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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User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task.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.

60

Quality

71%

Does it follow best practices?

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SecuritybySnyk

Critical

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tessl review fix ./configs/microservice/bff-service/configs/agent-skills/clawhub/self-improvement/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is exceptionally actionable — every operation has copy-paste commands, templates, and worked examples — but it pays for that with heavy redundancy and weak file splitting. Consolidating the three setup paths, deduplicating the promotion tables, and moving platform-specific detail into the existing references files would cut the token cost substantially without losing guidance.

Suggestions

Merge First-Use Initialisation, OpenClaw Setup, and Generic Setup into one setup section (or move OpenClaw detail into references/openclaw-integration.md) and delete one of the two promotion-target tables to remove structural duplication.

Move the three full entry templates (Learning/Error/Feature Request) into a single reference file (e.g. references/entry-templates.md) and keep only one compact example inline, reducing SKILL.md to a true overview.

Reference references/examples.md from the body (it exists but is never linked), and drop the Multi-Agent Support and Gitignore Options detail into reference files with a one-line pointer.

DimensionReasoningScore

Conciseness

The ~640-line body is noticeably verbose through structural duplication: three separate setup sections (First-Use Initialisation, OpenClaw Setup, Generic Setup), two promotion-target tables, and a Quick Reference table that restates the Detection Triggers section. This goes beyond "some unnecessary explanation" (anchor 3) — whole sections are padded repeats — though it never degenerates into explaining concepts Claude already knows at length.

2 / 5

Actionability

Guidance is fully executable throughout: guarded init commands ("[ -f .learnings/LEARNINGS.md ] || printf ..."), complete copy-paste markdown entry templates, exact ID format with examples, concrete grep commands for review, working hook JSON snippets, and referenced scripts that all exist in the bundle. The verbose-to-concise promotion examples show exactly what output should look like.

5 / 5

Workflow Clarity

The log → resolve → promote lifecycle is clearly sequenced, and the recurring-pattern workflow has real checkpoints: search-first grep dedup, an explicit promotion gate ("Recurrence-Count >= 3", 2 distinct tasks, 30-day window), and a verify step ("Read skill in fresh session"). Minor gaps — no verification that initialisation succeeded, and sequences are scattered across the duplicated setup sections — keep it below a 5; no destructive/batch cap applies since the skill only appends to log files.

4 / 5

Progressive Disclosure

References are one level deep and clearly signaled ("See references/hooks-setup.md", "See references/openclaw-integration.md", assets/SKILL-TEMPLATE.md), but large bodies of content that clearly belong in separate files are inlined — three full entry templates, the OpenClaw workspace structure, Multi-Agent Support, and Gitignore Options — bloating SKILL.md well past overview size. Additionally, references/examples.md exists in the bundle but is never referenced from the body, so navigation to it is missing.

3 / 5

Total

14

/

20

Passed

Description

78%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description with explicit, natural trigger conditions and a clear what/when split in third-person voice. Its main limitation is that the capability statement stays at a summary level rather than naming the concrete mechanisms the skill actually performs.

Suggestions

Add one or two concrete actions to the capability sentence, e.g. "Logs failures, corrections, and feature requests to markdown files in .learnings/ and promotes recurring learnings to project memory", to lift specificity.

Broaden trigger phrasing with common synonyms such as "mistake", "lesson learned", or "keep making the same error" to improve natural-term coverage.

DimensionReasoningScore

Specificity

"Captures learnings, errors, and corrections" names the domain and a couple of actions, but the what stays abstract — no concrete mechanics (log files, entry formats, promotion) appear. It is not a 4 because no list of several specific actions is given, and not a 2 because the domain is named with more than generic phrasing.

3 / 5

Completeness

It explicitly answers what ("Captures learnings, errors, and corrections to enable continuous improvement") and when, via a six-item numbered "Use when" list with concrete quoted trigger phrases plus "Also review learnings before major tasks". This matches the top anchor exactly; nothing is merely implied.

5 / 5

Trigger Term Quality

Triggers include natural user phrases such as "'No, that's wrong...'", "'Actually...'", "A command or operation fails unexpectedly", and "User requests a capability that doesn't exist". Missing common synonyms like "mistake", "lesson learned", or "retrospective", so it falls just short of comprehensive coverage.

4 / 5

Distinctiveness Conflict Risk

The self-improvement logging niche is clear and its triggers (user correction, unexpected failure, missing capability) are distinct from routine coding tasks. Minor overlap risk remains with memory-management or feedback-capture skills, keeping it below a 5.

4 / 5

Total

16

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (645 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository
UnicomAI/wanwu
Reviewed

Table of Contents

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