Initial commit: OpenClaw Skill Collection

6 custom skills (assign-task, dispatch-webhook, daily-briefing,
task-capture, qmd-brain, tts-voice) with technical documentation.
Compatible with Claude Code, OpenClaw, Codex CLI, and OpenCode.

openclaw-knowhow-skill/docs/infrastructure/token-use.md

@@ -0,0 +1,104 @@
+# Token Use and Costs
+
+OpenClaw tracks **tokens**, not characters. Tokens are model-specific, but most
+OpenAI-style models average ~4 characters per token for English text.
+
+## How the system prompt is built
+
+OpenClaw assembles its own system prompt on every run. It includes:
+
+* Tool list + short descriptions
+* Skills list (only metadata; instructions are loaded on demand with `read`)
+* Self-update instructions
+* Workspace + bootstrap files (`AGENTS.md`, `SOUL.md`, `TOOLS.md`, `IDENTITY.md`, `USER.md`, `HEARTBEAT.md`, `BOOTSTRAP.md` when new). Large files are truncated by `agents.defaults.bootstrapMaxChars` (default: 20000).
+* Time (UTC + user timezone)
+* Reply tags + heartbeat behavior
+* Runtime metadata (host/OS/model/thinking)
+
+See the full breakdown in [System Prompt](/concepts/system-prompt).
+
+## What counts in the context window
+
+Everything the model receives counts toward the context limit:
+
+* System prompt (all sections listed above)
+* Conversation history (user + assistant messages)
+* Tool calls and tool results
+* Attachments/transcripts (images, audio, files)
+* Compaction summaries and pruning artifacts
+* Provider wrappers or safety headers (not visible, but still counted)
+
+For a practical breakdown (per injected file, tools, skills, and system prompt size), use `/context list` or `/context detail`. See [Context](/concepts/context).
+
+## How to see current token usage
+
+Use these in chat:
+
+* `/status` → **emoji-rich status card** with the session model, context usage,
+  last response input/output tokens, and **estimated cost** (API key only).
+* `/usage off|tokens|full` → appends a **per-response usage footer** to every reply.
+  * Persists per session (stored as `responseUsage`).
+  * OAuth auth **hides cost** (tokens only).
+* `/usage cost` → shows a local cost summary from OpenClaw session logs.
+
+Other surfaces:
+
+* **TUI/Web TUI:** `/status` + `/usage` are supported.
+* **CLI:** `openclaw status --usage` and `openclaw channels list` show
+  provider quota windows (not per-response costs).
+
+## Cost estimation (when shown)
+
+Costs are estimated from your model pricing config:
+
+```
+models.providers.<provider>.models[].cost
+```
+
+These are **USD per 1M tokens** for `input`, `output`, `cacheRead`, and
+`cacheWrite`. If pricing is missing, OpenClaw shows tokens only. OAuth tokens
+never show dollar cost.
+
+## Cache TTL and pruning impact
+
+Provider prompt caching only applies within the cache TTL window. OpenClaw can
+optionally run **cache-ttl pruning**: it prunes the session once the cache TTL
+has expired, then resets the cache window so subsequent requests can re-use the
+freshly cached context instead of re-caching the full history. This keeps cache
+write costs lower when a session goes idle past the TTL.
+
+Configure it in [Gateway configuration](/gateway/configuration) and see the
+behavior details in [Session pruning](/concepts/session-pruning).
+
+Heartbeat can keep the cache **warm** across idle gaps. If your model cache TTL
+is `1h`, setting the heartbeat interval just under that (e.g., `55m`) can avoid
+re-caching the full prompt, reducing cache write costs.
+
+For Anthropic API pricing, cache reads are significantly cheaper than input
+tokens, while cache writes are billed at a higher multiplier. See Anthropic's
+prompt caching pricing for the latest rates and TTL multipliers:
+[https://docs.anthropic.com/docs/build-with-claude/prompt-caching](https://docs.anthropic.com/docs/build-with-claude/prompt-caching)
+
+### Example: keep 1h cache warm with heartbeat
+
+```yaml
+agents:
+  defaults:
+    model:
+      primary: "anthropic/claude-opus-4-5"
+    models:
+      "anthropic/claude-opus-4-5":
+        params:
+          cacheRetention: "long"
+    heartbeat:
+      every: "55m"
+```
+
+## Tips for reducing token pressure
+
+* Use `/compact` to summarize long sessions.
+* Trim large tool outputs in your workflows.
+* Keep skill descriptions short (skill list is injected into the prompt).
+* Prefer smaller models for verbose, exploratory work.
+
+See [Skills](/tools/skills) for the exact skill list overhead formula.