "numpy": true, "matplotlib": false},
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"artifacts": [{"path": "...", "bytes": 123}],
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"stdout": "...",
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"stderr": "..."
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}
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```
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## Workflow
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1. Decide whether the task is a good fit for local trusted execution.
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2. Write the smallest script that solves the problem.
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3. Use `--artifact-dir` when the user may want generated files preserved.
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4. Run with a short timeout.
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5. Inspect `stdout`, `stderr`, and `artifacts`.
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6. If producing files, mention their exact paths in the reply.
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## Patterns
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### Exact calculation
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Use a one-liner with `--code`.
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### File analysis
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Read input files from workspace, then write summaries/derived files back to `artifactDir`.
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### Automatic report bundle
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When the user wants a quick profiling pass, run `scripts/analyze_data.py` against the file and return the generated `summary.json`, `report.md`, `preview.csv`, and any PNG plots.
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### Table inspection
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Prefer pandas when available; otherwise fall back to csv/json stdlib.
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### Plotting
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If `matplotlib` is available, write PNG files to `artifactDir`. Use a forced CJK font strategy for Chinese charts. The bundled default is Google Noto Sans CJK TC under `assets/fonts/` when present, then system fallbacks. Apply the chosen font not only via rcParams but also directly to titles, axis labels, tick labels, and legend text through FontProperties. This avoids tofu/garbled Chinese and suppresses missing-glyph warnings reliably. If plotting is unavailable, continue with tabular/text output.
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### Reusable logic
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Write a small `.py` file in the current task area, run with `--file`, then keep it if it may be reused.
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## Notes
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- The runner launches `python3 -B` with a minimal environment.
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- It creates an isolated temp run directory under `workspace/.tmp/code-interpreter-runs/`.
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- `stdout` / `stderr` are truncated in the JSON preview if very large; save to files when needed.
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- `MPLBACKEND=Agg` is set so headless plotting works when matplotlib is installed.
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- If a task needs stronger isolation than this local runner provides, do not force it—use a real sandbox/container approach instead.
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BIN
skills/code-interpreter/assets/fonts/NotoSansCJKtc-Regular.otf
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skills/code-interpreter/assets/fonts/NotoSansCJKtc-Regular.otf
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skills/code-interpreter/references/v4-usage.md
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skills/code-interpreter/references/v4-usage.md
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# V4 Usage
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## Purpose
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Generate an automatic data analysis bundle from a local data file.
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## Command
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```bash
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/home/selig/.openclaw/workspace/.venv-code-interpreter/bin/python \
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/home/selig/.openclaw/workspace/skills/code-interpreter/scripts/analyze_data.py \
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/path/to/input.csv \
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--artifact-dir /home/selig/.openclaw/workspace/.tmp/my-analysis
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```
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## Outputs
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- `summary.json` — machine-readable profile
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- `report.md` — human-readable summary
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- `preview.csv` — first 50 rows after parsing
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- `*.png` — generated plots when matplotlib is available
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## Supported inputs
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- `.csv`
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- `.tsv`
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- `.json`
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- `.xlsx`
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- `.xls`
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skills/code-interpreter/scripts/analyze_data.py
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skills/code-interpreter/scripts/analyze_data.py
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#!/usr/bin/env python3
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import argparse
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import json
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import math
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import os
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from pathlib import Path
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try:
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import pandas as pd
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except ImportError:
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raise SystemExit(
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'pandas is required. Run with the code-interpreter venv:\n'
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' ~/.openclaw/workspace/.venv-code-interpreter/bin/python analyze_data.py ...'
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)
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try:
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import matplotlib
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import matplotlib.pyplot as plt
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HAS_MPL = True
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except Exception:
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HAS_MPL = False
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ZH_FONT_CANDIDATES = [
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'/home/selig/.openclaw/workspace/skills/code-interpreter/assets/fonts/NotoSansCJKtc-Regular.otf',
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'/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf',
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]
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def configure_matplotlib_fonts() -> tuple[str | None, object | None]:
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if not HAS_MPL:
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return None, None
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chosen = None
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chosen_prop = None
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for path in ZH_FONT_CANDIDATES:
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if Path(path).exists():
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try:
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from matplotlib import font_manager
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font_manager.fontManager.addfont(path)
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font_prop = font_manager.FontProperties(fname=path)
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font_name = font_prop.get_name()
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matplotlib.rcParams['font.family'] = [font_name]
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matplotlib.rcParams['axes.unicode_minus'] = False
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chosen = font_name
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chosen_prop = font_prop
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break
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except Exception:
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continue
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return chosen, chosen_prop
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def apply_font(ax, font_prop) -> None:
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if not font_prop:
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return
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title = ax.title
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if title:
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title.set_fontproperties(font_prop)
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ax.xaxis.label.set_fontproperties(font_prop)
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ax.yaxis.label.set_fontproperties(font_prop)
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for label in ax.get_xticklabels():
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label.set_fontproperties(font_prop)
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for label in ax.get_yticklabels():
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label.set_fontproperties(font_prop)
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legend = ax.get_legend()
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if legend:
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for text in legend.get_texts():
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text.set_fontproperties(font_prop)
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legend.get_title().set_fontproperties(font_prop)
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def detect_format(path: Path) -> str:
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ext = path.suffix.lower()
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if ext in {'.csv', '.tsv', '.txt'}:
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return 'delimited'
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if ext == '.json':
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return 'json'
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if ext in {'.xlsx', '.xls'}:
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return 'excel'
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raise SystemExit(f'Unsupported file type: {ext}')
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def load_df(path: Path) -> pd.DataFrame:
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fmt = detect_format(path)
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if fmt == 'delimited':
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sep = '\t' if path.suffix.lower() == '.tsv' else ','
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return pd.read_csv(path, sep=sep)
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if fmt == 'json':
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try:
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return pd.read_json(path)
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except ValueError:
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return pd.DataFrame(json.loads(path.read_text(encoding='utf-8')))
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if fmt == 'excel':
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return pd.read_excel(path)
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raise SystemExit('Unsupported format')
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def safe_name(s: str) -> str:
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keep = []
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for ch in s:
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if ch.isalnum() or ch in ('-', '_'):
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keep.append(ch)
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elif ch in (' ', '/'):
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keep.append('_')
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out = ''.join(keep).strip('_')
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return out[:80] or 'column'
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def series_stats(s: pd.Series) -> dict:
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non_null = s.dropna()
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result = {
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'dtype': str(s.dtype),
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'nonNull': int(non_null.shape[0]),
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'nulls': int(s.isna().sum()),
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'unique': int(non_null.nunique()) if len(non_null) else 0,
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}
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if pd.api.types.is_numeric_dtype(s):
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result.update({
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'min': None if non_null.empty else float(non_null.min()),
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'max': None if non_null.empty else float(non_null.max()),
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'mean': None if non_null.empty else float(non_null.mean()),
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'sum': None if non_null.empty else float(non_null.sum()),
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})
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else:
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top = non_null.astype(str).value_counts().head(5)
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result['topValues'] = [{
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'value': str(idx),
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'count': int(val),
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} for idx, val in top.items()]
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return result
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def maybe_parse_dates(df: pd.DataFrame) -> tuple[pd.DataFrame, list[str]]:
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parsed = []
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out = df.copy()
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for col in out.columns:
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if out[col].dtype == 'object':
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sample = out[col].dropna().astype(str).head(20)
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if sample.empty:
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continue
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parsed_col = pd.to_datetime(out[col], errors='coerce')
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success_ratio = float(parsed_col.notna().mean()) if len(out[col]) else 0.0
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if success_ratio >= 0.6:
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out[col] = parsed_col
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parsed.append(str(col))
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return out, parsed
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def write_report(df: pd.DataFrame, summary: dict, out_dir: Path) -> Path:
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lines = []
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lines.append('# Data Analysis Report')
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lines.append('')
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lines.append(f"- Source: `{summary['source']}`")
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lines.append(f"- Rows: **{summary['rows']}**")
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lines.append(f"- Columns: **{summary['columns']}**")
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lines.append(f"- Generated plots: **{len(summary['plots'])}**")
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if summary['parsedDateColumns']:
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lines.append(f"- Parsed date columns: {', '.join(summary['parsedDateColumns'])}")
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lines.append('')
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lines.append('## Columns')
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lines.append('')
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for name, meta in summary['columnProfiles'].items():
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lines.append(f"### {name}")
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lines.append(f"- dtype: `{meta['dtype']}`")
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lines.append(f"- non-null: {meta['nonNull']}")
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lines.append(f"- nulls: {meta['nulls']}")
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lines.append(f"- unique: {meta['unique']}")
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if 'mean' in meta:
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lines.append(f"- min / max: {meta['min']} / {meta['max']}")
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lines.append(f"- mean / sum: {meta['mean']} / {meta['sum']}")
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elif meta.get('topValues'):
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preview = ', '.join([f"{x['value']} ({x['count']})" for x in meta['topValues'][:5]])
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lines.append(f"- top values: {preview}")
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lines.append('')
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report = out_dir / 'report.md'
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report.write_text('\n'.join(lines).strip() + '\n', encoding='utf-8')
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return report
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def generate_plots(df: pd.DataFrame, out_dir: Path, font_prop=None) -> list[str]:
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if not HAS_MPL:
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return []
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plots = []
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numeric_cols = [c for c in df.columns if pd.api.types.is_numeric_dtype(df[c])]
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date_cols = [c for c in df.columns if pd.api.types.is_datetime64_any_dtype(df[c])]
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cat_cols = [c for c in df.columns if not pd.api.types.is_numeric_dtype(df[c]) and not pd.api.types.is_datetime64_any_dtype(df[c])]
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if numeric_cols:
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col = numeric_cols[0]
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plt.figure(figsize=(7, 4))
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bins = min(20, max(5, int(math.sqrt(max(1, df[col].dropna().shape[0])))))
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df[col].dropna().hist(bins=bins)
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plt.title(f'Histogram of {col}', fontproperties=font_prop)
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plt.xlabel(str(col), fontproperties=font_prop)
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plt.ylabel('Count', fontproperties=font_prop)
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apply_font(plt.gca(), font_prop)
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path = out_dir / f'hist_{safe_name(str(col))}.png'
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plt.tight_layout()
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plt.savefig(path, dpi=160)
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plt.close()
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plots.append(str(path))
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if cat_cols and numeric_cols:
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cat, num = cat_cols[0], numeric_cols[0]
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grp = df.groupby(cat, dropna=False)[num].sum().sort_values(ascending=False).head(12)
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if not grp.empty:
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plt.figure(figsize=(8, 4.5))
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grp.plot(kind='bar')
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plt.title(f'{num} by {cat}', fontproperties=font_prop)
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plt.xlabel(str(cat), fontproperties=font_prop)
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plt.ylabel(f'Sum of {num}', fontproperties=font_prop)
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apply_font(plt.gca(), font_prop)
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plt.tight_layout()
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path = out_dir / f'bar_{safe_name(str(num))}_by_{safe_name(str(cat))}.png'
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plt.savefig(path, dpi=160)
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plt.close()
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plots.append(str(path))
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if date_cols and numeric_cols:
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date_col, num = date_cols[0], numeric_cols[0]
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grp = df[[date_col, num]].dropna().sort_values(date_col)
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if not grp.empty:
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plt.figure(figsize=(8, 4.5))
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plt.plot(grp[date_col], grp[num], marker='o')
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plt.title(f'{num} over time', fontproperties=font_prop)
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plt.xlabel(str(date_col), fontproperties=font_prop)
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plt.ylabel(str(num), fontproperties=font_prop)
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apply_font(plt.gca(), font_prop)
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plt.tight_layout()
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path = out_dir / f'line_{safe_name(str(num))}_over_time.png'
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plt.savefig(path, dpi=160)
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plt.close()
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plots.append(str(path))
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return plots
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def main() -> int:
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parser = argparse.ArgumentParser(description='Automatic data analysis report generator')
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parser.add_argument('input', help='Input data file (csv/json/xlsx)')
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parser.add_argument('--artifact-dir', required=True, help='Output artifact directory')
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args = parser.parse_args()
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input_path = Path(args.input).expanduser().resolve()
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artifact_dir = Path(args.artifact_dir).expanduser().resolve()
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artifact_dir.mkdir(parents=True, exist_ok=True)
|
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df = load_df(input_path)
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original_columns = [str(c) for c in df.columns]
|
||||
df, parsed_dates = maybe_parse_dates(df)
|
||||
chosen_font, chosen_font_prop = configure_matplotlib_fonts()
|
||||
|
||||
preview_path = artifact_dir / 'preview.csv'
|
||||
df.head(50).to_csv(preview_path, index=False)
|
||||
|
||||
summary = {
|
||||
'source': str(input_path),
|
||||
'rows': int(df.shape[0]),
|
||||
'columns': int(df.shape[1]),
|
||||
'columnNames': original_columns,
|
||||
'parsedDateColumns': parsed_dates,
|
||||
'columnProfiles': {str(c): series_stats(df[c]) for c in df.columns},
|
||||
'plots': [],
|
||||
'plotFont': chosen_font,
|
||||
}
|
||||
|
||||
summary['plots'] = generate_plots(df, artifact_dir, chosen_font_prop)
|
||||
|
||||
summary_path = artifact_dir / 'summary.json'
|
||||
summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding='utf-8')
|
||||
report_path = write_report(df, summary, artifact_dir)
|
||||
|
||||
result = {
|
||||
'ok': True,
|
||||
'input': str(input_path),
|
||||
'artifactDir': str(artifact_dir),
|
||||
'summary': str(summary_path),
|
||||
'report': str(report_path),
|
||||
'preview': str(preview_path),
|
||||
'plots': summary['plots'],
|
||||
}
|
||||
print(json.dumps(result, ensure_ascii=False, indent=2))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise SystemExit(main())
|
||||
241
skills/code-interpreter/scripts/run_code.py
Normal file
241
skills/code-interpreter/scripts/run_code.py
Normal file
@@ -0,0 +1,241 @@
|
||||
#!/usr/bin/env python3
|
||||
import argparse
|
||||
import importlib.util
|
||||
import json
|
||||
import os
|
||||
import pathlib
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
WORKSPACE = pathlib.Path('/home/selig/.openclaw/workspace').resolve()
|
||||
RUNS_DIR = WORKSPACE / '.tmp' / 'code-interpreter-runs'
|
||||
MAX_PREVIEW = 12000
|
||||
ARTIFACT_SCAN_LIMIT = 100
|
||||
PACKAGE_PROBES = ['pandas', 'numpy', 'matplotlib']
|
||||
PYTHON_BIN = str(WORKSPACE / '.venv-code-interpreter' / 'bin' / 'python')
|
||||
|
||||
|
||||
def current_python_paths(run_dir_path: pathlib.Path) -> str:
|
||||
"""Build PYTHONPATH: run_dir (for ci_helpers) only.
|
||||
Venv site-packages are already on sys.path when using PYTHON_BIN."""
|
||||
return str(run_dir_path)
|
||||
|
||||
|
||||
def read_code(args: argparse.Namespace) -> str:
|
||||
sources = [bool(args.code), bool(args.file), bool(args.stdin)]
|
||||
if sum(sources) != 1:
|
||||
raise SystemExit('Provide exactly one of --code, --file, or --stdin')
|
||||
if args.code:
|
||||
return args.code
|
||||
if args.file:
|
||||
return pathlib.Path(args.file).read_text(encoding='utf-8')
|
||||
return sys.stdin.read()
|
||||
|
||||
|
||||
def ensure_within_workspace(path_str: Optional[str], must_exist: bool = True) -> pathlib.Path:
|
||||
if not path_str:
|
||||
return WORKSPACE
|
||||
p = pathlib.Path(path_str).expanduser().resolve()
|
||||
if p != WORKSPACE and WORKSPACE not in p.parents:
|
||||
raise SystemExit(f'Path must stay inside workspace: {WORKSPACE}')
|
||||
if must_exist and (not p.exists() or not p.is_dir()):
|
||||
raise SystemExit(f'Path not found or not a directory: {p}')
|
||||
return p
|
||||
|
||||
|
||||
def ensure_output_path(path_str: Optional[str]) -> Optional[pathlib.Path]:
|
||||
if not path_str:
|
||||
return None
|
||||
p = pathlib.Path(path_str).expanduser().resolve()
|
||||
p.parent.mkdir(parents=True, exist_ok=True)
|
||||
return p
|
||||
|
||||
|
||||
def write_text(path_str: Optional[str], text: str) -> None:
|
||||
p = ensure_output_path(path_str)
|
||||
if not p:
|
||||
return
|
||||
p.write_text(text, encoding='utf-8')
|
||||
|
||||
|
||||
def truncate(text: str) -> str:
|
||||
if len(text) <= MAX_PREVIEW:
|
||||
return text
|
||||
extra = len(text) - MAX_PREVIEW
|
||||
return text[:MAX_PREVIEW] + f'\n...[truncated {extra} chars]'
|
||||
|
||||
|
||||
def package_status() -> dict:
|
||||
out: dict[str, bool] = {}
|
||||
for name in PACKAGE_PROBES:
|
||||
proc = subprocess.run(
|
||||
[PYTHON_BIN, '-c', f"import importlib.util; print('1' if importlib.util.find_spec('{name}') else '0')"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
encoding='utf-8',
|
||||
errors='replace',
|
||||
)
|
||||
out[name] = proc.stdout.strip() == '1'
|
||||
return out
|
||||
|
||||
|
||||
def rel_to(path: pathlib.Path, base: pathlib.Path) -> str:
|
||||
try:
|
||||
return str(path.relative_to(base))
|
||||
except Exception:
|
||||
return str(path)
|
||||
|
||||
|
||||
def scan_artifacts(base_dir: pathlib.Path, root_label: str) -> list[dict]:
|
||||
if not base_dir.exists():
|
||||
return []
|
||||
items: list[dict] = []
|
||||
for p in sorted(base_dir.rglob('*')):
|
||||
if len(items) >= ARTIFACT_SCAN_LIMIT:
|
||||
break
|
||||
if p.is_file():
|
||||
try:
|
||||
size = p.stat().st_size
|
||||
except Exception:
|
||||
size = None
|
||||
items.append({
|
||||
'root': root_label,
|
||||
'path': str(p),
|
||||
'relative': rel_to(p, base_dir),
|
||||
'bytes': size,
|
||||
})
|
||||
return items
|
||||
|
||||
|
||||
def write_helper(run_dir_path: pathlib.Path, artifact_dir: pathlib.Path) -> None:
|
||||
helper = run_dir_path / 'ci_helpers.py'
|
||||
helper.write_text(
|
||||
"""
|
||||
from pathlib import Path
|
||||
import json
|
||||
import os
|
||||
|
||||
WORKSPACE = Path(os.environ['OPENCLAW_WORKSPACE'])
|
||||
RUN_DIR = Path(os.environ['CODE_INTERPRETER_RUN_DIR'])
|
||||
ARTIFACT_DIR = Path(os.environ['CODE_INTERPRETER_ARTIFACT_DIR'])
|
||||
|
||||
|
||||
def save_text(name: str, text: str) -> str:
|
||||
path = ARTIFACT_DIR / name
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(text, encoding='utf-8')
|
||||
return str(path)
|
||||
|
||||
|
||||
def save_json(name: str, data) -> str:
|
||||
path = ARTIFACT_DIR / name
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding='utf-8')
|
||||
return str(path)
|
||||
""".lstrip(),
|
||||
encoding='utf-8',
|
||||
)
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description='Local Python runner for OpenClaw code-interpreter skill')
|
||||
parser.add_argument('--code', help='Python code to execute')
|
||||
parser.add_argument('--file', help='Path to a Python file to execute')
|
||||
parser.add_argument('--stdin', action='store_true', help='Read Python code from stdin')
|
||||
parser.add_argument('--cwd', help='Working directory inside workspace')
|
||||
parser.add_argument('--artifact-dir', help='Artifact directory inside workspace to keep outputs')
|
||||
parser.add_argument('--timeout', type=int, default=20, help='Timeout seconds (default: 20)')
|
||||
parser.add_argument('--stdout-file', help='Optional file path to save full stdout')
|
||||
parser.add_argument('--stderr-file', help='Optional file path to save full stderr')
|
||||
parser.add_argument('--keep-run-dir', action='store_true', help='Keep generated temp run directory even on success')
|
||||
args = parser.parse_args()
|
||||
|
||||
code = read_code(args)
|
||||
cwd = ensure_within_workspace(args.cwd)
|
||||
RUNS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
run_dir_path = pathlib.Path(tempfile.mkdtemp(prefix='run-', dir=str(RUNS_DIR))).resolve()
|
||||
artifact_dir = ensure_within_workspace(args.artifact_dir, must_exist=False) if args.artifact_dir else (run_dir_path / 'artifacts')
|
||||
artifact_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
script_path = run_dir_path / 'main.py'
|
||||
script_path.write_text(code, encoding='utf-8')
|
||||
write_helper(run_dir_path, artifact_dir)
|
||||
|
||||
env = {
|
||||
'PATH': os.environ.get('PATH', '/usr/bin:/bin'),
|
||||
'HOME': str(run_dir_path),
|
||||
'PYTHONPATH': current_python_paths(run_dir_path),
|
||||
'PYTHONIOENCODING': 'utf-8',
|
||||
'PYTHONUNBUFFERED': '1',
|
||||
'OPENCLAW_WORKSPACE': str(WORKSPACE),
|
||||
'CODE_INTERPRETER_RUN_DIR': str(run_dir_path),
|
||||
'CODE_INTERPRETER_ARTIFACT_DIR': str(artifact_dir),
|
||||
'MPLBACKEND': 'Agg',
|
||||
}
|
||||
|
||||
started = time.time()
|
||||
timed_out = False
|
||||
exit_code = None
|
||||
stdout = ''
|
||||
stderr = ''
|
||||
|
||||
try:
|
||||
proc = subprocess.run(
|
||||
[PYTHON_BIN, '-B', str(script_path)],
|
||||
cwd=str(cwd),
|
||||
env=env,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
encoding='utf-8',
|
||||
errors='replace',
|
||||
timeout=max(1, args.timeout),
|
||||
)
|
||||
exit_code = proc.returncode
|
||||
stdout = proc.stdout
|
||||
stderr = proc.stderr
|
||||
except subprocess.TimeoutExpired as exc:
|
||||
timed_out = True
|
||||
exit_code = 124
|
||||
raw_out = exc.stdout or ''
|
||||
raw_err = exc.stderr or ''
|
||||
stdout = raw_out if isinstance(raw_out, str) else raw_out.decode('utf-8', errors='replace')
|
||||
stderr = (raw_err if isinstance(raw_err, str) else raw_err.decode('utf-8', errors='replace')) + f'\nExecution timed out after {args.timeout}s.'
|
||||
|
||||
duration = round(time.time() - started, 3)
|
||||
|
||||
write_text(args.stdout_file, stdout)
|
||||
write_text(args.stderr_file, stderr)
|
||||
|
||||
artifacts = scan_artifacts(artifact_dir, 'artifactDir')
|
||||
if artifact_dir != run_dir_path:
|
||||
artifacts.extend(scan_artifacts(run_dir_path / 'artifacts', 'runArtifacts'))
|
||||
|
||||
result = {
|
||||
'ok': (exit_code == 0 and not timed_out),
|
||||
'exitCode': exit_code,
|
||||
'timeout': timed_out,
|
||||
'durationSec': duration,
|
||||
'cwd': str(cwd),
|
||||
'runDir': str(run_dir_path),
|
||||
'artifactDir': str(artifact_dir),
|
||||
'packageStatus': package_status(),
|
||||
'artifacts': artifacts,
|
||||
'stdout': truncate(stdout),
|
||||
'stderr': truncate(stderr),
|
||||
}
|
||||
|
||||
print(json.dumps(result, ensure_ascii=False, indent=2))
|
||||
|
||||
if not args.keep_run_dir and result['ok'] and artifact_dir != run_dir_path:
|
||||
shutil.rmtree(run_dir_path, ignore_errors=True)
|
||||
|
||||
return 0 if result['ok'] else 1
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise SystemExit(main())
|
||||
1
skills/kokoro-tts
Symbolic link
1
skills/kokoro-tts
Symbolic link
@@ -0,0 +1 @@
|
||||
/home/selig/.openclaw/workspace/skills/kokoro-tts
|
||||
1
skills/remotion-best-practices
Symbolic link
1
skills/remotion-best-practices
Symbolic link
@@ -0,0 +1 @@
|
||||
/home/selig/.agents/skills/remotion-best-practices
|
||||
125
skills/research-to-paper-slides/SKILL.md
Normal file
125
skills/research-to-paper-slides/SKILL.md
Normal file
@@ -0,0 +1,125 @@
|
||||
---
|
||||
name: research-to-paper-slides
|
||||
description: Turn local analysis outputs into publication-style drafts and presentation materials. Use when the user already has research/data-analysis artifacts such as summary.json, report.md, preview.csv, plots, or code-interpreter output and wants a complete first-pass paper draft, slide outline, speaker notes, or HTML deck. Especially useful after using the code-interpreter skill on small-to-medium datasets and the next step is to package findings into a paper, report, pitch deck, class slides, or meeting presentation.
|
||||
---
|
||||
|
||||
# research-to-paper-slides
|
||||
|
||||
Generate a complete first-pass writing bundle from analysis artifacts.
|
||||
|
||||
## Inputs
|
||||
|
||||
Best input bundle:
|
||||
- `summary.json`
|
||||
- `report.md`
|
||||
- one or more plot PNG files
|
||||
|
||||
Optional:
|
||||
- `preview.csv`
|
||||
- raw CSV/JSON/XLSX path for source naming only
|
||||
- extra notes from the user (audience, tone, purpose)
|
||||
|
||||
## Levels
|
||||
|
||||
Choose how far the workflow should go:
|
||||
|
||||
- `--level v2` — **基礎交付版**
|
||||
- 輸出：`paper.md`、`slides.md`、`speaker-notes.md`、`deck.html`
|
||||
- 適合：快速草稿、先出第一版內容
|
||||
- 不包含：`insights.md`、逐圖解讀頁、正式 deck 視覺強化
|
||||
|
||||
- `--level v3` — **洞察強化版**
|
||||
- 包含 `v2` 全部內容
|
||||
- 另外增加：`insights.md`、每張圖各一頁解讀、speaker notes 逐圖講稿
|
||||
- 適合：內部討論、研究整理、需要把圖表講清楚
|
||||
|
||||
- `--level v4` — **正式交付版**
|
||||
- 包含 `v3` 全部內容
|
||||
- 另外增加：更正式的 deck 視覺版面、PDF-ready 工作流
|
||||
- 適合：正式簡報、提案、對外展示
|
||||
|
||||
## Modes
|
||||
|
||||
- `academic` — 論文/研究報告/研討會簡報
|
||||
- `business` — 內部決策/管理匯報/策略說明
|
||||
- `pitch` — 提案/募資/對外說服型簡報
|
||||
|
||||
## Outputs
|
||||
|
||||
Depending on `--level`, the generator creates:
|
||||
- `paper.md` — structured paper/report draft
|
||||
- `slides.md` — slide-by-slide content outline
|
||||
- `speaker-notes.md` — presenter script notes
|
||||
- `insights.md` — key insights + plot interpretations (`v3` / `v4`)
|
||||
- `deck.html` — printable deck HTML
|
||||
- `bundle.json` — machine-readable manifest with `level` and `levelNote`
|
||||
|
||||
Optional local export:
|
||||
- `export_pdf.py` — export `deck.html` to PDF via local headless Chromium
|
||||
|
||||
## Workflow
|
||||
|
||||
1. Point the generator at an analysis artifact directory.
|
||||
2. Pass `--mode` for audience style.
|
||||
3. Pass `--level` for workflow depth.
|
||||
4. Review the generated markdown/html.
|
||||
5. If needed, refine wording or structure.
|
||||
6. If using `v4`, export `deck.html` to PDF.
|
||||
|
||||
## Commands
|
||||
|
||||
### V2 — 基礎交付版
|
||||
|
||||
```bash
|
||||
python3 {baseDir}/scripts/generate_bundle.py \
|
||||
--analysis-dir /path/to/analysis/out \
|
||||
--output-dir /path/to/paper-slides-out \
|
||||
--title "研究標題" \
|
||||
--audience "投資人" \
|
||||
--purpose "簡報" \
|
||||
--mode business \
|
||||
--level v2
|
||||
```
|
||||
|
||||
### V3 — 洞察強化版
|
||||
|
||||
```bash
|
||||
python3 {baseDir}/scripts/generate_bundle.py \
|
||||
--analysis-dir /path/to/analysis/out \
|
||||
--output-dir /path/to/paper-slides-out \
|
||||
--title "研究標題" \
|
||||
--audience "研究者" \
|
||||
--purpose "研究整理" \
|
||||
--mode academic \
|
||||
--level v3
|
||||
```
|
||||
|
||||
### V4 — 正式交付版
|
||||
|
||||
```bash
|
||||
python3 {baseDir}/scripts/generate_bundle.py \
|
||||
--analysis-dir /path/to/analysis/out \
|
||||
--output-dir /path/to/paper-slides-out \
|
||||
--title "研究標題" \
|
||||
--audience "投資人" \
|
||||
--purpose "募資簡報" \
|
||||
--mode pitch \
|
||||
--level v4
|
||||
```
|
||||
|
||||
## PDF export
|
||||
|
||||
If local Chromium is available, try:
|
||||
|
||||
```bash
|
||||
python3 {baseDir}/scripts/export_pdf.py \
|
||||
--html /path/to/deck.html \
|
||||
--pdf /path/to/deck.pdf
|
||||
```
|
||||
|
||||
## Notes
|
||||
|
||||
- Prefer this skill after `code-interpreter` or any workflow that already produced plots and structured summaries.
|
||||
- Keep this as a first-pass drafting tool; the output is meant to be edited, not treated as final publication-ready text.
|
||||
- On this workstation, Chromium CLI `--print-to-pdf` may still fail with host-specific permission/runtime quirks even when directories are writable.
|
||||
- When the user wants a PDF, try `export_pdf.py` first; if it fails, immediately fall back to OpenClaw browser PDF export on a locally served `deck.html`.
|
||||
14
skills/research-to-paper-slides/references/pdf-notes.md
Normal file
14
skills/research-to-paper-slides/references/pdf-notes.md
Normal file
@@ -0,0 +1,14 @@
|
||||
# PDF Notes
|
||||
|
||||
## Current recommended path
|
||||
|
||||
1. Generate `deck.html` with this skill.
|
||||
2. Open `deck.html` in the browser.
|
||||
3. Export to PDF with browser print/PDF flow.
|
||||
4. If small textual tweaks are needed after PDF export, use the installed `nano-pdf` skill.
|
||||
|
||||
## Why this path
|
||||
|
||||
- HTML is easier to iterate than direct PDF generation.
|
||||
- Existing plot PNG files can be embedded cleanly.
|
||||
- Browser PDF export preserves layout reliably for first-pass decks.
|
||||
Binary file not shown.
Binary file not shown.
53
skills/research-to-paper-slides/scripts/export_pdf.py
Normal file
53
skills/research-to-paper-slides/scripts/export_pdf.py
Normal file
@@ -0,0 +1,53 @@
|
||||
#!/usr/bin/env python3
|
||||
import argparse
|
||||
import glob
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def find_browser() -> str:
|
||||
# Playwright Chromium (most reliable on this workstation)
|
||||
for pw in sorted(glob.glob(os.path.expanduser('~/.cache/ms-playwright/chromium-*/chrome-linux/chrome')), reverse=True):
|
||||
if os.access(pw, os.X_OK):
|
||||
return pw
|
||||
for name in ['chromium-browser', 'chromium', 'google-chrome', 'google-chrome-stable']:
|
||||
path = shutil.which(name)
|
||||
if path:
|
||||
return path
|
||||
raise SystemExit('No supported browser found for PDF export. Install Playwright Chromium: npx playwright install chromium')
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description='Export deck HTML to PDF using headless Chromium')
|
||||
parser.add_argument('--html', required=True)
|
||||
parser.add_argument('--pdf', required=True)
|
||||
args = parser.parse_args()
|
||||
|
||||
html_path = Path(args.html).expanduser().resolve()
|
||||
pdf_path = Path(args.pdf).expanduser().resolve()
|
||||
pdf_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if not html_path.exists():
|
||||
raise SystemExit(f'Missing HTML input: {html_path}')
|
||||
|
||||
browser = find_browser()
|
||||
with tempfile.TemporaryDirectory(prefix='rtps-chromium-') as profile_dir:
|
||||
cmd = [
|
||||
browser,
|
||||
'--headless',
|
||||
'--disable-gpu',
|
||||
'--no-sandbox',
|
||||
f'--user-data-dir={profile_dir}',
|
||||
f'--print-to-pdf={pdf_path}',
|
||||
html_path.as_uri(),
|
||||
]
|
||||
subprocess.run(cmd, check=True)
|
||||
print(str(pdf_path))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise SystemExit(main())
|
||||
498
skills/research-to-paper-slides/scripts/generate_bundle.py
Normal file
498
skills/research-to-paper-slides/scripts/generate_bundle.py
Normal file
@@ -0,0 +1,498 @@
|
||||
#!/usr/bin/env python3
|
||||
import argparse
|
||||
import json
|
||||
import html
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
|
||||
MODES = {'academic', 'business', 'pitch'}
|
||||
LEVELS = {'v2', 'v3', 'v4'}
|
||||
LEVEL_NOTES = {
|
||||
'v2': '基礎交付版：paper/slides/speaker-notes/deck',
|
||||
'v3': '洞察強化版：v2 + insights + 每張圖逐頁解讀',
|
||||
'v4': '正式交付版：v3 + 更正式 deck 視覺 + PDF-ready 工作流',
|
||||
}
|
||||
|
||||
|
||||
def read_json(path: Path) -> dict[str, Any]:
|
||||
return json.loads(path.read_text(encoding='utf-8'))
|
||||
|
||||
|
||||
def read_text(path: Path) -> str:
|
||||
return path.read_text(encoding='utf-8')
|
||||
|
||||
|
||||
def find_plots(analysis_dir: Path) -> list[Path]:
|
||||
return sorted([p for p in analysis_dir.glob('*.png') if p.is_file()])
|
||||
|
||||
|
||||
def build_key_findings(summary: dict[str, Any]) -> list[str]:
|
||||
findings: list[str] = []
|
||||
for name, meta in summary.get('columnProfiles', {}).items():
|
||||
if 'mean' in meta and meta.get('mean') is not None:
|
||||
findings.append(f"欄位「{name}」平均值約為 {meta['mean']:.2f}，總和約為 {meta['sum']:.2f}。")
|
||||
elif meta.get('topValues'):
|
||||
top = meta['topValues'][0]
|
||||
findings.append(f"欄位「{name}」最常見值為「{top['value']}」，出現 {top['count']} 次。")
|
||||
if len(findings) >= 6:
|
||||
break
|
||||
if not findings:
|
||||
findings.append('資料已完成初步整理，但尚缺少足夠特徵以自動歸納具體發現。')
|
||||
return findings
|
||||
|
||||
|
||||
def build_method_text(summary: dict[str, Any]) -> str:
|
||||
rows = summary.get('rows', 0)
|
||||
cols = summary.get('columns', 0)
|
||||
parsed_dates = summary.get('parsedDateColumns', [])
|
||||
parts = [f"本研究以一份包含 {rows} 筆資料、{cols} 個欄位的資料集作為分析基礎。"]
|
||||
if parsed_dates:
|
||||
parts.append(f"其中已自動辨識日期欄位：{', '.join(parsed_dates)}。")
|
||||
parts.append("分析流程包含欄位剖析、數值摘要、類別分布觀察，以及圖表化初步探索。")
|
||||
return ''.join(parts)
|
||||
|
||||
|
||||
def build_limitations(summary: dict[str, Any], mode: str) -> list[str]:
|
||||
base = [
|
||||
'本版本內容依據自動分析結果生成，仍需依情境補充背景、語境與論證細節。',
|
||||
'目前主要反映描述性分析與初步視覺化結果，尚未自動進行嚴格因果推論或完整驗證。',
|
||||
]
|
||||
if mode == 'pitch':
|
||||
base[0] = '本版本適合作為提案底稿，但對外簡報前仍需補上商業敘事、案例與風險說明。'
|
||||
elif mode == 'business':
|
||||
base[0] = '本版本可支援內部決策討論，但正式匯報前仍建議補充商務脈絡與對照基準。'
|
||||
elif mode == 'academic':
|
||||
base[0] = '本版本可作為論文或研究報告草稿，但正式提交前仍需補足文獻回顧、研究問題與方法論細節。'
|
||||
if not summary.get('plots'):
|
||||
base.append('本次分析未包含圖表產物，因此視覺化證據仍需後續補充。')
|
||||
return base
|
||||
|
||||
|
||||
def classify_plot(name: str) -> str:
|
||||
low = name.lower()
|
||||
if low.startswith('hist_'):
|
||||
return 'histogram'
|
||||
if low.startswith('bar_'):
|
||||
return 'bar'
|
||||
if low.startswith('line_'):
|
||||
return 'line'
|
||||
return 'plot'
|
||||
|
||||
|
||||
def interpret_plot(plot: Path, mode: str) -> dict[str, str]:
|
||||
kind = classify_plot(plot.name)
|
||||
base = {
|
||||
'histogram': {
|
||||
'title': f'圖表解讀：{plot.name}',
|
||||
'summary': '這張 histogram 用來觀察數值欄位的分布狀態、集中區域與可能的離群位置。',
|
||||
'so_what': '若資料分布偏斜或過度集中，後續可考慮分群、分層或補充異常值檢查。',
|
||||
},
|
||||
'bar': {
|
||||
'title': f'圖表解讀：{plot.name}',
|
||||
'summary': '這張 bar chart 適合比較不同類別或分組之間的量體差異，幫助快速辨識高低落差。',
|
||||
'so_what': '若類別差異明顯，後續可針對高表現或低表現組別追查原因與策略。',
|
||||
},
|
||||
'line': {
|
||||
'title': f'圖表解讀：{plot.name}',
|
||||
'summary': '這張 line chart 用於觀察時間序列變化，幫助辨識趨勢、波動與可能轉折點。',
|
||||
'so_what': '若趨勢持續上升或下降，建議進一步比對外部事件、季節性與干預因素。',
|
||||
},
|
||||
'plot': {
|
||||
'title': f'圖表解讀：{plot.name}',
|
||||
'summary': '這張圖表提供一個視覺化切面，有助於快速掌握資料重點與分布特徵。',
|
||||
'so_what': '建議將圖表與主要論點對齊，補上更具體的背景解讀。',
|
||||
},
|
||||
}[kind]
|
||||
|
||||
if mode == 'pitch':
|
||||
base['so_what'] = '簡報時應直接說明這張圖支持了哪個主張，以及它如何增加說服力。'
|
||||
elif mode == 'business':
|
||||
base['so_what'] = '建議把這張圖對應到 KPI、風險或下一步行動，方便管理層做判斷。'
|
||||
elif mode == 'academic':
|
||||
base['so_what'] = '建議將這張圖與研究問題、假設或比較基準一起討論，以提升論證完整度。'
|
||||
return base
|
||||
|
||||
|
||||
def build_insights(summary: dict[str, Any], plots: list[Path], mode: str) -> list[str]:
|
||||
insights: list[str] = []
|
||||
numeric = []
|
||||
categorical = []
|
||||
for name, meta in summary.get('columnProfiles', {}).items():
|
||||
if 'mean' in meta and meta.get('mean') is not None:
|
||||
numeric.append((name, meta))
|
||||
elif meta.get('topValues'):
|
||||
categorical.append((name, meta))
|
||||
|
||||
for name, meta in numeric[:3]:
|
||||
insights.append(f"數值欄位「{name}」平均約 {meta['mean']:.2f}，範圍約 {meta['min']:.2f} 到 {meta['max']:.2f}。")
|
||||
for name, meta in categorical[:2]:
|
||||
top = meta['topValues'][0]
|
||||
insights.append(f"類別欄位「{name}」目前以「{top['value']}」最常見（{top['count']} 次），值得作為第一輪聚焦對象。")
|
||||
if plots:
|
||||
insights.append(f"本次已生成 {len(plots)} 張圖表，可直接支撐逐頁圖表解讀與口頭報告。")
|
||||
|
||||
if mode == 'pitch':
|
||||
insights.append('對外提案時，建議把最強的一項數據證據前置，讓聽眾先記住價值主張。')
|
||||
elif mode == 'business':
|
||||
insights.append('內部決策簡報時，建議把洞察轉成 KPI、優先順序與負責人。')
|
||||
elif mode == 'academic':
|
||||
insights.append('學術/研究情境下，建議將洞察進一步轉成研究問題、比較架構與後續驗證方向。')
|
||||
return insights
|
||||
|
||||
|
||||
def make_insights_md(title: str, mode: str, summary: dict[str, Any], plots: list[Path]) -> str:
|
||||
insights = build_insights(summary, plots, mode)
|
||||
plot_notes = [interpret_plot(p, mode) for p in plots]
|
||||
lines = [f"# {title}｜Insights", '', f"- 模式：`{mode}`", '']
|
||||
lines.append('## 關鍵洞察')
|
||||
lines.extend([f"- {x}" for x in insights])
|
||||
lines.append('')
|
||||
if plot_notes:
|
||||
lines.append('## 圖表解讀摘要')
|
||||
for note in plot_notes:
|
||||
lines.append(f"### {note['title']}")
|
||||
lines.append(f"- 解讀：{note['summary']}")
|
||||
lines.append(f"- 延伸：{note['so_what']}")
|
||||
lines.append('')
|
||||
return '\n'.join(lines).strip() + '\n'
|
||||
|
||||
|
||||
def make_paper(title: str, audience: str, purpose: str, mode: str, level: str, summary: dict[str, Any], report_md: str, plots: list[Path], insights_md: str | None = None) -> str:
|
||||
findings = build_key_findings(summary)
|
||||
method_text = build_method_text(summary)
|
||||
limitations = build_limitations(summary, mode)
|
||||
plot_refs = '\n'.join([f"- `{p.name}`" for p in plots]) or '- 無'
|
||||
findings_md = '\n'.join([f"- {x}" for x in findings])
|
||||
limitations_md = '\n'.join([f"- {x}" for x in limitations])
|
||||
|
||||
if mode == 'academic':
|
||||
sections = f"## 摘要\n\n本文面向{audience}，以「{purpose}」為導向，整理目前資料分析結果並形成學術/研究草稿。\n\n## 研究背景與問題意識\n\n本文件根據既有分析產物自動整理，可作為研究報告、論文初稿或研究提案的起點。\n\n## 研究方法\n\n{method_text}\n\n## 研究發現\n\n{findings_md}\n\n## 討論\n\n目前結果可支撐初步描述性討論，後續可進一步補上研究假設、比較對照與方法嚴謹性。\n\n## 限制\n\n{limitations_md}\n\n## 結論\n\n本分析已形成研究性文件的結構基礎，適合進一步擴展為正式研究報告。"
|
||||
elif mode == 'business':
|
||||
sections = f"## 執行摘要\n\n本文面向{audience}，目的是支援「{purpose}」的商務溝通與內部決策。\n\n## 商務背景\n\n本文件根據既有分析產物自動整理，適合作為內部簡報、策略討論或管理層報告的第一版。\n\n## 分析方法\n\n{method_text}\n\n## 關鍵洞察\n\n{findings_md}\n\n## 商業意涵\n\n目前資料已足以支撐一輪決策討論，建議進一步對照 KPI、目標值與外部環境。\n\n## 風險與限制\n\n{limitations_md}\n\n## 建議下一步\n\n建議針對最具決策價值的指標建立定期追蹤與後續驗證流程。"
|
||||
else:
|
||||
sections = f"## Pitch Summary\n\n本文面向{audience}，用於支援「{purpose}」的提案、募資或說服型簡報。\n\n## Opportunity\n\n本文件根據既有分析產物自動整理，可作為提案 deck 與口頭簡報的第一版底稿。\n\n## Evidence\n\n{method_text}\n\n## Key Takeaways\n\n{findings_md}\n\n## Why It Matters\n\n目前結果已可形成明確敘事雛形，後續可補上市場機會、競品比較與具體行動方案。\n\n## Risks\n\n{limitations_md}\n\n## Ask / Next Step\n\n建議將數據證據、主張與下一步行動整合成對外一致的提案版本。"
|
||||
|
||||
insight_section = ''
|
||||
if insights_md:
|
||||
insight_section = f"\n## 洞察摘要\n\n{insights_md}\n"
|
||||
|
||||
return f"# {title}\n\n- 模式：`{mode}`\n- 等級：`{level}` — {LEVEL_NOTES[level]}\n- 對象：{audience}\n- 目的：{purpose}\n\n{sections}\n\n## 圖表與視覺化資產\n\n{plot_refs}{insight_section}\n## 附錄：原始自動分析摘要\n\n{report_md}\n"
|
||||
|
||||
|
||||
def make_slides(title: str, audience: str, purpose: str, mode: str, summary: dict[str, Any], plots: list[Path], level: str) -> str:
|
||||
findings = build_key_findings(summary)
|
||||
rows = summary.get('rows', 0)
|
||||
cols = summary.get('columns', 0)
|
||||
|
||||
if mode == 'academic':
|
||||
slides = [
|
||||
('封面', [f'標題：{title}', f'對象：{audience}', f'目的：{purpose}', f'等級：{LEVEL_NOTES[level]}']),
|
||||
('研究問題', ['定義研究背景與核心問題', '說明本次分析欲回答的主題']),
|
||||
('資料概況', [f'資料筆數：{rows}', f'欄位數：{cols}', '已完成基本欄位剖析與摘要']),
|
||||
('方法', ['描述性統計', '類別分布觀察', '視覺化探索']),
|
||||
('研究發現', findings[:3]),
|
||||
('討論', ['解釋主要發現的可能意義', '連結研究問題與資料結果']),
|
||||
('限制', build_limitations(summary, mode)[:2]),
|
||||
('後續研究', ['補充文獻回顧', '加入比較基準與進階分析']),
|
||||
('結論', ['本份簡報可作為研究報告或論文簡報的第一版底稿']),
|
||||
]
|
||||
elif mode == 'business':
|
||||
slides = [
|
||||
('封面', [f'標題：{title}', f'對象：{audience}', f'目的：{purpose}', f'等級：{LEVEL_NOTES[level]}']),
|
||||
('決策問題', ['這份分析要支援什麼決策', '為什麼現在需要處理']),
|
||||
('資料概況', [f'資料筆數：{rows}', f'欄位數：{cols}', '已完成基本資料盤點']),
|
||||
('分析方法', ['描述性統計', '類別分布觀察', '視覺化探索']),
|
||||
('關鍵洞察', findings[:3]),
|
||||
('商業意涵', ['把數據結果轉成管理層可理解的含義', '指出可能影響的目標或 KPI']),
|
||||
('風險與限制', build_limitations(summary, mode)[:2]),
|
||||
('建議行動', ['列出近期可執行事項', '定義需要追蹤的指標']),
|
||||
('結語', ['本份簡報可作為正式管理簡報的第一版底稿']),
|
||||
]
|
||||
else:
|
||||
slides = [
|
||||
('封面', [f'標題：{title}', f'對象：{audience}', f'目的：{purpose}', f'等級：{LEVEL_NOTES[level]}']),
|
||||
('痛點 / 機會', ['說明這份分析解決什麼問題', '點出為什麼值得關注']),
|
||||
('證據基礎', [f'資料筆數：{rows}', f'欄位數：{cols}', '已完成資料摘要與圖表探索']),
|
||||
('方法', ['描述性統計', '類別觀察', '關鍵圖表整理']),
|
||||
('核心亮點', findings[:3]),
|
||||
('為什麼重要', ['連結價值、影響與說服力', '把發現轉成可傳達的敘事']),
|
||||
('風險', build_limitations(summary, mode)[:2]),
|
||||
('Next Step / Ask', ['明確提出下一步', '對齊資源、合作或決策需求']),
|
||||
('結語', ['本份 deck 可作為提案或募資簡報的第一版底稿']),
|
||||
]
|
||||
|
||||
parts = [f"# {title}｜簡報稿\n\n- 模式：`{mode}`\n- 等級：`{level}` — {LEVEL_NOTES[level]}\n"]
|
||||
slide_no = 1
|
||||
for heading, bullets in slides:
|
||||
parts.append(f"## Slide {slide_no} — {heading}")
|
||||
parts.extend([f"- {x}" for x in bullets])
|
||||
parts.append('')
|
||||
slide_no += 1
|
||||
|
||||
if level in {'v3', 'v4'} and plots:
|
||||
for plot in plots:
|
||||
note = interpret_plot(plot, mode)
|
||||
parts.append(f"## Slide {slide_no} — {note['title']}")
|
||||
parts.append(f"- 圖檔：{plot.name}")
|
||||
parts.append(f"- 解讀：{note['summary']}")
|
||||
parts.append(f"- 延伸：{note['so_what']}")
|
||||
parts.append('')
|
||||
slide_no += 1
|
||||
return '\n'.join(parts).strip() + '\n'
|
||||
|
||||
|
||||
def make_speaker_notes(title: str, mode: str, summary: dict[str, Any], plots: list[Path], level: str) -> str:
|
||||
findings = build_key_findings(summary)
|
||||
findings_md = '\n'.join([f"- {x}" for x in findings])
|
||||
opener = {
|
||||
'academic': '先交代研究背景、研究問題與資料來源，再說明這份內容是研究草稿第一版。',
|
||||
'business': '先講這份分析支援哪個決策，再交代這份內容的管理價值與時間敏感性。',
|
||||
'pitch': '先抓住聽眾注意力，說明痛點、機會與這份資料為何值得相信。',
|
||||
}[mode]
|
||||
closer = {
|
||||
'academic': '結尾時回到研究限制與後續研究方向。',
|
||||
'business': '結尾時回到建議行動與追蹤機制。',
|
||||
'pitch': '結尾時回到 ask、資源需求與下一步承諾。',
|
||||
}[mode]
|
||||
parts = [
|
||||
f"# {title}｜Speaker Notes",
|
||||
'',
|
||||
f"- 模式：`{mode}`",
|
||||
f"- 等級：`{level}` — {LEVEL_NOTES[level]}",
|
||||
'',
|
||||
'## 開場',
|
||||
f"- {opener}",
|
||||
'',
|
||||
'## 重點提示',
|
||||
findings_md,
|
||||
'',
|
||||
]
|
||||
if level in {'v3', 'v4'} and plots:
|
||||
parts.extend(['## 逐圖口頭提示', ''])
|
||||
for plot in plots:
|
||||
note = interpret_plot(plot, mode)
|
||||
parts.append(f"### {plot.name}")
|
||||
parts.append(f"- {note['summary']}")
|
||||
parts.append(f"- {note['so_what']}")
|
||||
parts.append('')
|
||||
parts.extend(['## 收尾建議', f"- {closer}", '- 針對最重要的一張圖，多講一層其背後的意義與行動建議。', ''])
|
||||
return '\n'.join(parts)
|
||||
|
||||
|
||||
def make_deck_html(title: str, audience: str, purpose: str, slides_md: str, plots: list[Path], mode: str, level: str) -> str:
|
||||
if level == 'v4':
|
||||
theme = {
|
||||
'academic': {'primary': '#0f172a', 'accent': '#334155', 'bg': '#eef2ff', 'hero': 'linear-gradient(135deg,#0f172a 0%,#1e293b 55%,#475569 100%)'},
|
||||
'business': {'primary': '#0b3b66', 'accent': '#1d4ed8', 'bg': '#eff6ff', 'hero': 'linear-gradient(135deg,#0b3b66 0%,#1d4ed8 60%,#60a5fa 100%)'},
|
||||
'pitch': {'primary': '#4c1d95', 'accent': '#7c3aed', 'bg': '#faf5ff', 'hero': 'linear-gradient(135deg,#4c1d95 0%,#7c3aed 60%,#c084fc 100%)'},
|
||||
}[mode]
|
||||
primary = theme['primary']
|
||||
accent = theme['accent']
|
||||
bg = theme['bg']
|
||||
hero = theme['hero']
|
||||
plot_map = {p.name: p for p in plots}
|
||||
else:
|
||||
primary = '#1f2937'
|
||||
accent = '#2563eb'
|
||||
bg = '#f6f8fb'
|
||||
hero = None
|
||||
plot_map = {p.name: p for p in plots}
|
||||
|
||||
slide_blocks = []
|
||||
current = []
|
||||
current_title = None
|
||||
for line in slides_md.splitlines():
|
||||
if line.startswith('## Slide '):
|
||||
if current_title is not None:
|
||||
slide_blocks.append((current_title, current))
|
||||
current_title = line.replace('## ', '', 1)
|
||||
current = []
|
||||
elif line.startswith('- 模式：') or line.startswith('- 等級：') or line.startswith('# '):
|
||||
continue
|
||||
else:
|
||||
current.append(line)
|
||||
if current_title is not None:
|
||||
slide_blocks.append((current_title, current))
|
||||
|
||||
sections = []
|
||||
for heading, body in slide_blocks:
|
||||
body_html = []
|
||||
referenced_plot = None
|
||||
for line in body:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
if line.startswith('- 圖檔：'):
|
||||
plot_name = line.replace('- 圖檔：', '', 1).strip()
|
||||
referenced_plot = plot_map.get(plot_name)
|
||||
body_html.append(f"<li>{html.escape(line[2:])}</li>")
|
||||
elif line.startswith('- '):
|
||||
body_html.append(f"<li>{html.escape(line[2:])}</li>")
|
||||
else:
|
||||
body_html.append(f"<p>{html.escape(line)}</p>")
|
||||
img_html = ''
|
||||
if referenced_plot and level in {'v3', 'v4'}:
|
||||
img_html = f"<div class='plot-single'><img src='{html.escape(referenced_plot.name)}' alt='{html.escape(referenced_plot.name)}' /><div class='plot-caption'>圖：{html.escape(referenced_plot.name)}</div></div>"
|
||||
list_items = ''.join(x for x in body_html if x.startswith('<li>'))
|
||||
paras = ''.join(x for x in body_html if x.startswith('<p>'))
|
||||
list_html = f"<ul>{list_items}</ul>" if list_items else ''
|
||||
if level == 'v4':
|
||||
sections.append(
|
||||
f"<section class='slide'><div class='slide-top'><div class='eyebrow'>{html.escape(mode.upper())}</div>"
|
||||
f"<div class='page-tag'>{html.escape(heading.split(' — ')[0])}</div></div><h2>{html.escape(heading)}</h2>{paras}{list_html}{img_html}</section>"
|
||||
)
|
||||
else:
|
||||
sections.append(f"<section class='slide'><h2>{html.escape(heading)}</h2>{paras}{list_html}{img_html}</section>")
|
||||
|
||||
if level == 'v4':
|
||||
css = f"""
|
||||
@page {{ size: A4 landscape; margin: 0; }}
|
||||
@media print {{
|
||||
body {{ background: #fff; padding: 0; }}
|
||||
.slide {{ box-shadow: none; margin: 0; min-height: 100vh; border-radius: 0; page-break-after: always; page-break-inside: avoid; border-top-width: 16px; border-top-style: solid; border-top-color: {accent}; }}
|
||||
.hero {{ box-shadow: none; margin: 0; min-height: 100vh; border-radius: 0; }}
|
||||
}}
|
||||
body {{ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans CJK TC', sans-serif; background: {bg}; margin: 0; padding: 32px; color: {primary}; }}
|
||||
.hero {{ max-width: 1180px; margin: 0 auto 32px; padding: 56px 64px; border-radius: 32px; background: {hero}; color: white; box-shadow: 0 32px 64px rgba(15,23,42,.15); display: flex; flex-direction: column; justify-content: center; min-height: 500px; }}
|
||||
.hero h1 {{ margin: 12px 0 20px; font-size: 52px; line-height: 1.2; letter-spacing: -0.02em; font-weight: 800; text-wrap: balance; }}
|
||||
.hero p {{ margin: 8px 0; font-size: 20px; opacity: .9; font-weight: 400; }}
|
||||
.hero-meta {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 16px; margin-top: 48px; }}
|
||||
.hero-card {{ background: rgba(255,255,255,.1); border: 1px solid rgba(255,255,255,.2); border-radius: 20px; padding: 20px 24px; backdrop-filter: blur(10px); }}
|
||||
.hero-card strong {{ display: block; font-size: 14px; text-transform: uppercase; letter-spacing: 0.05em; opacity: 0.8; margin-bottom: 6px; }}
|
||||
.slide {{ background: #fff; border-radius: 32px; padding: 48px 56px; margin: 0 auto 32px; max-width: 1180px; min-height: 660px; box-shadow: 0 16px 48px rgba(15,23,42,.08); page-break-after: always; border-top: 16px solid {accent}; position: relative; overflow: hidden; display: flex; flex-direction: column; }}
|
||||
.slide::after {{ content: ''; position: absolute; right: -80px; top: -80px; width: 240px; height: 240px; background: radial-gradient(circle, {bg} 0%, rgba(255,255,255,0) 70%); pointer-events: none; }}
|
||||
.slide-top {{ display: flex; justify-content: space-between; align-items: center; margin-bottom: 24px; z-index: 1; }}
|
||||
h1, h2 {{ margin-top: 0; font-weight: 700; }}
|
||||
h2 {{ font-size: 36px; margin-bottom: 24px; color: {primary}; letter-spacing: -0.01em; }}
|
||||
.slide p {{ font-size: 20px; line-height: 1.6; color: #334155; margin-bottom: 16px; }}
|
||||
.slide ul {{ line-height: 1.6; font-size: 22px; padding-left: 28px; color: #1e293b; margin-top: 8px; flex-grow: 1; }}
|
||||
.slide li {{ position: relative; padding-left: 8px; }}
|
||||
.slide li + li {{ margin-top: 14px; }}
|
||||
.slide li::marker {{ color: {accent}; font-weight: bold; }}
|
||||
.eyebrow {{ display: inline-flex; align-items: center; padding: 8px 16px; border-radius: 999px; background: {bg}; color: {accent}; font-weight: 800; font-size: 13px; letter-spacing: .1em; box-shadow: 0 2px 8px rgba(0,0,0,0.04); }}
|
||||
.page-tag {{ color: #94a3b8; font-size: 14px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.05em; }}
|
||||
.plot-single {{ margin-top: auto; text-align: center; padding-top: 24px; position: relative; display: flex; flex-direction: column; align-items: center; justify-content: center; }}
|
||||
.plot-single img {{ max-width: 100%; max-height: 380px; border: 1px solid #e2e8f0; border-radius: 20px; background: #f8fafc; box-shadow: 0 12px 32px rgba(15,23,42,.06); padding: 8px; }}
|
||||
.plot-caption {{ margin-top: 14px; font-size: 15px !important; color: #64748b !important; font-style: italic; text-align: center; background: #f1f5f9; padding: 6px 16px; border-radius: 999px; }}
|
||||
""".strip()
|
||||
hero_html = (
|
||||
f"<div class='hero'><div class='eyebrow'>{html.escape(mode.upper())}</div>"
|
||||
f"<h1>{html.escape(title)}</h1><p>適用對象：{html.escape(audience)}</p><p>目的：{html.escape(purpose)}</p>"
|
||||
f"<div class='hero-meta'>"
|
||||
f"<div class='hero-card'><strong>等級</strong><br>{html.escape(level)} — {html.escape(LEVEL_NOTES[level])}</div>"
|
||||
f"<div class='hero-card'><strong>圖表數量</strong><br>{len(plots)}</div>"
|
||||
f"<div class='hero-card'><strong>輸出定位</strong><br>正式 deck / PDF-ready</div>"
|
||||
f"</div></div>"
|
||||
)
|
||||
else:
|
||||
css = f"""
|
||||
body {{ font-family: Arial, 'Noto Sans CJK TC', sans-serif; background: {bg}; margin: 0; padding: 24px; color: {primary}; }}
|
||||
.hero {{ max-width: 1100px; margin: 0 auto 24px; padding: 8px 6px; }}
|
||||
.slide {{ background: #fff; border-radius: 18px; padding: 32px; margin: 0 auto 24px; max-width: 1100px; box-shadow: 0 8px 28px rgba(0,0,0,.08); page-break-after: always; border-top: 10px solid {accent}; }}
|
||||
h1, h2 {{ margin-top: 0; }}
|
||||
h1 {{ font-size: 40px; }}
|
||||
ul {{ line-height: 1.7; }}
|
||||
.plot-single {{ margin-top: 18px; text-align: center; }}
|
||||
img {{ max-width: 100%; border: 1px solid #ddd; border-radius: 12px; background: #fff; }}
|
||||
.plot-caption {{ margin-top: 10px; font-size: 14px; color: #6b7280; font-style: italic; }}
|
||||
""".strip()
|
||||
hero_html = (
|
||||
f"<div class='hero'><h1>{html.escape(title)}</h1><p>對象：{html.escape(audience)}</p>"
|
||||
f"<p>目的：{html.escape(purpose)}</p><p>等級：{html.escape(level)} — {html.escape(LEVEL_NOTES[level])}</p></div>"
|
||||
)
|
||||
|
||||
return (
|
||||
"<!doctype html><html><head><meta charset='utf-8'>"
|
||||
f"<title>{html.escape(title)}</title><style>{css}</style></head><body>"
|
||||
+ hero_html
|
||||
+ ''.join(sections)
|
||||
+ "</body></html>"
|
||||
)
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Generate paper/slides bundle from analysis outputs',
|
||||
epilog=(
|
||||
'Levels: '
|
||||
'v2=基礎交付版（paper/slides/speaker-notes/deck）； '
|
||||
'v3=洞察強化版（v2 + insights + 每張圖逐頁解讀）； '
|
||||
'v4=正式交付版（v3 + 更正式 deck 視覺 + PDF-ready 工作流）'
|
||||
),
|
||||
)
|
||||
parser.add_argument('--analysis-dir', required=True)
|
||||
parser.add_argument('--output-dir', required=True)
|
||||
parser.add_argument('--title', default='研究分析草稿')
|
||||
parser.add_argument('--audience', default='決策者')
|
||||
parser.add_argument('--purpose', default='研究報告')
|
||||
parser.add_argument('--mode', default='business', choices=sorted(MODES))
|
||||
parser.add_argument(
|
||||
'--level',
|
||||
default='v4',
|
||||
choices=sorted(LEVELS),
|
||||
help='輸出等級：v2=基礎交付版；v3=洞察強化版；v4=正式交付版（預設）',
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
analysis_dir = Path(args.analysis_dir).expanduser().resolve()
|
||||
output_dir = Path(args.output_dir).expanduser().resolve()
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
summary_path = analysis_dir / 'summary.json'
|
||||
report_path = analysis_dir / 'report.md'
|
||||
if not summary_path.exists():
|
||||
raise SystemExit(f'Missing summary.json in {analysis_dir}')
|
||||
if not report_path.exists():
|
||||
raise SystemExit(f'Missing report.md in {analysis_dir}')
|
||||
|
||||
summary = read_json(summary_path)
|
||||
report_md = read_text(report_path)
|
||||
plots = find_plots(analysis_dir)
|
||||
insights_md = make_insights_md(args.title, args.mode, summary, plots) if args.level in {'v3', 'v4'} else None
|
||||
|
||||
paper_md = make_paper(args.title, args.audience, args.purpose, args.mode, args.level, summary, report_md, plots, insights_md)
|
||||
slides_md = make_slides(args.title, args.audience, args.purpose, args.mode, summary, plots, args.level)
|
||||
speaker_notes = make_speaker_notes(args.title, args.mode, summary, plots, args.level)
|
||||
deck_html = make_deck_html(args.title, args.audience, args.purpose, slides_md, plots, args.mode, args.level)
|
||||
|
||||
for plot in plots:
|
||||
dest = output_dir / plot.name
|
||||
if dest != plot:
|
||||
shutil.copy2(plot, dest)
|
||||
|
||||
(output_dir / 'paper.md').write_text(paper_md, encoding='utf-8')
|
||||
(output_dir / 'slides.md').write_text(slides_md, encoding='utf-8')
|
||||
(output_dir / 'speaker-notes.md').write_text(speaker_notes, encoding='utf-8')
|
||||
(output_dir / 'deck.html').write_text(deck_html, encoding='utf-8')
|
||||
if insights_md:
|
||||
(output_dir / 'insights.md').write_text(insights_md, encoding='utf-8')
|
||||
|
||||
manifest_outputs = {
|
||||
'paper': str(output_dir / 'paper.md'),
|
||||
'slides': str(output_dir / 'slides.md'),
|
||||
'speakerNotes': str(output_dir / 'speaker-notes.md'),
|
||||
'deckHtml': str(output_dir / 'deck.html'),
|
||||
}
|
||||
if insights_md:
|
||||
manifest_outputs['insights'] = str(output_dir / 'insights.md')
|
||||
|
||||
manifest = {
|
||||
'title': args.title,
|
||||
'audience': args.audience,
|
||||
'purpose': args.purpose,
|
||||
'mode': args.mode,
|
||||
'level': args.level,
|
||||
'levelNote': LEVEL_NOTES[args.level],
|
||||
'analysisDir': str(analysis_dir),
|
||||
'outputs': manifest_outputs,
|
||||
'plots': [str(p) for p in plots],
|
||||
}
|
||||
(output_dir / 'bundle.json').write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding='utf-8')
|
||||
print(json.dumps(manifest, ensure_ascii=False, indent=2))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise SystemExit(main())
|
||||
@@ -23,6 +23,7 @@ tools:
|
||||
- `GITEA_URL`: Gitea 基礎 URL（https://git.nature.edu.kg）
|
||||
- `GITEA_TOKEN_<AGENT>`: 你的 Gitea API token（根據 agent ID 取對應的）
|
||||
- Agent → Gitea 帳號對應：
|
||||
- main → `xiaoming`（小明，專案管理/綜合審查）
|
||||
- tiangong → `tiangong`（天工，架構/安全）
|
||||
- kaiwu → `kaiwu`（開物，UX/前端）
|
||||
- yucheng → `yucheng`（玉成，全棧/測試）
|
||||
@@ -31,6 +32,12 @@ tools:
|
||||
|
||||
根據你的角色，重點審查不同面向：
|
||||
|
||||
### 小明（main）— 專案經理
|
||||
- 整體 skill 的完整性與一致性
|
||||
- SKILL.md 描述是否清楚、trigger 是否遺漏常見用法
|
||||
- 跨 skill 的重複邏輯或可整合之處
|
||||
- 文件與實作是否同步
|
||||
|
||||
### 天工（tiangong）— 架構設計師
|
||||
- SKILL.md 的 trigger 設計是否合理、會不會誤觸發
|
||||
- handler.ts 的錯誤處理、邊界情況
|
||||
|
||||
@@ -9,6 +9,7 @@ const REPO_NAME = 'openclaw-skill';
|
||||
|
||||
// Agent ID → Gitea 帳號 & token 環境變數對應
|
||||
const AGENT_MAP: Record<string, { username: string; tokenEnv: string }> = {
|
||||
main: { username: 'xiaoming', tokenEnv: 'GITEA_TOKEN_XIAOMING' },
|
||||
tiangong: { username: 'tiangong', tokenEnv: 'GITEA_TOKEN_TIANGONG' },
|
||||
kaiwu: { username: 'kaiwu', tokenEnv: 'GITEA_TOKEN_KAIWU' },
|
||||
yucheng: { username: 'yucheng', tokenEnv: 'GITEA_TOKEN_YUCHENG' },
|
||||
|
||||
1
skills/summarize
Symbolic link
1
skills/summarize
Symbolic link
@@ -0,0 +1 @@
|
||||
/home/selig/.openclaw/workspace/skills/summarize
|
||||
7
skills/tavily-tool/.clawhub/origin.json
Normal file
7
skills/tavily-tool/.clawhub/origin.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"version": 1,
|
||||
"registry": "https://clawhub.ai",
|
||||
"slug": "tavily-tool",
|
||||
"installedVersion": "0.1.1",
|
||||
"installedAt": 1773199294594
|
||||
}
|
||||
46
skills/tavily-tool/SKILL.md
Normal file
46
skills/tavily-tool/SKILL.md
Normal file
@@ -0,0 +1,46 @@
|
||||
---
|
||||
name: tavily
|
||||
description: Use Tavily web search/discovery to find URLs/sources, do lead research, gather up-to-date links, or produce a cited summary from web results.
|
||||
metadata: {"openclaw":{"requires":{"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}}
|
||||
---
|
||||
|
||||
# Tavily
|
||||
|
||||
Use the bundled CLI to run Tavily searches from the terminal and collect sources fast.
|
||||
|
||||
## Quick start (CLI)
|
||||
|
||||
The scripts **require** `TAVILY_API_KEY` in the environment (sent as `Authorization: Bearer ...`).
|
||||
|
||||
```bash
|
||||
export TAVILY_API_KEY="..."
|
||||
node skills/tavily/scripts/tavily_search.js --query "best rust http client" --max_results 5
|
||||
```
|
||||
|
||||
- JSON response is printed to **stdout**.
|
||||
- A simple URL list is printed to **stderr** by default.
|
||||
|
||||
## Common patterns
|
||||
|
||||
### Get URLs only
|
||||
|
||||
```bash
|
||||
export TAVILY_API_KEY="..."
|
||||
node skills/tavily/scripts/tavily_search.js --query "OpenTelemetry collector config" --urls-only
|
||||
```
|
||||
|
||||
### Restrict to (or exclude) specific domains
|
||||
|
||||
```bash
|
||||
export TAVILY_API_KEY="..."
|
||||
node skills/tavily/scripts/tavily_search.js \
|
||||
--query "oauth device code flow" \
|
||||
--include_domains oauth.net,datatracker.ietf.org \
|
||||
--exclude_domains medium.com
|
||||
```
|
||||
|
||||
## Notes
|
||||
|
||||
- The bundled CLI supports a subset of Tavily’s request fields (query, max_results, include_domains, exclude_domains).
|
||||
- For API field notes and more examples, read: `references/tavily-api.md`.
|
||||
- Wrapper script (optional): `scripts/tavily_search.sh`.
|
||||
6
skills/tavily-tool/_meta.json
Normal file
6
skills/tavily-tool/_meta.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"ownerId": "kn78x7kg14jggfbz385es5bdrn81ddgw",
|
||||
"slug": "tavily-tool",
|
||||
"version": "0.1.1",
|
||||
"publishedAt": 1772290357545
|
||||
}
|
||||
55
skills/tavily-tool/references/tavily-api.md
Normal file
55
skills/tavily-tool/references/tavily-api.md
Normal file
@@ -0,0 +1,55 @@
|
||||
# Tavily API notes (quick reference)
|
||||
|
||||
## Endpoint
|
||||
|
||||
- Search: `POST https://api.tavily.com/search`
|
||||
|
||||
## Auth
|
||||
|
||||
- Send the API key via HTTP header: `Authorization: Bearer <TAVILY_API_KEY>`.
|
||||
- This skill’s scripts read the key from **env var only**: `TAVILY_API_KEY`.
|
||||
|
||||
## Common request fields
|
||||
|
||||
```json
|
||||
{
|
||||
"query": "...",
|
||||
"max_results": 5,
|
||||
"include_domains": ["example.com"],
|
||||
"exclude_domains": ["spam.com"]
|
||||
}
|
||||
```
|
||||
|
||||
(Additional Tavily options exist; this skill’s CLI supports only a common subset for discovery use-cases.)
|
||||
|
||||
## Script usage
|
||||
|
||||
### JSON output (stdout) + URL list (stderr)
|
||||
|
||||
```bash
|
||||
export TAVILY_API_KEY="..."
|
||||
node skills/tavily/scripts/tavily_search.js --query "best open source vector database" --max_results 5
|
||||
```
|
||||
|
||||
### URLs only
|
||||
|
||||
```bash
|
||||
export TAVILY_API_KEY="..."
|
||||
node skills/tavily/scripts/tavily_search.js --query "SvelteKit tutorial" --urls-only
|
||||
```
|
||||
|
||||
### Include / exclude domains
|
||||
|
||||
```bash
|
||||
export TAVILY_API_KEY="..."
|
||||
node skills/tavily/scripts/tavily_search.js \
|
||||
--query "websocket load testing" \
|
||||
--include_domains k6.io,github.com \
|
||||
--exclude_domains medium.com
|
||||
```
|
||||
|
||||
## Notes
|
||||
|
||||
- Exit code `2` indicates missing required args or missing `TAVILY_API_KEY`.
|
||||
- Exit code `3` indicates network/HTTP failure.
|
||||
- Exit code `4` indicates a non-JSON response.
|
||||
161
skills/tavily-tool/scripts/tavily_search.js
Normal file
161
skills/tavily-tool/scripts/tavily_search.js
Normal file
@@ -0,0 +1,161 @@
|
||||
#!/usr/bin/env node
|
||||
/**
|
||||
* Tavily Search CLI
|
||||
*
|
||||
* - Reads TAVILY_API_KEY from env only.
|
||||
* - Prints full JSON response to stdout.
|
||||
* - Prints a simple list of URLs to stderr by default (can be disabled).
|
||||
*/
|
||||
|
||||
const TAVILY_ENDPOINT = 'https://api.tavily.com/search';
|
||||
|
||||
function usage(msg) {
|
||||
if (msg) console.error(`Error: ${msg}\n`);
|
||||
console.error(`Usage:
|
||||
tavily_search.js --query "..." [--max_results 5] [--include_domains a.com,b.com] [--exclude_domains x.com,y.com]
|
||||
|
||||
Options:
|
||||
--query, -q Search query (required)
|
||||
--max_results, -n Max results (default: 5; clamped to 0..20)
|
||||
--include_domains Comma-separated domains to include
|
||||
--exclude_domains Comma-separated domains to exclude
|
||||
--urls-stderr Print URL list to stderr (default: true)
|
||||
--no-urls-stderr Disable URL list to stderr
|
||||
--urls-only Print URLs (one per line) to stdout instead of JSON
|
||||
--help, -h Show help
|
||||
|
||||
Env:
|
||||
TAVILY_API_KEY (required) Tavily API key
|
||||
|
||||
Exit codes:
|
||||
0 success
|
||||
2 usage / missing required inputs
|
||||
3 network / HTTP error
|
||||
4 invalid JSON response
|
||||
`);
|
||||
}
|
||||
|
||||
function parseArgs(argv) {
|
||||
const out = {
|
||||
query: null,
|
||||
max_results: 5,
|
||||
include_domains: null,
|
||||
exclude_domains: null,
|
||||
urls_stderr: true,
|
||||
urls_only: false,
|
||||
help: false,
|
||||
};
|
||||
|
||||
for (let i = 0; i < argv.length; i++) {
|
||||
const a = argv[i];
|
||||
if (a === '--help' || a === '-h') out.help = true;
|
||||
else if (a === '--query' || a === '-q') out.query = argv[++i];
|
||||
else if (a === '--max_results' || a === '-n') out.max_results = Number(argv[++i]);
|
||||
else if (a === '--include_domains') out.include_domains = argv[++i];
|
||||
else if (a === '--exclude_domains') out.exclude_domains = argv[++i];
|
||||
else if (a === '--urls-stderr') out.urls_stderr = true;
|
||||
else if (a === '--no-urls-stderr') out.urls_stderr = false;
|
||||
else if (a === '--urls-only') out.urls_only = true;
|
||||
else return { error: `Unknown arg: ${a}` };
|
||||
}
|
||||
|
||||
if (Number.isNaN(out.max_results) || !Number.isFinite(out.max_results)) {
|
||||
return { error: `--max_results must be a number` };
|
||||
}
|
||||
// Tavily allows 0..20; clamp to stay in range.
|
||||
out.max_results = Math.max(0, Math.min(20, Math.trunc(out.max_results)));
|
||||
|
||||
const csvToArray = (s) => {
|
||||
if (!s) return null;
|
||||
const arr = s.split(',').map(x => x.trim()).filter(Boolean);
|
||||
return arr.length ? arr : null;
|
||||
};
|
||||
|
||||
out.include_domains = csvToArray(out.include_domains);
|
||||
out.exclude_domains = csvToArray(out.exclude_domains);
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
async function main() {
|
||||
const args = parseArgs(process.argv.slice(2));
|
||||
if (args.error) {
|
||||
usage(args.error);
|
||||
process.exit(2);
|
||||
}
|
||||
if (args.help) {
|
||||
usage();
|
||||
process.exit(0);
|
||||
}
|
||||
|
||||
const apiKey = process.env.TAVILY_API_KEY;
|
||||
if (!apiKey) {
|
||||
usage('TAVILY_API_KEY env var is required');
|
||||
process.exit(2);
|
||||
}
|
||||
|
||||
if (!args.query) {
|
||||
usage('--query is required');
|
||||
process.exit(2);
|
||||
}
|
||||
|
||||
const payload = {
|
||||
query: args.query,
|
||||
max_results: args.max_results,
|
||||
};
|
||||
if (args.include_domains) payload.include_domains = args.include_domains;
|
||||
if (args.exclude_domains) payload.exclude_domains = args.exclude_domains;
|
||||
|
||||
let res;
|
||||
try {
|
||||
res = await fetch(TAVILY_ENDPOINT, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${apiKey}`,
|
||||
},
|
||||
body: JSON.stringify(payload),
|
||||
});
|
||||
} catch (e) {
|
||||
console.error(`Network error calling Tavily: ${e?.message || String(e)}`);
|
||||
process.exit(3);
|
||||
}
|
||||
|
||||
if (!res.ok) {
|
||||
let bodyText = '';
|
||||
try { bodyText = await res.text(); } catch {}
|
||||
console.error(`Tavily HTTP error: ${res.status} ${res.statusText}`);
|
||||
if (bodyText) console.error(bodyText);
|
||||
process.exit(3);
|
||||
}
|
||||
|
||||
let data;
|
||||
try {
|
||||
data = await res.json();
|
||||
} catch (e) {
|
||||
console.error(`Invalid JSON response from Tavily: ${e?.message || String(e)}`);
|
||||
process.exit(4);
|
||||
}
|
||||
|
||||
const urls = Array.isArray(data?.results)
|
||||
? data.results.map(r => r?.url).filter(Boolean)
|
||||
: [];
|
||||
|
||||
if (args.urls_only) {
|
||||
for (const u of urls) process.stdout.write(`${u}\n`);
|
||||
process.exit(0);
|
||||
}
|
||||
|
||||
process.stdout.write(JSON.stringify(data, null, 2));
|
||||
process.stdout.write('\n');
|
||||
|
||||
if (args.urls_stderr && urls.length) {
|
||||
console.error('\nURLs:');
|
||||
for (const u of urls) console.error(u);
|
||||
}
|
||||
}
|
||||
|
||||
main().catch((e) => {
|
||||
console.error(`Unexpected error: ${e?.stack || e?.message || String(e)}`);
|
||||
process.exit(1);
|
||||
});
|
||||
9
skills/tavily-tool/scripts/tavily_search.sh
Normal file
9
skills/tavily-tool/scripts/tavily_search.sh
Normal file
@@ -0,0 +1,9 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
# Wrapper to run the Node Tavily search CLI.
|
||||
# Usage:
|
||||
# TAVILY_API_KEY=... ./tavily_search.sh --query "..." --max_results 5
|
||||
|
||||
DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
exec node "$DIR/tavily_search.js" "$@"
|
||||
Reference in New Issue
Block a user