- 📄 SKILL.md
openra-rl
Play Command & Conquer Red Alert RTS — build bases, train armies, and defeat AI opponents using 48 MCP tools.
Play Command & Conquer Red Alert RTS — build bases, train armies, and defeat AI opponents using 48 MCP tools.
Manage AWS resources via the aws CLI.
View and set configuration. Use when the user asks to change trigger behavior, extension settings, or other settings.
Deep code scan for AI security issues — prompt injection, PII in prompts, hardcoded keys, unguarded agents.
Use when writing test fixtures for @copilotkit/llmock — mock LLM responses, tool call sequences, error injection, multi-turn agent loops, embeddings, structured output, sequential responses, or debugging fixture mismatches
Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
Systematic workflow for clustering biological samples, features, or any quantitative data matrix. Implements multiple clustering algorithms with rigorous validation, comparison, and interpretation to identify meaningful data groupings.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Use this skill when the user asks to: (1) create a new skill, (2) make a skill, (3) build a skill, (4) set up a skill, (5) initialize a skill, (6) scaffold a skill, (7) update or modify an existing skill, (8) validate a skill, (9) learn about skill structure, (10) understand how skills work, or (11) get guidance on skill design patterns. Trigger on phrases like \"create a skill\", \"new skill\", \"make a skill\", \"skill for X\", \"how do I create a skill\", or \"help me build a skill\".
Evaluate and score agent behavior against a golden reference. Use this skill whenever the user wants to run evaluation, check pass/fail status, understand metric scores, compare sessions for regressions, validate agent behavior, or score a trace from a file or a live session. Trigger on phrases like "eval this trace", "check my agent output", "did my agent do the right thing", "compare runs", "did my agent regress", "score session X", "evaluate against golden", "run evals". Works with both local trace files and live streaming sessions. --- Evaluate agent behavior and explain what the scores mean. ## Determine the input type First, figure out what to evaluate: - **Trace file(s)** — user mentions a `.json` or `.jsonl` file path → use `evaluate_traces` - **Sessions vs golden** — user has multiple live sessions and wants regression testing → use `evaluate_sessions` - **Single live session** — user wants to score one session against a golden eval set → guide them to use `evaluate_sessions` with one session as golden ## Evaluating trace files 1. Get the file path(s). Check the extension: `.jsonl` → `trace_format: "otlp-json"` | `.json` → `"jaeger-json"` (default) 2. Ask if they have a golden eval set JSON. For `tool_trajectory_avg_score` (the default metric), an eval set is required — it provides the expected tool call sequence to compare against. If they don't have one yet, explain this and suggest starting with `hallucinations_v1`, or ask if they want to create a golden set from a reference run first. 3. Call `evaluate_traces` with the file(s), format, and eval set. 4. Present results as a score table (see Score interpretation below) and explain failures. ## Evaluating sessions (regression testing) This workflow requires the server to be running with the `--dev` flag (which enables WebSocket and session streaming). Plain `agentevals serve` will not have sessions. If you get a connection error from any tool below, tell the user: ```bash uv run agentevals serve --dev ```
Skill bundle for long-running Clawcolony agents. Use when joining the colony, deciding what to work on, reading mail, routing to domain skills, or starting a new session. NOT for one-shot tasks outside Clawcolony.
Use when working in a Ruby project - provides authoritative sources for documentation, typing, and tooling
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: