Use when creating, updating, reviewing, or merging a PR in this repo. Covers first-push rebases, review and simplification passes, benchmark baseline requirements, and post-merge `$postmortem` runs.
Academic paper reading system — find papers via Semantic Scholar, read with structured notes, discuss with auto-recording, track progress. 7 commands: /setup /feed /read /discuss /recap /update /sync
Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.
Deep research with citation tracking
Use when advising on project architecture, experiment history, codebase navigation, or research findings. Auto-maintained by /update-project-skill.
Activate when the user mentions healthcare AI testing, safety evaluation, adversarial testing of medical chatbots, or clinical AI benchmarking. Guides them to the right preclinical command.
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
De-Anthropocentric Research Engine — the main entry point for all research tasks. Use this skill whenever the user wants to research a topic, find papers, survey a field, trace citation chains, explore research gaps, generate novel ideas, or run experiments. This skill routes to the appropriate mode (quick/survey/deep/web/hybrid/research/execute) based on user intent. For full research mode, it orchestrates the complete pipeline: brainstorming → literature survey → gap analysis → idea generation → review loop → spec writing → implementation planning → experiment execution. ALWAYS use this skill as the starting point for any research-related request. --- # DARE DARE is a Vibe Researching Toolkit. You are a research assistant that uses DARE's external MCP tools to accomplish research tasks. ## Your Role You are an autonomous research agent. Given a research topic or question, you: 1. Understand intent, gauge desired depth and breadth 2. Automatically select the appropriate mode 3. Orchestrate tool calls autonomously, adapting based on intermediate results 4. Deliver structured research output ## Philosophical Orientation <HARD-GATE> Before proceeding to ANY mode (quick, survey, deep, research, web, hybrid, or execute), you MUST read `skills/dare/references/de-anthropocentric.md` in full. This is the value system that governs all research activity in DARE. Do NOT skip this step, do NOT summarize it from memory — read the actual file every time. </HARD-GATE> This philosophy is not a set of instructions. It is a lens. After reading it, carry its principles forward into every decision you make: which papers matter, which gaps to prioritize, which ideas to pursue, how to evaluate progress. The document will tell you what those principles are. ## Tools See `skills/tools/SKILL.md` for full reference, `skills/tools/references/dare-scholar.md` for detailed paper tool usage. | MCP Server | Tool | Purpose | |---|---|---| | apify | `google-scholar-scraper` | Google Scholar search
This skill should be used when the user asks about agent architecture, evaluation, metrics, production monitoring, debugging agents, or best practices for building reliable AI agents. Use for questions like "evaluate my agent", "set up production monitoring", "add guardrails", "detect hallucinations", "agent anti-patterns", "compare experiments", "create evaluation dataset".
Browse the web for any task — research topics, read articles, interact with web apps, fill forms, take screenshots, extract data, and test web pages. Use whenever a browser would be useful, not just when the user explicitly asks.
Analyze session replay patterns across experiment variants to understand user behavior differences. Use when the user wants to see how users interact with different experiment variants, identify usability issues, compare behavior patterns between control and test groups, or get qualitative insights to complement quantitative experiment results.
Evidence-backed web research with citations and confidence scores. Use when the user needs researched, verified answers backed by real sources — not LLM hallucinations.
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:
