OpenClaw Agent Optimization

Use this skill to tune an OpenClaw workspace for cost-aware routing, parallel-first delegation, and lean context.

Default posture

This skill is advisory first. It should produce:

• audit,

• options,

• recommended plan,

• exact patch proposal,

• rollback,

• verification plan.

No persistent mutations without explicit approval.

Quick start

1) Full audit (safe, no changes)

Audit my OpenClaw setup for cost, reliability, and context bloat. Output a prioritized plan with rollback notes. Do NOT apply changes.

2) Context bloat / transcript noise

My OpenClaw context is bloating (slow replies / high cost / lots of transcript noise). Identify the top offenders (tools, crons, bootstrap files, skills) and propose the smallest reversible fixes first. Do NOT apply changes.

3) Model routing / delegation posture

Propose a model routing plan for (a) coding/engineering, (b) short notifications/reminders, (c) reasoning-heavy research/writing. Include an exact config patch + rollback plan, but do NOT apply changes.

What good output looks like

• Executive summary

• Top drivers

  • cost
  • context
  • reliability
  • operator friction

• Options A/B/C with tradeoffs

• Recommended plan (smallest safe change first)

• Exact proposals + rollback + verify

Safety contract

• Do not mutate persistent settings without explicit approval.

• Do not create/update/remove cron jobs without explicit approval.

• If an optimization reduces monitoring coverage, present options and require choice.

• Before any approved change, show:

  1. exact change,
  2. expected impact,
  3. rollback plan,
  4. post-change verification.

High-ROI optimization levers

1) Output discipline for automation

Make maintenance loops truly silent on success.

2) Separate work from notification

If you want alerts but want interactive context lean:

• do the work quietly

• notify out-of-band with a short human receipt

3) Bootstrap discipline

Keep always-injected files short and load-bearing only. Move long runbooks into references/ or adjacent notes.

4) Ambient specialist surface reduction

A common hidden tax is too many always-visible specialist skills. If a workflow is low-frequency or specialist:

• prefer on-demand worker/subagent usage,

• do not keep it permanently ambient in main-chat prompt surface.

5) Measure optimizations authoritatively

Prefer fresh-session /context json or equivalent receipts over “feels better”. High-signal fields include:

• eligible skills

• skills.promptChars

• projectContextChars

• systemPrompt.chars

• promptTokens

6) Verification-first ops hygiene

After any approved optimization, verify:

• core chat still works

• recall/behavior did not degrade

• new session actually picks up the change

• rollback path is proven, not theoretical

Workflow (concise)

1. Audit rules + memory: keep restart-critical facts only.

2. Audit skill surface: trim ambient specialists before touching tool surface.

3. Audit transcripts/noise: silence cron and heartbeat success paths.

4. Audit model routing and delegation posture.

5. Recommend the smallest viable change first.

6. Verify on a new session when skill/bootstrap snapshotting exists.

Notes

• Some runtimes snapshot skills/config per session. If you install/update skills and do not see changes, start a new session.

• Prefer short SKILL.md + references/ for long runbooks.

• If context bloat is the main complaint, pair this skill with context-clean-up (audit-only).

References

• references/optimization-playbook.md

• references/model-selection.md

• references/context-management.md

• references/agent-orchestration.md

• references/cron-optimization.md

• references/heartbeat-optimization.md

• references/memory-patterns.md

• references/continuous-learning.md

• references/safeguards.md