Search, read, and query research papers via the `alpha` CLI (alphaXiv-backed). Use when the user asks about academic papers, wants to find research on a topic, needs to read a specific paper, ask questions about a paper, inspect a paper's code repository, or manage paper annotations.
Methodical research assistant for exhaustive investigations through systematic research cycles. Best for literature reviews, competitive analysis, trend reports, and comprehensive topic exploration.
Use this when the user wants research ideas, innovation points, or to find research gaps (找研究方向, 生成创新点). Generates 5 ideas from collected papers, identifies gaps, proposes novel methods with citations. Requires papers in workspace.
Routing guide -- when to use `nansen agent` (AI research) vs direct CLI data commands. Use when deciding how to answer a user's research question with Nansen tools.
Adversarial research analysis framework that uses structured Bull/Bear/Arbiter debates to help users make better research judgments. Maintains a belief graph as backend engine, applies statistical calibration discipline, tracks phase transitions, and detects biases.
Save and organize links for later reading. Use when: user wants to save a URL, manage bookmarks, find a saved link. Don't use when: user just wants to read/fetch a URL (use web_fetch) or research a topic (use research skill).
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.
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.
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.
Strategic research framework that compresses months of market/competitive research into hours through structured power questions. Extracts unspoken industry insights, fragile market assumptions, and strategic attack surfaces from competitor data, reviews, and industry sources using parallel Exa-powered intelligence gathering. Use when user says "attack surface", "research the market", "competitive analysis", "analyze competitors", "find market opportunity", "stress-test this idea", "market research", "evaluate opportunity", "find blind spots", "market entry", or when they want to deeply understand a market, evaluate a new direction, find industry blind spots, assess a partnership, or analyze opportunities. Do NOT use for code review, testing, deployment, bug fixing, or implementation tasks. --- # Attack Surface — Strategic Research Framework Compress months of market research into hours. The difference between 3 hours and 3 months isn't the amount of information — it's knowing which questions actually matter. Instead of "summarize these" or "analyze the competition", this framework extracts: - **UNSPOKEN INSIGHTS** — what successful players understand that customers never say out loud - **FRAGILE ASSUMPTIONS** — beliefs the entire market is built on, and how they break - **ATTACK SURFACES** — the blind spots, the fragile consensus, the opening nobody is talking about ## When to Use - Entering a new market or vertical - Evaluating a new feature direction for an existing project - Assessing a partnership or platform opportunity - Stress-testing a business idea before committing - Finding competitive blind spots and underserved niches - Any strategic question that benefits from deep evidence-based analysis ## Workflow Overview 7 phases, alternating between automated intelligence gathering and user-guided analysis: | Phase | Name | Mode | Output | |-------|------|------|--------| | 1 | Briefing | Interactive | Research brief | | 2 | Source Collection | Automated (parall
Index and search code repositories, documentation, research papers, HuggingFace datasets, local folders, Slack workspaces, Google Drive, X (Twitter), and packages with Nia AI. Includes auth bootstrapping, Oracle autonomous research, GitHub live search, Tracer agent, dependency analysis, context sharing, code advisor, document agent, data extraction, filesystem operations, and generic connectors.
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:
