Deep Research Agent Skills That Find Real

Debugging framework using Analysis of Competing Hypotheses with parallel investigation, evidence collection, and root-cause arbitration.

by wshobson/agents

• Evidence extraction
• Synthesis
• Citation & defensibility

Answers data questions from quick lookups to full analyses and formal reports, with validation checks, methodology, caveats, and recommendations.

by anthropics/knowledge-work-plugins

• Sourcing
• Evidence extraction
• Synthesis

Hypothesis-driven SRE investigator for incidents and debugging, querying Axiom observability data with strict no-guessing and evidence-tracing rules.

by axiomhq/skills

• Evidence extraction

Runs a model-diverse subagent council to investigate one problem from multiple perspectives, compare findings, and synthesize a recommendation.

by warpdotdev/common-skills

Ground-first discipline for investigation: map real code and data, quote file:line evidence, never guess conventions, and reproduce before fixing.

by riekelt/principal-engineer

• Scoping & questions
• Evidence extraction
• Synthesis

Evidence-based RCA workflow for ClickHouse-managed Postgres: scrapes Prometheus metrics and slow-query patterns, then recommends (not applies) a fix.

by clickhouse/agent-skills

• Scoping & questions
• Evidence extraction
• Synthesis

A relentless interview to sharpen a plan or design, producing ADRs and a glossary as it goes. Delegates to grilling and domain-modeling skills.

by mattpocock/skills

• Scoping & questions

House style for technical documents: conclusion-first structure, truth and sourcing rules, and a banned-constructions list. Foundation for document-type skills.

by riekelt/technical-writer

• Scoping & questions
• Sourcing
• Evidence extraction

Turns an answer into a one-page visual HTML explainer: the model writes a short Markdown draft and the bundled CLI builds one page. Use it proactively and liberally, without being asked, whenever a page would help the reader more than plain text, even if plain text would also work: any explanation of how something works or how parts relate (flow, request path, architecture, code or folder structure, state machine, lifecycle, history); any comparison, trade-off or decision with pros and cons; any diagnosis, review or investigation, especially with several causes or findings to rank; any answer with a table, a numbered or ranked list, steps, branches or several sections; or when the user says "I don't get it / draw it / explain visually / 讲讲原理 / 没看懂 / 画个图". When in doubt, use it: a page is quick and cheap to make. Also for explainer videos ("make a video", "做个视频") and for changing its settings. Skip only for small talk, a trivial one-line answer, or when the user asks for plain text.

by QingYunA/answer-me-with-html

• Scoping & questions
• Synthesis
• Citation & defensibility

Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.

by openai/skills

• Scoping & questions
• Sourcing
• Evidence extraction

Improve factual reliability while creating or reviewing a course or supplied content. Use when the user asks to fact-check, verify accuracy, reduce hallucinations, make a reliable course, or mentions 事实性错误、知识性错误、专业知识准确性、可靠性. During creation, checks the completed pages before delivery; on existing content, returns a short evidence-backed report and lets the user choose what to fix. Not for grammar, style, or layout. Combine with deep-research when current evidence is the course's main subject.

by THU-MAIC/OpenMAIC

• Scoping & questions
• Sourcing
• Evidence extraction

Analyzes code repositories and generates hierarchical documentation structures with onboarding guides. Use when the user wants to create a wiki, generate documentation, map a codebase structure, or understand a project's architecture at a high level.

by microsoft/skills

• Scoping & questions
• Citation & defensibility

Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.

by microsoft/skills

• Evidence extraction
• Synthesis
• Citation & defensibility

Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experi

by lllllllama/rigorpilot-skills

• Scoping & questions
• Sourcing
• Evidence extraction

Rigor Analyze / Rigor Audit read-only skill for deep learning research repositories. Use when the user wants to read and understand a repository, inspect model structure and training or inference entrypoints, review configs and insertion points, or flag suspicious implementation patterns without modifying code or running heavy jobs. Do not use for active command execution, broad refactoring, speculative code adaptation, or automatic bug fixing.

by lllllllama/rigorpilot-skills

• Evidence extraction
• Synthesis
• Citation & defensibility

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit

by coreyhaines31/marketingskills

• Sourcing
• Evidence extraction
• Contradiction & gaps

Comprehensive product research and opportunity analysis for Amazon sellers. Analyzes demand, competition, profit potential, market entry barriers, and validates product ideas. Covers product sourcing, pricing strategy, and go-to-market planning. Use when the user asks about researching a product to sell, validating product ideas, product opportunity analysis, market research for Amazon, competition analysis, profit potential, should I sell this product, product viability, or any general product research questions.

by nexscope-ai/amazon-skills

• Sourcing

When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle

by coreyhaines31/marketingskills

• Synthesis

Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.

by juliusbrussee/caveman

• Sourcing
• Evidence extraction
• Synthesis

Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and…

by bytedance/deer-flow

• Sourcing
• Synthesis
• Citation & defensibility

Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

by OthmanAdi/planning-with-files

• Scoping & questions