Add proper Tracey spec annotations to code, find requirements, and check coverage. Use when working with projects that have Tracey configuration (.config/tracey/config.styx), when adding spec references to code, or when checking requirement coverage.
Use when defining ABM tiers, scoring logic, and coverage rules.
Code coverage analysis tools. These tools help analyze and visualize code coverage for test execution, upload coverage data to Neo4j, and display coverage statistics. Available tools: run-coverage, show-coverage.
Analyze test coverage data from tobari.toon and help improve test coverage incrementally. Use when the user has run go test with tobari enabled (e.g., `GOFLAGS="$(tobari flags)" go test ./...`) and wants to improve test coverage or increase code coverage percentage. Triggers on phrases like "improve coverage", "increase coverage", "coverage improvement", or "add more tests".
Map requirements (PRD/user stories/AC) to comprehensive test coverage using a traceability matrix (RTM). Outputs coverage gaps, risks, test levels, prioritization, automation candidates, and change-impact notes. Designed for QA/Test Architect workflows.
Analyze test coverage, identify gaps, and create a testing strategy. Does NOT write tests.
Write missing unit tests for a makeup-js module based on coverage gaps
Test coverage heatmap from lcov or JSON coverage data. Finds coverage reports, parses line coverage per file, and renders a color-coded file-tree heatmap as HTML. Opens in the system browser.
Comprehensive testing strategy using engram to track test execution, results, and coverage.
Reviews existing test suites for coverage gaps, quality, and rigor; identifies weak or missing tests and guides fixes to reach a target coverage threshold. Use when asked to "review unit tests", "fix unit tests", "check test coverage", "audit tests", "improve test suite", or ensure coverage at end of an initiative or during maintenance. Do NOT use when the user asks to write tests from scratch for new, untested code.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills:
