Build validated web research processes through self-annealing loops. Takes a research goal, generates search steps, tests against sample companies, scores accuracy, and iterates until 90%+. Use when creating new research workflows, building claygent/agent prompts, or systematizing any web research task.
Full-autopilot trend discovery, deep research, and social publishing pipeline. Uses trend-pulse (20 sources), cf-browser (headless Chrome), and notebooklm (research + artifacts) MCP servers. Generates algorithm-optimized content based on Meta's 7 patent-based ranking algorithms. Use when user mentions trending topics, content creation, social media publishing, trend analysis, research pipeline, viral content, content scoring, or Threads posting.
Use when preparing to engage a target account, researching a company before outreach, or building pre-call intelligence. Triggers: 'research this company', 'account brief for [company]', 'company research', 'what do I need to know about [company]', 'pre-outreach research'.
Amazon seller data analysis tool. Features: market research, product selection, competitor analysis, ASIN evaluation, pricing reference, category research. Uses scripts/apiclaw.py to call APIClaw API, requires APICLAW_API_KEY. --- # APIClaw — Amazon Seller Data Analysis > AI-powered Amazon product research. Respond in user's language. ## Files | File | Purpose | |------|---------| | `scripts/apiclaw.py` | **Execute** for all API calls (run `--help` for params) | | `references/reference.md` | Load when you need exact field names or filter details | ## Credential
Complete academic research skill suite covering the full pipeline: paper reading (read/explain papers with storytelling), idea generation (brainstorm research directions), experiment design (plan experiments, ablation, baselines), proof writing (mathematical proofs, LaTeX theorems), paper writing (draft to camera-ready for top venues like NeurIPS/ICLR/ACL), paper review (structured 4-step review with scoring), and professor fit analysis (evaluate advisors, cold emails, interview strategy). Trigger keywords: read paper, brainstorm, experiment design, prove, write paper, review, professor fit, advisor, cold email, LaTeX, research, NeurIPS, ICLR, ACL, arXiv, 讀論文, 寫論文, 審稿, 實驗設計, 數學證明, 研究方向, 教授分析, 選指導教授.
Use this skill whenever a researcher wants to test, validate, stress-test, or falsify a research idea or hypothesis — especially in AI/ML/deep learning. Trigger on phrases like "I have an idea," "would this work," "test this hypothesis," "sanity check my idea," "what's wrong with this idea," "review my results," "is this publishable," "why isn't this working," or any request to evaluate the feasibility, novelty, or correctness of a research concept.
Set up and launch an autonomous AI research project with Limina. TRIGGER when: user types /limina, says 'start a limina project', 'set up a research project', 'I want to research X autonomously', 'create a research agent', 'autonomous research', 'start a new research mission', or asks to investigate/research a hard technical problem systematically with experiments and evidence. Also triggers on 'limina' mentioned as a tool to use. DO NOT TRIGGER for: general coding questions, simple research lookups, or tasks that don't need structured multi-session research.
Research automation skills for Claude Code. Full pipeline from idea to submittable paper. Skills: /lit-review, /novelty-check, /experiment, /analyze-results, /write-paper, /research (orchestrator), /setup.
Deep research with citation tracking
Fast research that beats plain websearch — discovers what exists before searching specifics (Landscape Scan), catches recent releases within days/weeks (Recency Pulse + upstream supply chain), and runs parallel queries for multi-angle coverage. Good for everyday research and current-info questions. Use when user requests research, comparison, or "what's the latest on X". For high-stakes decisions requiring hypothesis testing, COMPASS audit, Red Team, or full report → use /deep-research-pro instead.
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
Plans new DataHub connectors by classifying the source system, researching it using a dedicated agent or inline research, and generating a _PLANNING.md blueprint with entity mapping and architecture decisions. Use when building a new connector, researching a source system for DataHub, or designing connector architecture. Triggers on: "plan a connector", "new connector for X", "research X for DataHub", "design connector for X", "create planning doc", or any request to plan/research/design a DataHub ingestion source.
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:
