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skill-creator

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch for Claude Code or Cursor, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

70%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 highly actionable, well-sequenced workflow document with concrete commands, templates, and validation checkpoints — the core loop is easy to follow end to end. Its weaknesses are verbosity from chatty asides and a verbatim repeated loop, plus broken bundle references (missing agents/ and eval-viewer/ files) that undermine navigation and executability.

Suggestions

Fix or ship the referenced bundle files — agents/grader.md, agents/comparator.md, agents/analyzer.md, and eval-viewer/generate_review.py are cited as instructions to read but are absent, breaking navigation and the grading/viewer steps.

Trim the conversational padding (e.g., "Cool? Cool.", "Good luck!", the user-familiarity anecdotes, the closing verbatim repeat of the core loop) to reclaim tokens — the repeated loop section in particular duplicates the opening overview.

Remove or write the missing "API Credentials" section that Description Optimization Step 3 tells the reader to follow, since the dangling reference leaves a key step unexecutable.

Move the detailed Description Optimization workflow into a references/ file to bring the body comfortably under the 500-line guidance.

DimensionReasoningScore

Conciseness

The body is mostly dense, actionable instruction (exact commands, JSON templates, field names), but it carries unnecessary padding: chatty asides ("Cool? Cool.", "Good luck!"), anecdotal material (the plumbers/grandparents passage, "we are trying to create billions a year in economic value here!"), and a verbatim re-statement of the core loop at the end. Fits the 3 anchor (mostly efficient, could be tightened); not 2 because the bulk is genuinely instructive rather than explanations of concepts Claude already knows.

3 / 5

Actionability

Guidance is largely copy-paste ready — the subagent run prompt template, `python -m scripts.aggregate_benchmark`, the nohup viewer command, and exact grading.json field names ("text", "passed", "evidence") — with concrete good/bad examples for eval queries. Not 5: there are real gaps — the "API Credentials section above" referenced in Step 3 of Description Optimization does not exist in the document, and key instructions point to files absent from the bundle (agents/grader.md, eval-viewer/generate_review.py).

4 / 5

Workflow Clarity

The multi-step process is explicitly sequenced (Step 1: spawn all runs, Step 2: draft assertions, Step 3: capture timing, Step 4: grade/aggregate/analyze/view, Step 5: read feedback) with explicit validation gates ("Do NOT generate the viewer or benchmark until grading.json exists for every run"; timing must be captured immediately from each notification), feedback loops (iterate on feedback until the user is happy), and a checklist-style recap. Matches the top anchor.

5 / 5

Progressive Disclosure

Sections are well organized and there is a clear "Reference files" map pointing to references/schemas.md (which exists and covers the cited schemas), but a substantial fraction of referenced paths are broken in the actual bundle: agents/grader.md, agents/comparator.md, agents/analyzer.md, and eval-viewer/generate_review.py are all cited as places to read and none exist. Scoring against the actual bundle structure, references are clearly signaled but partially broken and the 483-line body inlines content (Description Optimization details) that could live in references — fits the 3 anchor better than 4's "minor organization gaps".

3 / 5

Total

15

/

20

Passed

Description

92%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: third-person, concrete, and comprehensive on both capabilities and trigger conditions, covering creation, improvement, benchmarking, and description optimization. The only weakness is modest synonym coverage (no mention of SKILL.md or casual phrasings like "make a skill"), which leaves a small chance of missing natural user phrasings.

Suggestions

Add natural synonyms such as "SKILL.md" or casual phrasings like "make/build a skill" to the trigger clause to broaden keyword coverage.

Consider mentioning "improve triggering accuracy" alternatives like "make the skill activate more reliably" for users who don't know the term 'triggering'.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions — "Create new skills, modify and improve existing skills, and measure skill performance", "run evals", "benchmark skill performance with variance analysis", "optimize a skill's description for better triggering accuracy" — with comprehensive coverage of the skill's capabilities. Not 4: there are no notable gaps in the action coverage.

5 / 5

Completeness

Both questions are answered explicitly and concretely: the what ("Create new skills, modify and improve existing skills, and measure skill performance") and the when ("Use when users want to create a skill from scratch... update or optimize an existing skill, run evals... benchmark... or optimize a skill's description"). Matches the top anchor exactly.

5 / 5

Trigger Term Quality

Good natural-term coverage: "create a skill from scratch", "update or optimize an existing skill", "run evals", "benchmark skill performance" — phrases users would actually say. Not 5: some natural synonyms are missing (e.g., "SKILL.md", "agent skills", "make a skill"); not 3 because common variations of the core intents are all present.

4 / 5

Distinctiveness Conflict Risk

Clear niche (skill authoring, evaluation, and description optimization) with every trigger scoped to skills, giving minimal overlap risk with unrelated skills. The potential overlap with generic eval/benchmark tooling is explicitly scoped to testing "a skill".

5 / 5

Total

19

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

cognitedata/builder-skills

Path

skills/skill-creator/SKILL.md

Reviewed

Table of Contents

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