Expertise in evaluating AWS accounts for compliance — what checks are meaningful, which SCF controls they map to, and how to interpret aws CLI output.
Set up an AI-driven changelog system in any project. Creates CHANGELOG.md with an [Unreleased] section for AI agents, a Python version/changelog stamping script (CalVer or SemVer), and build/CI integration. Use when the user wants to add automated changelog management, AI-friendly changelog workflows, version stamping, or set up a changelog system for a new or existing project. Also use when asked to improve an existing changelog process.
Generate a branded HTML impact report showing what GitHub Copilot helped you accomplish — goals delivered, human effort equivalent, skills augmented, collaboration patterns, and estimation evidence grounded in peer-reviewed research. Works with any Copilot CLI or VS Code agent session.
Guide for writing MoonBit bindings to C libraries using native FFI. Use when adding extern "c" declarations, writing C stubs with moonbit.h, configuring native-stub and link.native in moon.pkg or moon.pkg.json, choosing #borrow/#owned ownership annotations, designing callback trampolines, wrapping C pointers with external objects and finalizers, converting strings across FFI, or validating bindings with AddressSanitizer.
Use when the user wants to fix a Sentry issue, auto-repair a bug from Sentry, or create a fix PR for a Sentry error. Triggers on "修复 sentry", "fix sentry issue", "sentry 修复", "sentry fix".
View and set configuration. Use when the user asks to change trigger behavior, extension settings, or other settings.
Inspect external prediction model implementations and adapt them to EasyTSF task contracts. Use when the user provides model code, class definitions, forward logic, or config fragments and wants Codex to classify the target task as `sequence_prediction`, `graph_prediction`, or `grid_prediction`, determine the current repository fit, and produce either a direct adaptation plan or a repository extension plan.
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Create modern Angular standalone components following v20+ best practices. Use for building UI components with signal-based inputs/outputs, OnPush change detection, host bindings, content projection, and lifecycle hooks. Triggers on component creation, refactoring class-based inputs to signals, adding host bindings, or implementing accessible interactive components.
Research industry standards and best practices, identify viable approaches for a given technical or architectural problem, and produce a structured factual comparison against project-specific constraints. Reports options — does not decide.
Expert guidance for FedRAMP certification and compliance. Use this skill whenever a user asks about FedRAMP authorization, ATO (Authority to Operate), cloud security for federal government, NIST SP 800-53 controls, CSP compliance, or any of the core
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:
