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Found 76 Skills
Research Solana/crypto startup opportunities using builder project history, crypto archives, investor theses, and market signals. Answers questions conversationally by default; runs the full 8-step deep research workflow on explicit opt-in ("vet this idea", "deep dive").
Use this skill when the user discusses experiment design, ablations, training runs, evaluation, baselines, metrics, failures, or result interpretation that should be logged into Obsidian experiment and result notes.
Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.
Deep Research Skill - Multi-source investigation across X (Twitter), the Web, and academic papers using team agents. Utilize this skill when users request deep research, comprehensive investigation, multi-perspective analysis, or hypothesis development on any topic. It is triggered by phrases such as "deep research", "investigate thoroughly", "research across sources", "ディープリサーチ", or requests for fact-based analysis with original hypotheses. It conducts a 6-phase research process: needs analysis, X preliminary research, parallel web deep-dive (3 agents), information integration, hypothesis construction, and final report delivery.
Comprehensive DeepResearch methodology for conducting rigorous, traceable research projects with quality gates, structured analysis, and decision-ready deliverables. Use when (1) Conducting deep research projects requiring evidence-based analysis, (2) Managing research progress with quality gates and artifacts, (3) Producing research reports with traceable sources and structured reasoning, (4) Applying OSINT verification techniques, (5) Using structured analytic techniques (ACH, Key Assumptions Check, Red Team), (6) Expressing uncertainty and confidence in research findings, (7) Ensuring research deliverables meet intelligence tradecraft standards (ICD 203/206/208)
Multi-agent orchestration workflow for deep research: Split a research objective into parallel sub-objectives, run sub-processes using Claude Code non-interactive mode (`claude -p`); prioritize installed skills for network access and data collection, followed by MCP tools; aggregate sub-results with scripts and refine them chapter by chapter, and finally deliver "finished report file path + summary of key conclusions/recommendations". Applicable scenarios: systematic web/data research, competitor/industry analysis, batch link/dataset shard retrieval, long-form writing and evidence integration, or scenarios where users mention "deep research/Deep Research/Wide Research/multi-agent parallel research/multi-process research".
Multi-AI Parallel Deep Research. Triggered when users need comprehensive research, in-depth study, multi-party comparison, or comprehensive analysis covering multiple dimensions and sources for a certain topic. Suitable for complex topics (technical selection, competitor analysis, industry trends, controversial topics, etc.), not suitable for simple fact queries. Conduct parallel research through multiple AI services, cross-validate, and output a comprehensive report with citations.
Use this skill any time the user wants in-depth research or comprehensive analysis on any topic. This includes: industry analysis, competitive landscape mapping, market sizing, trend analysis, technology reviews, investment research, sector overviews, due diligence, benchmark studies, patent landscape analysis, regulatory analysis, and academic surveys. Also trigger when: user says 帮我调研一下, 深度分析, 行业研究, 市场规模分析, 竞争格局, 技术趋势, 做个研究报告. If deep research or comprehensive analysis is needed, use this skill.
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
Transform research questions into constructs, design, samples, measurement, analysis, falsification, and execution plans. Use when the user asks for "design research", "can this method answer the question?", "help me create a research proposal", or requests the rw-research-design workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Search and download academic papers from arXiv. Find papers by keywords, authors, or arXiv ID.