- 📄 SKILL.md
data-pipeline
Guide agent through geospatial ETL workflows using built-in, learned, and fabricated geo tools
Guide agent through geospatial ETL workflows using built-in, learned, and fabricated geo tools
Enforces design review gate after brainstorming — bridges superpowers:brainstorming into the metaswarm quality pipeline
Use when the user asks about available workflow skills, wants an overview of the engineering workflow, or references "nanostack". Also triggers on /nanostack.
Use this skill to extract and list tables from Excel files using the eparse CLI. Call when you need to discover or extract tabular data from one or more Excel files or directories. Supports output to console, SQLite, or PostgreSQL.
Document-driven OSpec workflow for AI-assisted development with change-ready initialization, execution, validation, and archive readiness.
Analyzes a Go service using lib-commons v2/v3 and generates a visual migration report showing every change needed to upgrade to lib-commons v4. Produces an interactive HTML page (via ring:visualize) and optionally generates
Use when analyzing a large corpus of text, code, or data that exceeds a single agent's effective context - orchestrates parallel Worker subagents, Critic review subagents, and a final Summarizer subagent with task tracking and failure recovery
Browse, upload, and interact with videos on BoTTube (bottube.ai) - a video platform for AI agents. Generate videos with any tool and share them.
Set up and run an autonomous experiment loop for any optimization target. Gathers what to optimize, then starts the loop immediately. Use when asked to "run autoresearch", "optimize X in a loop", "set up autoresearch for X", or "start experiments".
Use when feature development, bugfix, or refactoring is complete in the EigenFlux project and code needs validation. Proactively invoke after finishing implementation — build, start services, run affected unit and integration tests autonomously.
Price alerts, threshold monitoring, and notification triggers for agents.
Create a temporary real project and prove a prove_it feature works (or doesn't) end-to-end. Builds a disposable git repo, writes a focused config, runs real dispatches through the installed or local prove_it, and produces a human-readable session transcript. Use when you need to prove a feature, reproduce a bug, or validate a fix against a real project — not just unit tests. --- # Prove a feature works (or doesn't) Build a throwaway project and exercise a prove_it feature through the real dispatcher pipeline. The output is a human-readable transcript the user can read to confirm the system works end-to-end. ## What "prove" means — read this first **Proving a feature means watching the feature do its actual job, not just watching the dispatcher accept a config and return a decision.** If the feature is a reviewer that detects dead code, you must: 1. Create a project that **contains dead code** → run the reviewer → see it **catch** the dead code 2. Create a project that **has no dead code** → run the reviewer → see it **pass clean** If the feature is a task that validates API design, you must: 1. Write an API file with **real design violations** → see the task **reject** it 2. Write a clean API file → see the task **approve** it If the feature is a when-condition gate, you must: 1. Run with the condition **unmet** → see the task **get skipped** 2. Run with the condition **met** → see the task **actually execute and produce its real output**
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.
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We try to avoid that. Use ranking + comments to surface better skills: