# Desk Research

> Structured desk research workflow for market, company, policy, product, and competitor questions. Use when a user asks for secondary research, landscape scans, evidence-based summaries, source triangulation, or insight synthesis from public information.

- Skill: `dvcrn/desk-research` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dvcrn/desk-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/desk-research/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/dvcrn/desk-research

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# Desk Research

Execute this workflow for any desk-research request.

## 0) Load methodology checklist (first)

Read `references/methodology.md`, `references/deep-writing-patterns.md`, and `references/quality-checklist.md` and apply all as guardrails.

## 1) Define the research brief

Write 4 lines before searching:
- Research question (1 sentence)
- Scope (time, geography, industry)
- Must-answer sub-questions (3-6 bullets)
- Output format needed by user

If the question is vague, propose assumptions explicitly and continue.

## 2) Build a source plan

Collect evidence in this priority order:
1. Primary/official sources (government, regulator, company filings, product docs)
2. Reputable secondary analysis (major research firms, established media)
3. Community signals (forums/social) only as supporting evidence

Require at least 2 independent sources for every key claim.

## 3) Gather evidence fast

For each sub-question:
- Find 3-8 candidate sources
- Keep the highest-signal sources
- Extract only claim + evidence + date + link

Reject sources that are undated, anonymous, or purely opinionated unless the user asked for sentiment.

## 4) Score source reliability

Tag each source:
- A = official primary source
- B = credible secondary source
- C = weak/indicative source

When claims conflict, prefer newer A/B sources and explicitly note uncertainty.

## 5) Synthesize insights

Convert notes into:
- Facts (well-supported)
- Interpretations (reasoned but inferential)
- Unknowns (gaps needing validation)

Never present interpretation as fact.

## 5.5) Deepening loop (mandatory)

Before final delivery, run at least 2 rounds of self-questioning:

Round A — Coverage challenge
- What did I miss by source type, time window, or geography?
- Which category/conclusion is over-dependent on one source?
- What contradicts my current conclusion?

Round B — Decision challenge
- If this conclusion is wrong, what evidence would prove it wrong?
- Which part is descriptive but not decision-useful?
- What next data pull would most change the recommendation?

After each round, update findings and confidence.

## 6) Deliver in concise structure

Use this exact section order:
1. Core Questions (2 questions)
2. One-sentence Verdict
3. Executive Summary (5-8 bullets)
4. Key Findings by sub-question (with metric anchors)
5. Evidence Table (claim | source | date | reliability)
6. Confidence tags (High/Medium/Low per major claim)
7. Risks / Uncertainty
8. What would falsify this conclusion
9. Next Verification Steps / Todo

For output shape and compact template, use `references/output-template.md`.

## 7) Quality bar before sending

Check all items:
- Every major claim has source/date
- No single-source critical claim
- Time/geography scope matches user ask
- Clear separation of fact vs interpretation
- Actionable takeaway included
- Each promising case uses the full 9-part deep case framework
- Each promising case includes one final case-summary paragraph: what it does / who pays / business model / why pay
- Each key section ends with decision implication (so-what)

## 8) Case-depth hard rule (for startup/case research)

When the task is startup/use-case research, apply these hard requirements:
- For each promising case, collect at least 3 website evidence snippets (feature/pricing/use-flow)
- Add at least 1 metric anchor from trusted dataset (revenue/MRR/growth)
- Include at least 1 risk point and 1 falsification condition
- Do not submit if any case is only descriptive without judgment