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llm-wiki

Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.

74

Quality

93%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

A well-engineered skill body: executable quick start, verified bundle files, one-level-deep clearly labeled references, and sensible safety constraints (immutable raw/, approval gate, lint). The only deductions are minor redundancy in the RAG comparison and the absence of an inline validation checkpoint in the batch-ingest summary.

Suggestions

Collapse the 'Why this works (vs plain RAG)' table into the 'Core principle' section (or vice versa) — the RAG-vs-compounding contrast is stated twice and could be trimmed to one.

Add one inline validation step to the ingest operation summary (e.g., 'after batch page updates, run python scripts/lint_wiki.py to catch broken links/orphans') so the batch-write flow has a visible feedback loop without requiring references/ingest-workflow.md.

DimensionReasoningScore

Conciseness

The body is largely efficient — dense tables, terse bullets, no tutorials on concepts Claude already knows — but the RAG-vs-compounding contrast is stated twice ('Core principle' prose and the 'Why this works (vs plain RAG)' table), and the 'Related skills' section is partly promotional. This is 'efficient with minor instances that could be trimmed' (4) rather than 'every token earns its place' (5).

4 / 5

Actionability

The quick start gives copy-paste-ready commands ('python scripts/init_vault.py --path ~/vaults/research --topic "LLM interpretability"', slash-command invocations), the scripts table lists eight concrete tools each with a stated purpose and a '--help' entry point, and every referenced script and command was verified to exist. Specific examples cover the common init/ingest/query/lint/log cases, matching the 5 anchor.

5 / 5

Workflow Clarity

The three core operations are each given a numbered, sequenced summary with detail delegated to reference files, and checkpoints exist — the wiki-ingestor 'applies after your approval', lint is an explicit periodic health check, and the iron rule guards writes. It falls short of 5 because the ingest summary (a batch operation touching '10-15 relevant pages') has no inline validation/feedback step; that detail lives only in references/ingest-workflow.md.

4 / 5

Progressive Disclosure

The body is a genuine overview: all 8 referenced files in references/ exist with substantive content (76-194 lines each), all 8 scripts exist, and the assets/templates are present. References are one level deep, each clearly signaled with a one-line description in the 'Reference docs' section, making navigation easy — matching the 5 anchor.

5 / 5

Total

18

/

20

Passed

Description

100%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.

This is an exemplary description: concrete third-person action statements, an explicit 'Use when' clause with a rich set of natural trigger phrases and synonyms, and self-contrasting against RAG to minimize mis-triggering. Every dimension lands at the top anchor without padding or over-claiming.

DimensionReasoningScore

Specificity

The description lists four concrete, distinct actions — 'incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current' — giving comprehensive coverage of the skill's capabilities within its domain. It clearly matches the 5 anchor rather than 4 because there are no meaningful gaps in the action coverage.

5 / 5

Completeness

It explicitly answers 'what' (ingests sources, updates pages, maintains cross-references, keeps synthesis current) and 'when' (explicit 'Use when...' clause followed by concrete trigger phrases). The structure mirrors the 5 anchor example; it is well above 4, where the 'when' would be less explicit.

5 / 5

Trigger Term Quality

It quotes seven natural trigger phrases — 'second brain', 'Obsidian wiki', 'personal knowledge management', 'ingest this paper/article/book', 'build a research wiki', 'compound knowledge', 'Memex' — covering synonyms a user would naturally say, plus a behavioral condition ('knowledge to accumulate across sessions instead of being re-derived by RAG'). This is comprehensive coverage including synonyms, matching the 5 anchor.

5 / 5

Distinctiveness Conflict Risk

It carves out a clear niche — a maintained, compounding Obsidian wiki — and even contrasts itself with RAG ('instead of being re-derived by RAG on every query'), avoiding generic note-taking terms that would overlap with simpler Obsidian skills. Distinct triggers and minimal conflict risk match the 5 anchor.

5 / 5

Total

20

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository

alirezarezvani/claude-skills

Path

engineering/llm-wiki/skills/llm-wiki/SKILL.md

Reviewed

Table of Contents

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