skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, 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.
85
81%
Does it follow best practices?
Impact
88%
1.87xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Quality
Discovery
100%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 a strong, well-crafted description that clearly articulates both what the skill does and when it should be triggered. It lists multiple concrete actions, uses natural trigger terms, and occupies a distinct meta-skill niche that minimizes conflict risk with other skills. The explicit 'Use when...' clause with varied trigger scenarios is particularly effective.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Create new skills', 'modify and improve existing skills', 'measure skill performance', 'run evals', 'benchmark skill performance with variance analysis', 'optimize a skill's description for better triggering accuracy'. | 3 / 3 |
Completeness | Clearly answers both 'what' (create, modify, improve, measure skills) and 'when' with an explicit 'Use when...' clause listing specific trigger scenarios like creating from scratch, editing, running evals, benchmarking, and optimizing descriptions. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'create a skill', 'edit', 'optimize', 'evals', 'benchmark', 'skill performance', 'triggering accuracy', 'description'. These cover a good range of terms a user working with skills would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | The description targets a very specific meta-domain — skill creation, editing, evaluation, and optimization — which is a clear niche unlikely to conflict with other skills that perform domain-specific tasks like PDF processing or data analysis. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
62%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a highly actionable and well-structured skill with excellent workflow clarity and concrete executable guidance throughout. Its primary weakness is extreme verbosity — conversational asides, repeated summaries of the core loop, explanations of obvious concepts, and inline content that should be in reference files significantly bloat the token budget. The skill would benefit greatly from aggressive trimming and moving environment-specific sections to separate reference files.
Suggestions
Cut conversational filler ('Cool? Cool.', 'This task is pretty important...billions a year in economic value', 'Sorry in advance but I'm gonna go all caps here') and reduce the 3 repetitions of the core loop to one authoritative version at the top.
Move environment-specific sections (Claude.ai-specific, Cowork-specific) into separate reference files (e.g., references/claude-ai.md, references/cowork.md) and reference them with one-line pointers from SKILL.md.
Remove the 'Communicating with the user' section — Claude already understands audience adaptation — or reduce it to 2-3 bullet points of skill-specific terminology guidance.
Trim the description optimization section significantly; the detailed explanation of how triggering works and what makes good eval queries could move to a reference file, keeping only the 4-step process in the main body.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~500+ lines with significant conversational padding ('Cool? Cool.'), extensive explanations of concepts Claude already understands (what skills are, how to communicate with users, what PDF is), and repeated instructions (the core loop is stated 3 times). Much of the content could be cut by 40-50% without losing actionable information. | 1 / 3 |
Actionability | Despite verbosity, the skill provides highly concrete, executable guidance: specific CLI commands, exact JSON schemas, file path conventions, step-by-step processes with real code blocks, and precise instructions for tools like generate_review.py, aggregate_benchmark, and package_skill. The guidance is copy-paste ready throughout. | 3 / 3 |
Workflow Clarity | The multi-step workflow is clearly sequenced with explicit phases (Capture Intent → Interview → Write → Test → Evaluate → Iterate), validation checkpoints (grading assertions, user review via viewer, feedback loops), error recovery patterns (iterate until satisfied), and parallel execution guidance. The 5-step evaluation sequence is particularly well-structured with clear dependencies between steps. | 3 / 3 |
Progressive Disclosure | The skill references external files appropriately (agents/grader.md, agents/comparator.md, agents/analyzer.md, references/schemas.md, assets/eval_review.html) with clear guidance on when to read them. However, the SKILL.md body itself is monolithic and contains substantial content that could be split into reference files — the description optimization section, Claude.ai-specific instructions, and Cowork-specific instructions could each be separate files to keep the main body leaner. | 2 / 3 |
Total | 9 / 12 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 — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- netlify/context-and-tools
- Commit
cffaf74
- Reviewed
Table of Contents
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.