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llm-wiki

Build and maintain a persistent knowledge base (LLM Wiki) using native backend tools. Supports ingesting PDFs, Markdown, and TXT files, and querying or linting the data. Use this whenever the user wants to add, retrieve, or manage their own local knowledge base and documents.

64

Quality

76%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./jiuwenclaw/jiuwenclaw/resources/agent/jiuwenclaw_workspace/skills/llm-wiki/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

68%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 skill body is lean, actionable, and well organized, naming the native tools and concrete follow-up edits. Its main weakness is the absence of validation/verification checkpoints in the batch ingest and destructive lint-repair workflows.

Suggestions

Add an explicit verification step after ingestion (e.g. re-open index.md/log.md to confirm the entry was written and links resolve) before declaring success.

Add a validate-then-retry feedback loop to the lint workflow: after editing .md files to repair paths, re-run wiki_lint to confirm no broken links or orphaned pages remain.

Specify a quick post-ingest sanity check (e.g. confirm dedup manifest was updated and new topics appear in index.md) to make the batch operation safer.

DimensionReasoningScore

Conciseness

The body is mostly efficient with well-organized sections and no teaching of basics Claude already knows; a few explanatory phrases (e.g. deduplication rationale) could be trimmed.

4 / 5

Actionability

Names exact tools (wiki_ingest, wiki_query, wiki_lint) and concrete follow-up file edits with specific paths, giving mostly executable guidance with only minor gaps.

4 / 5

Workflow Clarity

Operations are numbered and sequenced, but batch directory ingest and the lint-then-repair loop are destructive/batch operations with no explicit validation or verification checkpoints, which caps workflow clarity at 3 per the rubric.

3 / 5

Progressive Disclosure

Content is well structured into clearly labeled sections in a single file with no nested references, but there is no external reference layer to demonstrate one-level-deep navigation.

4 / 5

Total

15

/

20

Passed

Description

83%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.

The description is specific, complete, and carries an explicit use-when trigger clause with concrete capability verbs and file types. It is slightly short of comprehensive synonym/extension coverage and has minor overlap risk, but is otherwise strong.

DimensionReasoningScore

Specificity

Lists several concrete actions (ingesting PDFs/Markdown/TXT, querying, linting) with named file types, but stops short of fully enumerating every capability, leaving minor gaps.

4 / 5

Completeness

Clearly states what it does (build/maintain a wiki, ingest, query, lint) and provides an explicit 'Use this whenever...' trigger clause covering add/retrieve/manage.

5 / 5

Trigger Term Quality

Includes natural terms users would say ("knowledge base", "ingest", "querying", "linting", "add, retrieve, or manage") but is missing some common synonyms and extensions.

4 / 5

Distinctiveness Conflict Risk

The 'local knowledge base and documents' niche with native backend tools is mostly distinct, with only minor overlap risk against generic document skills.

4 / 5

Total

17

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
XiaoLuoLYG/GOD
Reviewed

Table of Contents

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