wiki-query

Query the LLM Wiki — reads index.md first, drills into 3-10 relevant pages, synthesizes an answer with inline [[wikilink]] citations, and offers to file the answer back as a new comparison or synthesis page. Usage /wiki-query "<question>"

Quality

66%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.gemini/skills/wiki-query/SKILL.md

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 a lean, well-structured command overview with a clear sequenced workflow, concrete script references, and appropriately signaled one-level-deep pointers, scoring consistently across all dimensions.

Dimension	Reasoning	Score
Conciseness	The ~55-line body is lean and well-organized with tables and numbered steps, avoiding explanations of concepts Claude already knows; only minor redundancy between the intro and the frontmatter description.	4 / 5
Actionability	Provides concrete guidance — named scripts (wiki_search.py, append_log.py), an output-format table mapped to question shapes, and a sub-agent dispatch reference — with only minor gaps in executable detail.	4 / 5
Workflow Clarity	A clear six-step sequence runs from index read through synthesize to offer-to-file-back, with a BM25 fallback search as a checkpoint and user-gated filing; minor validation gaps but the task is non-destructive.	4 / 5
Progressive Disclosure	Good section structure with clearly signaled one-level-deep references (sub-agent, scripts, references/query-workflow.md); no bundle files exist in this skill directory, and references point to repo-root-relative paths rather than an in-bundle structure.	4 / 5
	Total	16 / 20 Passed

Description

58%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 clearly conveys what the skill does through several concrete actions and is well-scoped to a distinct niche, but it lacks an explicit 'Use when...' trigger clause and has only moderate natural-keyword coverage.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user asks what the wiki says about a topic, wants to compare sources, or needs a cited synthesis from the LLM Wiki.'

Broaden natural trigger terms with synonyms users might actually say (e.g. 'ask the wiki', 'look up in the wiki', 'compare my sources').

Dimension	Reasoning	Score
Specificity	Lists several concrete actions — 'reads index.md first, drills into 3-10 relevant pages, synthesizes an answer with inline [[wikilink]] citations, and offers to file the answer back' — with only minor gaps in coverage of output formats.	4 / 5
Completeness	The 'what' is clearly stated, but there is no explicit 'Use when...' trigger clause — only a 'Usage /wiki-query' invocation — so per the rubric guideline completeness is capped at 3.	3 / 5
Trigger Term Quality	Relevant terms like 'wiki', 'LLM Wiki', 'query', and 'question' appear, but common synonyms and natural phrasing variations users might say are missing.	3 / 5
Distinctiveness Conflict Risk	Tied to a distinct '/wiki-query' command and the specific LLM Wiki niche, with only minor overlap risk against general search or query skills.	4 / 5
	Total	14 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: alirezarezvani/claude-skills
Path: .gemini/skills/wiki-query/SKILL.md
Commit: 19392f7

Reviewed: ~14 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.