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BF self-improvement

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks. For CI-only/headless learning capture, use self-improvement-ci.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 4 089 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Captures learnings, errors, and corrections to enable continuous improvement.

As a process F 34/100 · Will not run — References files that are not bundled: scripts/activator.sh, scripts/error-detector.sh, references/hooks-setup.md

IntegrationGitHubAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
F
34/100
Will not run
References files that are not bundled: scripts/activator.sh, scripts/error-detector.sh, references/hooks-setup.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/activator.sh
  • warning missing-ref reference to a missing file: scripts/error-detector.sh
  • warning missing-ref reference to a missing file: references/hooks-setup.md
  • warning missing-ref reference to a missing file: assets/SKILL-TEMPLATE.md
  • warning missing-ref reference to a missing file: references/openclaw-integration.md

Process rating: all ten parameters 34/100

Will not run. References files that are not bundled: scripts/activator.sh, scripts/error-detector.sh, references/hooks-setup.md
  • 0Tools and files. 5 referenced file(s) missing: scripts/activator.sh, scripts/error-detector.sh, references/hooks-setup.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (self-improvement) differs from the folder (self-improvement-2)
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 4089 tokens
  • 85Steps. 106 steps, 1 vague phrases
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low 18 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 509: enough signal without eating the budget
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 106 items
  • +4Has examples (17 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.