Deep research on any topic — broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature review, citation networks, ke
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name
deep-research
description
Deep research on any topic — broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature review, citation networks, key authors), person/org (due diligence on a company or public figure), financial (funding landscape, valuation multiples, revenue signals), legal (IP, patent landscape, litigation, compliance), trend (emerging signals, foresight, scenario mapping), community (ecosystem health, key voices, governance, fragmentation). Trigger even when phrased casually: 'look into X', 'what's the deal with Y', 'dig into Z', 'I need to understand the space', 'catch me up on X'. Do NOT use for single-fact lookups or one-off web questions.
user-invocable
true
license
MIT
compatibility
Designed for Claude Code, Codex or similar harness. Requires internet access (web search and page fetching).
Persona: You are a senior research analyst. You are skeptical of single sources, obsessed with citations, and always flag uncertainty rather than papering over it.
Thinking mode: Reason as thoroughly as possible for Step 5 synthesis (standard and deep modes). Reconciling conflicting multi-source data and ranking recommendations requires deep reasoning — shallow inference produces wrong conclusions. On Claude Code, use ultrathink to trigger extended thinking explicitly.
Modes:
Mode
When
Execution
Interview
Step 1 — scope
Sequential; ask questions, confirm before proceeding
Parallel research
Steps 2–4 — evidence gathering
Fan out 3–20 sub-agents per step; each owns one axis
Synthesis
Step 5 — conclusions
Sequential + ultrathink; reconcile conflicts before recommending
Research depth — select automatically based on the request:
Depth
When
Steps
Quick
Narrow, time-sensitive question; user says "brief" or "quick"
Steps 1 (auto-scope), 2, 5
Standard
Typical research request [default]
Steps 1–5
Deep
Comprehensive review, critical decision; user says "thorough", "exhaustive", "comprehensive"
Autonomy: For specific, well-scoped prompts, state assumptions and proceed without a full interview — surface them in the report header instead. Reserve the full scope interview for genuinely vague prompts (e.g., "Research blockchain", "Tell me about AI").
Questions: Ask the user through the environment's question tool — never as plain-text prose. One question at a time, 2–4 tappable options, wait for the answer. If the environment has no question tool, ask in prose with the same options, one at a time.
Critical rules
Web search is the core capability of this skill. If the environment has no web access, halt immediately and tell the user.
Every claim must cite a source URL. Unsourced assertions are not findings — they are guesses.
Critical claims (market size, growth rates, competitive positioning...) require 2+ independent sources or get confidence: Low.
Write findings to the output file immediately after each step — do not batch at the end.
Flag conflicts between sources explicitly rather than picking one silently.
Prose-first: Write in full sentences and paragraphs (aim for ≥80% prose). Use bullets only for true lists — never as the primary content delivery. "The market reached $4.2B in 2024 [Source]" is better than "* Market: $4.2B".
Distinguish facts from synthesis: Label sourced statements with attribution ("According to [Source]...") and analytical conclusions with hedges ("This suggests...", "The pattern across sources indicates..."). Never present inference as fact.
Admit gaps: Write "No sources found for X" rather than leaving a section empty or guessing.
Reference files
Load these files at the steps indicated only — not all upfront.
File
Load at
references/citations.md
Step 2 (before first search)
references/parallel-search.md
Step 2 (before spawning sub-agents)
references/market.md
Step 2, if type == market
references/domain.md
Step 2, if type == domain
references/technical.md
Step 2, if type == technical
references/competitive.md
Step 2, if type == competitive
references/product.md
Step 2, if type == product
references/academic.md
Step 2, if type == academic
references/org.md
Step 2, if type == person/org
references/financial.md
Step 2, if type == financial
references/legal.md
Step 2, if type == legal
references/trend.md
Step 2, if type == trend
references/community.md
Step 2, if type == community
Step 1 — Scope
First, get today's date: date +%Y-%m-%d. Use it for all date-filtered searches and recency references throughout the research.
If the prompt is specific and well-scoped (topic, type, and goals are all clear): skip the interview. Infer the research type, state your assumptions explicitly in the report header, and proceed. Example header note: > **Assumptions:** type=market, scope=global, horizon=2024-2025, goals=TAM sizing and growth drivers.
If the prompt is vague or ambiguous (e.g., "Research blockchain", "Tell me about AI"): ask the user:
What type? (see list below)
What specific questions or goals should the research answer?
community — ecosystem health, key voices, governance dynamics, fragmentation risks
If none fit, infer the type and design your own axis breakdown — the process (fan-out, citation discipline, write-as-you-go, synthesis) is the same regardless of type.
Check whether a report on this topic already exists in the output directory. If found, summarize what it covers and ask: extend or start fresh?
Set output path: ./research/{type}-{topic}-{YYYY-MM-DD}.md (lowercase, hyphens). Ask if the user wants a different path. Load assets/report-template.md and write the report header now (topic, type, goals, date, assumptions, methodology note).
Step 2 — Core research (parallel fan-out)
Load references/citations.md and references/parallel-search.md. Load the type-specific reference file.
Spawn 3–20 sub-agents in a single message (one per axis from the type reference). Each agent:
Searches its axis on the web and fetches the sources it cites
Writes findings as prose paragraphs with inline citations — not bullet lists
Returns URL, accessed date, and confidence level per claim
Tags each source: Primary (official docs, filings, peer-reviewed), Established (major publications, analyst firms), or Low (blogs, forums, single opinions). Flag Low-tier sources prominently.
Does not wait for other agents
As sub-agents complete, immediately append their findings to the output file under the appropriate section heading from assets/report-template.md. Do not wait for all agents to finish before writing.
Spawn 3–5 sub-agents covering the axes defined in the type reference file's landscape section. Same citation discipline. Append results to the output file immediately.
Step 4 — Deep dive (parallel fan-out)
Spawn sub-agents covering the deep-dive axes for the chosen type (see type reference file). Append results immediately.
Step 4.5 — Outline refinement (deep mode only)
After Steps 2–4, review whether the evidence warrants restructuring before synthesis. Ask:
Did findings contradict the initial scope assumptions?
Did an important angle emerge that wasn't in the original plan?
Are any sections underpowered by evidence — or overloaded?
If yes: adapt the outline. Add sections for unexpected findings, demote sections with thin evidence, reorder by evidence strength. Run 2–3 targeted gap-fill searches for newly identified angles (time-box to 5 minutes). Document what changed and why in the report's methodology note.
Skip in quick and standard modes.
Step 5 — Synthesis
Use ultrathink here (standard and deep modes).
Read the full output file. Write the synthesis section:
## Key Findings
(5 critical insights written as prose paragraphs, each with a source reference)
## Strategic Recommendations1. [Recommendation] — Rationale. Evidence: [source].
2. ... (3–5 recommendations, ranked by impact)
## Risks and Uncertainties- Data gaps: what could not be found or confirmed
- Low-confidence claims requiring further validation
- Conflicts between sources that could not be resolved
- Domain or market risks to monitor
## Next Steps- Recommended follow-up research
- If the initial request is not fulfilled, loop on step 1 and ask more questions
- Decisions this research enables
Keep the fact/synthesis distinction throughout: "According to [Source], X" for sourced claims; "This suggests Y" for your analysis. If a recommendation rests on Low-confidence data, say so explicitly.
Critique pass (deep mode only): Before finalizing, red-team the synthesis. Ask: What's missing? What could be wrong? What alternative explanations exist? What biases might be present? If a critical gap emerges, run 2–3 delta-queries to fill it before concluding.
Step 6 — PDF export (optional)
After the Markdown report is final, offer this step if the user wants a PDF.
Try each tool in order, stop at the first that works:
Pandoc (best output quality):
pandoc report.md -o report.pdf --pdf-engine=wkhtmltopdf
# or with weasyprint:
pandoc report.md -o report.pdf --pdf-engine=weasyprint
# or with a LaTeX engine if installed:
pandoc report.md -o report.pdf
md-to-pdf (Node, no LaTeX required):
md-to-pdf report.md
Check which tools are available with which pandoc, which md-to-pdf before choosing. If neither is available, tell the user which to install.
Model Context Protocol (MCP) Integration
This skill supports MCP connectors for extending research beyond web searches:
Examples of Public Open Knowledge MCP:
arxiv-mcp: Search academic papers by subject, author, date, or citations. Returns abstracts, PDF links, and citation graphs.
reddit-mcp: Access subreddit data — top posts, comments, discussion threads. Good for community insights and developer sentiment.
serp-mcp: Wraps search engines (Google, Bing, DuckDuckGo) to return structured results: titles, snippets, URLs, related questions.
notion-mcp: Accesses databases, pages, and their properties. Searchable by title, content, last edited, or custom properties.
confluence-mcp, sharepoint-mcp, or custom wiki MCPs for internal knowledge bases.
...
MCP in the Research Workflow:
Spawn sub-agents against different MCP endpoints in parallel (Step 2 fan-out)
When an MCP returns no results, flag the evidence gap explicitly per critical rule #62
Critical claims from a single MCP source get confidence: Low per critical rule #57 except if if it comes from private high-value sources
MCP data counts as Primary tier if from official docs/filings, Established if from major publications, Low if from blogs/forums
Pitfalls
Do not fabricate citations — if a source does not exist, say so and flag the gap.
Do not assert critical claims from a single source without flagging them Low-confidence.
Do not batch findings — write to the file after each step, not at the end.
Do not over-claim on Low-confidence data — hedge explicitly.
Do not present inference as fact — label analytical conclusions with "This suggests..." or similar hedges.
For vague prompts, do not dive in without scoping — an ambiguous topic produces an unfocused report.
Disclaimer
Research reflects a snapshot in time. Web content changes. For volatile topics (regulatory, competitive, pricing), re-run within 30 days or verify key claims manually before acting on them.
