Autoresearch loop for governance files. Researches latest X discourse on each governance topic, proposes ONE atomic improvement per file, validates it, keeps or discards. Use when the user asks to improve, update, or evolve the governance framework using latest community insights.
Create a well-formed git commit from current changes using session history for rationale and summary; use when asked to commit, prepare a commit message, or finalize staged work. --- # Commit ## Goals - Produce a commit that reflects the actual code changes and the session context. - Follow common git conventions (type prefix, short subject, wrapped body). - Include both summary and rationale in the body. ## Inputs - Session history for intent and rationale. - `git status`, `git diff`, and `git diff --staged` for actual changes. - Repo-specific commit conventions if documented. ## Steps 1. Read session history to identify scope, intent, and rationale. 2. Inspect the working tree and staged changes (`git status`, `git diff`, `git diff --staged`). 3. Stage intended changes, including new files (`git add -A`) after confirming scope. 4. Sanity-check newly added files; if anything looks random or likely ignored (build artifacts, logs, temp files), flag it to the user before committing. 5. If staging is incomplete or includes unrelated files, fix the index or ask for confirmation. 6. Choose a conventional type and optional scope that match the change (e.g., `feat(scope): ...`, `fix(scope): ...`, `refactor(scope): ...`). 7. Write a subject line in imperative mood, <= 72 characters, no trailing period. 8. Write a body that includes: - Summary of key changes (what changed). - Rationale and trade-offs (why it changed). - Tests or validation run (or explicit note if not run). 9. Wrap body lines at 72 characters. 10. Create the commit message with a here-doc or temp file and use `git commit -F <file>` so newlines are literal (avoid `-m` with `\n`). 11. Commit only when the message matches the staged changes: if the staged diff includes unrelated files or the message describes work that isn't staged, fix the index or revise the message before committing. ## Output - A single commit created with `git commit` whose message reflects the session. ## Template Type and scope are examp
Use when managing AI Hub account, API keys, balance, usage, or API endpoints. Use when user says "AI Hub", "add AI credits", "create API key", "check AI usage", "auto-recharge", "AI Hub endpoint", "AI Hub base URL", "how to use AI Hub API", "LLM API", "AI API", "OpenAI compatible", "Anthropic API", "GPT", "Claude", "Gemini", "DeepSeek", or "Grok" in the context of Zeabur.
Use when advising on project architecture, experiment history, codebase navigation, or research findings. Auto-maintained by /update-project-skill.
Interactive browser automation via Chrome DevTools Protocol. Use when you need to interact with web pages, test frontends, or when user interaction with a visible browser is required.
Audit documentation coverage across CLI commands, web features, and configuration. Builds the CLI, discovers all commands/flags, checks web pages, and cross-references against docs/ and apps/docs/.
Expert data engineer specializing in building scalable data pipelines, ETL/ELT processes, and data infrastructure. Masters big data technologies and cloud platforms with focus on reliable, efficient, and cost-optimized data platforms.
Professional writing assistant for drafting and polishing many kinds of text. Trigger when users need writing help, copy editing, or content rewrites.
Look up any arxiv paper on alphaxiv.org to get a structured AI-generated overview. This is faster and more reliable than trying to read a raw PDF.
Discovers architecture from codebases and authors Sruja DSL (repo.sruja). Use when discovering architecture, generating or refactoring repo.sruja, validating architecture against code, maintaining architecture docs, or when the user mentions architecture-as-code, C4, or .sruja files.
cross-surface agent contracts, evaluate routing behavior, tighten invocation
This skill should be used when the user asks to "write a PR/FAQ", "prfaq", "working backwards", "product discovery", "evaluate a product idea", "press release FAQ", "test product value", "revise prfaq", "update prfaq", "add research to prfaq", "add FAQs", "run a meeting", "review meeting", "hive meeting", "autonomous meeting", "consensus meeting", "stress test my prfaq", "go/no-go decision", "should we build this", "vote on prfaq", or wants to use the Amazon Working Backwards process to evaluate whether a product or feature is worth building. --- # Working Backwards: PR/FAQ ## Purpose Guide the user through the Amazon Working Backwards process to produce a professional PR/FAQ document. The output is a LaTeX file that compiles to a polished PDF suitable for executive review and product decision-making. The process forces clarity about customer value, surfaces risks early, and creates a shared artifact for go/no-go decisions. ## When to Use - Evaluating whether a new product or feature is worth building - Forcing specificity on a vague product idea - Preparing a product pitch for leadership review - Testing whether a team truly understands the customer problem - Structuring a go/no-go decision with an auditable artifact ## Revise Mode Before starting the full workflow, check if a `prfaq.tex` file already exists in the project root (or the path the user specifies). If it does, enter **revise mode** instead of starting from scratch. 1. **Read the existing document.** Parse the `.tex` file to understand what's already written — the press release, FAQs, and risk assessment. 2. **Ask what to revise.** Present the user with the sections found and ask what they want to improve. Common revision goals: - **Refine the product** — sharpen the problem statement, solution, or differentiation based on new thinking - **Incorporate research** — thread new primary data (customer interviews, market analysis, survey results) into existing sections. Run Phase 0 research discovery to find
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:
