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understand-knowledge

Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.

Quality

77%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./understand-anything-plugin/skills/understand-knowledge/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

75%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 an efficient, well-sequenced five-phase workflow with concrete commands, explicit validation, and safe guards around destructive operations. Remaining gaps are minor: a missing subagent prompt template, no merge-script failure handling, and referenced bundle scripts that are not verifiable in the provided bundle.

Suggestions

Include a concrete prompt template for the article-analyzer subagent dispatch in Phase 3 so the step is copy-paste executable rather than descriptive.

Add failure handling for the Phase 4 merge script (e.g., what to report and whether to fall back to the scan-manifest graph if the merge errors out).

Ensure the referenced bundled scripts actually ship with the skill (no scripts/ directory is present) and consider a brief inline note on what each script expects, since the Karpathy pattern reference is only an external URL.

DimensionReasoningScore

Conciseness

The body is lean and phase-structured with no padding of concepts Claude already knows (the Karpathy pattern description is genuinely niche), but the $UA_DIR resolution logic is spelled out twice (Phase 1 step 1 and the Phase 5 cleanup snippet) and the "Phase 2: SCAN (already done)" section is a stub — minor trimmable content matching anchor 4.

4 / 5

Actionability

Concrete commands dominate: the two python3 script invocations, a full meta.json template, a guarded bash cleanup snippet, and explicit validation criteria ("Every edge source/target must reference an existing node"). The main gap is the article-analyzer subagent dispatch, which lists inputs but provides no concrete prompt template — anchor 4's "mostly executable with minor gaps".

4 / 5

Workflow Clarity

A clear five-phase sequence with explicit checkpoints: script-error handling in Phase 1, batch-failure degradation in Phase 3, and structural validation plus a guarded rm -rf in Phase 5 (so the destructive-op cap of 3 does not apply). Minor gaps hold it at 4: Phase 4 has no failure handling for the merge script, and validation auto-removes dangling edges rather than offering a fix-and-retry loop.

4 / 5

Progressive Disclosure

A single-file skill with well-organized phase sections and clearly signaled script references, but the two referenced bundled scripts (parse-knowledge-base.py, merge-knowledge-graph.py) are absent from the bundle listing and the Karpathy pattern is cited only via an external URL rather than a bundled reference — good structure with minor organization gaps, matching anchor 4.

4 / 5

Total

16

/

20

Passed

Description

70%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 and distinctive, with concrete actions and niche trigger terms, but it entirely lacks a 'when to use' clause, which caps its completeness and leaves trigger guidance implicit.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user asks to understand, visualize, or build a knowledge graph from a wiki-style knowledge base with wikilinks and index.md".

Include common natural variations of trigger terms such as "wikilinks", "index.md", "wiki markdown", or "knowledge base" to broaden keyword coverage.

Mention the primary outputs (saved knowledge-graph.json and dashboard) so the 'what' coverage is comprehensive.

DimensionReasoningScore

Specificity

"generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering" lists several concrete actions, but coverage has minor gaps (no mention of detection, saving the graph, or the dashboard output), matching anchor 4 rather than the comprehensive anchor 5.

4 / 5

Completeness

The 'what' is clear (analyze a wiki knowledge base, generate a knowledge graph with listed features), but there is no "Use when..." clause or equivalent explicit trigger guidance, which per the judging guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

Terms like "Karpathy-pattern LLM wiki", "knowledge base", "knowledge graph", and "topic clustering" are natural phrasings, but common variations such as "wikilinks", "index.md", or ".md files" are missing, so it falls short of anchor 5's comprehensive synonym/extension coverage.

4 / 5

Distinctiveness Conflict Risk

"Karpathy-pattern LLM wiki knowledge base" is a highly niche, well-scoped trigger with distinct keywords, giving it a clear niche with minimal conflict risk — a direct match for anchor 5.

5 / 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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

Egonex-AI/Understand-Anything

Path

understand-anything-plugin/skills/understand-knowledge/SKILL.md

Reviewed

Table of Contents

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