Drift spec-to-code anchor conventions. Use when editing code that is bound by drift specs, updating specs, working with drift frontmatter, or when drift check reports stale anchors.
Persistent spec management for AI coding workflows. Use this skill when the user explicitly mentions specs, forging, or structured planning: says "forge", "forge a spec", "write a spec for X", "create a spec", "plan X as a spec", "resume", "what was I working on", "spec list/status/pause/switch/activate", "implement the spec", "implement phase N", "implement all phases", "generate openapi", or exits plan mode (offer to save as a spec). Also trigger when a `.specs/` directory exists at session start. Do NOT trigger on general feature requests, coding tasks, or questions that don't mention specs or forging — those are normal coding tasks, not spec management. --- # Spec Mint Core Turn ephemeral plans into structured, persistent specs built through deep research and iterative interviews. Specs have phases, tasks, acceptance criteria, a registry, resume context, a decision log, and a deviations log. They live in `.specs/` at the project root and work with any AI coding tool that can read markdown. Whether `.specs/` is committed is repository policy. Respect `.gitignore` and the user's preference for tracked vs local-only spec state. ## Critical Invariants 1. **Single-file policy**: Keep this workflow in one `SKILL.md` file. 2. **Canonical paths**: - Registry: `.specs/registry.md` - Per-spec files: `.specs/<id>/SPEC.md`, `.specs/<id>/research-*.md`, `.specs/<id>/interview-*.md` 3. **Authority rule**: `SPEC.md` frontmatter is authoritative. Registry is a denormalized index for quick lookup. 4. **Active-spec rule**: Target exactly one active spec at a time. 5. **Parser policy**: Use best-effort parsing with clear warnings and repair guidance instead of hard failure on malformed rows. 6. **Progress tracking is sacred**: After completing any task, immediately update SPEC.md (checkbox, `← current` marker, phase marker) AND registry.md (progress count, date). Then re-read both files to verify the edits landed correctly. Never move to the next task without updating both files.
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:
