openclaw-skill/skills/code-interpreter/SKILL.md

name: code-interpreter 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.

Code Interpreter

Run local Python code through the bundled runner.

Safety boundary

This is local execution, not a hardened container. Treat it as a convenience tool for trusted, low-risk tasks.

Always:

• Keep work inside the OpenClaw workspace when possible.
• Prefer reading/writing files under the current task directory or an explicit artifact directory.
• Keep timeouts short by default.
• Avoid network access unless the user explicitly asks and the task truly needs it.
• Do not execute untrusted code copied from the web or other people.
• Do not expose secrets, tokens, SSH keys, browser cookies, or system files to the script.

Do not use this skill for:

• system administration
• package installation loops
• long-running servers
• privileged operations
• destructive file changes outside the workspace
• executing arbitrary third-party code verbatim

Runner

Run from the OpenClaw workspace:

python3 {baseDir}/scripts/run_code.py --code 'print(2 + 2)'

Or pass a script file:

python3 {baseDir}/scripts/run_code.py --file path/to/script.py

Or pipe code via stdin:

cat my_script.py | python3 {baseDir}/scripts/run_code.py --stdin

Useful options

# set timeout seconds (default 20)
python3 {baseDir}/scripts/run_code.py --code '...' --timeout 10

# run from a specific working directory inside workspace
python3 {baseDir}/scripts/run_code.py --file script.py --cwd /home/selig/.openclaw/workspace/project

# keep outputs in a known artifact directory inside workspace
python3 {baseDir}/scripts/run_code.py --file script.py --artifact-dir /home/selig/.openclaw/workspace/.tmp/my-analysis

# save full stdout / stderr
python3 {baseDir}/scripts/run_code.py --code '...' --stdout-file out.txt --stderr-file err.txt

Built-in environment

The runner uses the dedicated interpreter at:

• /home/selig/.openclaw/workspace/.venv-code-interpreter/bin/python (use the venv path directly; do not resolve the symlink to system Python)

This keeps plotting/data-analysis dependencies stable without touching the system Python.

The runner exposes these variables to the script:

• OPENCLAW_WORKSPACE
• CODE_INTERPRETER_RUN_DIR
• CODE_INTERPRETER_ARTIFACT_DIR

It also writes a helper file in the run directory:

from ci_helpers import save_text, save_json

Use those helpers to save artifacts into CODE_INTERPRETER_ARTIFACT_DIR.

V4 automatic data analysis

For automatic profiling/report generation from a local data file, use:

• scripts/analyze_data.py
• Reference: references/v4-usage.md

This flow is ideal when the user wants a fast "analyze this CSV/JSON/Excel and give me a report + plots" result.

Output

The runner prints compact JSON:

{
  "ok": true,
  "exitCode": 0,
  "timeout": false,
  "runDir": "...",
  "artifactDir": "...",
  "packageStatus": {"pandas": true, "numpy": true, "matplotlib": false},
  "artifacts": [{"path": "...", "bytes": 123}],
  "stdout": "...",
  "stderr": "..."
}

Workflow

1. Decide whether the task is a good fit for local trusted execution.
2. Write the smallest script that solves the problem.
3. Use --artifact-dir when the user may want generated files preserved.
4. Run with a short timeout.
5. Inspect stdout, stderr, and artifacts.
6. If producing files, mention their exact paths in the reply.

Patterns

Exact calculation

Use a one-liner with --code.

File analysis

Read input files from workspace, then write summaries/derived files back to artifactDir.

Automatic report bundle

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.

Table inspection

Prefer pandas when available; otherwise fall back to csv/json stdlib.

Plotting

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.

Reusable logic

Write a small .py file in the current task area, run with --file, then keep it if it may be reused.

Notes

• The runner launches python3 -B with a minimal environment.
• It creates an isolated temp run directory under workspace/.tmp/code-interpreter-runs/.
• stdout / stderr are truncated in the JSON preview if very large; save to files when needed.
• MPLBACKEND=Agg is set so headless plotting works when matplotlib is installed.
• If a task needs stronger isolation than this local runner provides, do not force it—use a real sandbox/container approach instead.