Skill: x-rayluan/openclaw-self-improvement (version 0.2.11)

--- name: openclaw-self-improvement description: A reusable operator-guided workflow improvement skill for OpenClaw and ClawLite that turns repeated failures into logged learnings, binary eval loops, SOPs, checklists, and proof-based operational improvements. metadata: { "openclaw": { "requires": { "bins": ["node"] }, "writes": [".learnings/", "memory/harness-backlog-latest.md", "mission-control/data/delivery-receipts/agent-scorecard-YYYY-MM-DD.md", "AGENTS.md", "TOOLS.md", "SOUL.md"], "env": ["WORKSPACE", "OBSIDIAN_LEARNINGS_DIR"], "network": false, "notes": "Local-file workflow only. Promotion writes should be reviewed and can be previewed with --dry-run." } } --- # OpenClaw / ClawLite Self-Improvement Use this skill to turn mistakes, corrections, blockers, and better approaches into durable operating knowledge. ## What problem this solves AI ops often repeat the same failures because mistakes stay in chat history instead of becoming system rules. This skill creates a lightweight improvement loop: - log failures and learnings - separate errors from feature requests - run small eval-driven experiments on repeated failures - classify harness/runtime failures instead of blaming vague “model issues” - generate daily agent scorecards from real evidence chains - promote important patterns into AGENTS.md / TOOLS.md / SOUL.md - write operator notes into Obsidian vault - support stricter acceptance via Karen / Mission Control ## When to use Use this skill when the user asks: - "make the agent improve itself" - "capture learnings" - "log mistakes so we do not repeat them" - "record blockers / corrections / feature gaps" - "build a self-improving OpenClaw workflow" - "operationalize lessons learned" - "test whether this new rule actually helps" - "run an eval loop on this workflow/skill/SOP" - "should we keep this new guardrail or discard it" - "why did the agents fail today" - "why is daily marketing not closing automatically" - "classify OpenClaw harness failures" - "generate agent delivery scorecard" ## Files this skill uses - `.learnings/LEARNINGS.md` - `.learnings/ERRORS.md` - `.learnings/FEATURE_REQUESTS.md` - `.learnings/EXPERIMENTS.md` - `memory/harness-backlog-latest.md` - `mission-control/data/delivery-receipts/agent-scorecard-YYYY-MM-DD.md` - Optional export under `.learnings/exports/obsidian/` by default, or `OBSIDIAN_LEARNINGS_DIR` if explicitly configured ## Safety boundaries - Local-file workflow only, no network I/O - Promotion can append to `AGENTS.md`, `TOOLS.md`, or `SOUL.md` - Always review promotion targets first, or run `scripts/promote-learning.mjs ... --dry-run` - `OBSIDIAN_LEARNINGS_DIR` should only point at a path you intend to modify ## Command examples ```bash node {baseDir}/scripts/log-learning.mjs learning "Summary" "Details" "Suggested action" node {baseDir}/scripts/log-learning.mjs error "Summary" "Error details" "Suggested fix" node {baseDir}/scripts/log-learning.mjs feature "Capability name" "User context" "Suggested implementation" node {baseDir}/scripts/log-learning.mjs experiment "Target problem" "Baseline failure" "Single mutation to test" node {baseDir}/scripts/log-experiment.mjs "Target problem" "Baseline failure" "Single mutation" "eval1|eval2|eval3" "Result summary" "testing" node {baseDir}/scripts/promote-learning.mjs workflow "Rule text" node {baseDir}/scripts/analyze-openclaw-failures.mjs --output /Users/m1/.openclaw/workspace/memory/harness-backlog-latest.md node {baseDir}/scripts/daily-agent-scorecard.mjs --output /Users/m1/.openclaw/workspace/mission-control/data/delivery-receipts/agent-scorecard-$(date +%F).md node {baseDir}/scripts/daily-agent-scorecard.mjs --repair --output /Users/m1/.openclaw/workspace/mission-control/data/delivery-receipts/agent-scorecard-$(date +%F).md ``` ## Categories ### learning Use for: - user corrections - better recurring workflows - tool gotchas - operational lessons ### error Use for: - command failures - integration failures - runtime blockers - broken release / deploy behavior ### feature Use for: - missing capability requests - operator workflow gaps - recurring requests that deserve a build item ### experiment Use for: - repeated failures that need a tested guardrail - checklist/SOP/schema changes that should be validated before broad promotion - keep/discard decisions on new operating rules - binary eval loops for skills, workflows, receipts, summaries, or deploy closeout rules ### harness Use for: - gateway, channel, provider, tool, session, or platform failures - repeated "agent did not respond / did not finish / forgot identity" incidents - daily workflow failures where Mission Control says one thing but proof chains say another - scorecards that compare agent delivery against real receipts, URLs, and closeout evidence Default failure taxonomy: - `NetworkPolicyBlocked` - provider/tool blocked by local or external network policy - `GatewayUnavailable` - gateway process, port, websocket, or reachability failure - `SessionContextRot` - stale session, stale skill snapshot, identity drift, or outdated config context - `SkillMissing` - expected skill absent from installed path or session snapshot - `ToolInvalidArguments` - malformed tool/edit call or bad argument shape - `ProviderError` - provider/model/API failure not caused by network policy - `ExternalPlatformBlocked` - X/LinkedIn/Facebook/Feishu/etc. platform/API/login/visibility blocker - `HumanApprovalRequired` - real approval boundary for external, destructive, production, money, or ambiguous action Harness workflow: 1. Scan logs and receipts with `scripts/analyze-openclaw-failures.mjs`. 2. Generate same-day agent scorecard with `scripts/daily-agent-scorecard.mjs`. 3. Run `scripts/daily-agent-scorecard.mjs --repair` to create/update recovery tickets for failed, blocked, or pending lanes. 4. Convert repeated classes into an `error`, `experiment`, or promoted rule. 5. Do not call a workflow closed until the scorecard has proof links or explicit blocker evidence. Repair loop rules: - Every failed/blocked/pending lane should have a `failureClass`, `repairState`, `nextAction`, `repeatCount7d`, and evidence. - `ProofMissing`, `UpstreamMissing`, and `HumanApprovalRequired` must not be blindly retried. - Repeated `agent + lane + failureClass` failures within 7 days should become `EXPERIMENT_REQUIRED`. - Recovery tickets should be written under [REDACTED_SECRET]. ## Promotion targets - `AGENTS.md` → workflow / delegation / execution rules - `TOOLS.md` → tool gotchas, secrets locations, environment routing rules - `SOUL.md` → behavior / communication / non-negotiable principles - Obsidian vault → reusable operator log and content proof asset ## Karen / Mission Control compatibility This skill is designed to work with stricter ops governance: - Karen can reference learnings when repeated failures happen - Mission Control can treat promoted learnings as new operating rules - recurring blockers can be elevated from chat into tracked operational knowledge - experiments can test whether a new summary contract, receipt rule, or deploy closeout guardrail actually reduced the failure pattern ## Eval loop rule When a repeated failure is turning into a new rule/SOP/checklist, do not only log it. Also: 1. define 3-5 binary evals 2. record the baseline failure state 3. change one thing at a time 4. re-check the same evals 5. classify the change as keep / discard / partial_keep Use `{baseDir}/references/eval-loop.md` for the experiment format and examples. ## Output goal A good use of this skill should produce one of: - a durable learning entry - a durable error entry - a durable feature request entry - a durable experiment entry with binary evals - a promoted rule in AGENTS.md / TOOLS.md / SOUL.md - an Obsidian vault operations note ## Important limits - Logging is not the same as fixing. - Do not treat a learning entry as closure for a broken deliverable. - Use this skill to reduce repeated mistakes, not to excuse them. ## References - `{baseDir}/references/schema.md` - `{baseDir}/references/promotion-guide.md` - `{baseDir}/references/eval-loop.md` - `{baseDir}/references/examples.md` - `{baseDir}/references/decision-rules.md` - `{baseDir}/references/eval-loop.md` - `{baseDir}/references/examples.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)