context-clean-up

Context Clean Up (audit-only)

This skill identifies what is bloating prompt context and turns it into a safe, reversible plan.

Contract

• Audit-only by default.
• No automatic deletions.
• No unattended config edits.
• No silent cron/session pruning.
• If you ask for changes, the skill should propose:
  1. exact change,
  2. expected impact,
  3. rollback plan,
  4. verification steps.

Safety model

• No exec tool usage.
• No read tool usage.
• If you want file-level analysis, run the bundled script manually and paste the JSON.

Quick start

• /context-clean-up → audit + actionable plan (no changes)

Optional manual report generation:

python3 scripts/context_cleanup_audit.py --out context-cleanup-audit.json

Windows variant:

py -3 scripts/context_cleanup_audit.py --out context-cleanup-audit.json

What to measure (authoritative, not vibes)

When available, prefer fresh-session /context json receipts over subjective claims like “it feels leaner”.

High-signal fields:

• eligible skills
• skills.promptChars
• projectContextChars
• systemPrompt.chars
• promptTokens

If exact receipts are unavailable, fall back to ranked offenders + change scope, but label confidence lower.

Common offender classes

1. Tool result dumps

   • oversized exec output
   • large read output
   • long web_fetch payloads

2. Automation transcript noise

   • cron jobs that say “OK” every run
   • heartbeat messages that are not alert-only

3. Bootstrap reinjection bloat

   • overgrown AGENTS.md / MEMORY.md / SOUL.md / USER.md
   • long runbooks embedded directly in SKILL.md

4. Ambient specialist surface

   • too many always-visible specialist skills that should be on-demand workers/subagents instead

5. Summary accretion

   • repeated summaries that keep historical detail instead of restart-critical facts only

Recommended trim ladder (lowest-risk first)

Phase 1 — Noise discipline

• Make no-op automation truly silent (NO_REPLY or nothing on success).
• Keep alerts out-of-band when possible.

Phase 2 — Bootstrap slimming

• Keep always-injected files short.
• Move long guidance to references/, memory/, or external notes.

Phase 3 — Ambient surface reduction

• Remove low-frequency specialist skills from always-on prompt surface.
• Prefer worker/subagent invocation for specialist flows.

Phase 4 — Higher-risk changes

• Tool-surface or deeper runtime/config narrowing.
• Only propose with stronger rollback and explicit approval.

Workflow (audit → plan)

Step 0 — Determine scope

You need:

• workspace dir
• state dir (<OPENCLAW_STATE_DIR>)

Common defaults:

• macOS/Linux: ~/.openclaw
• Windows: %USERPROFILE%\.openclaw

Step 1 — Run the audit script

python3 scripts/context_cleanup_audit.py --workspace . --state-dir <OPENCLAW_STATE_DIR> --out context-cleanup-audit.json

Interpretation cheatsheet:

• huge tool outputs → transcript bloat
• many cron/system lines → automation bloat
• large bootstrap docs → reinjection bloat

Step 2 — Produce a fix plan

Include:

• top offenders
• lowest-risk fixes first
• expected impact
• rollback notes
• verification plan

Step 3 — Verify

After changes:

• confirm automation is silent on success
• check context growth flattens
• if possible, compare fresh-session /context json before/after

Important caveat

Many OpenClaw runtimes snapshot skills/bootstrap per session. So skill/config slimming often does not fully apply to the current session. Use a new session for authoritative verification.

References

• references/out-of-band-delivery.md
• references/cron-noise-checklist.md