# Research Brief > Deep research on a technical or market topic producing a structured, sourced brief saved to the project's docs. Depth means verifiability, not length — citations re-fetched, negatives cross-checked, blind spots hunted by a fresh agent. Use when the user says "research X", "deep dive into X", "do research into X and propose candidates", or asks for analysis of an architecture, paper, strategy, or market event. Not for quick factual questions. - Skill: `evan-daruwalla/research-brief` (Agent Skill) - Install (CLI): `npx skillmds@latest add evan-daruwalla/research-brief` - Raw SKILL.md: https://api.skillmd.com/api/skills/evan-daruwalla/research-brief/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: Evan-Daruwalla (https://skillmd.com/u/evan-daruwalla) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/evan-daruwalla/research-brief --- # Research Brief Structured deep research with sources, skepticism, and a saved artifact — instead of a one-off chat answer that evaporates. Runs a 10-stage research process adapted for desk research: an LLM can search, read, and analyze; it cannot run surveys or interviews — stages needing new field data are scoped honestly as limitations, never simulated. ## Trigger `/research-brief `, or the user says "research X", "deep dive into X", "analyze what happened [in the market / to this stock]", "propose top N candidates for X". ## Inputs - Topic, plus any constraints (time budget, number of candidates, decision it feeds into). - Where to save: default `docs/research/_.md` in the current project; if no docs/ exists, ask. ## The 10 stages **1. Identify the problem.** One sentence: what question is this brief answering, and what practical problem makes it worth answering? If the topic is a proposal the user wrote, analyze it from multiple genuinely different angles (optimistic, pessimistic, different approach directions) — no yes-man convergence. **2. Survey existing work.** Before forming any view, check what already exists: prior project docs/briefs, published papers and peer-reviewed literature, authoritative primary docs. The gap between what's already answered and what isn't IS the research problem — restate stage 1 if the survey shifts it. **3. State hypotheses.** Write the expected answer as a falsifiable working hypothesis, plus at least one rival (or null) hypothesis. Do this BEFORE deep collection — it is the anti-confirmation-bias gate: you now know in advance what evidence would count against you. **4. Design the research.** Plan before collecting: which source types (primary — papers, filings, official docs, datasets — vs. secondary commentary), what analysis (comparison table, quantitative check, timeline), and what evidence would confirm or refute each stage-3 hypothesis. **5. Name the audience.** Who reads this brief, and what decision do they make with it? Depth, jargon level, and ranking criteria all follow from this. **6–7. Choose methods, then collect.** Web-search wide then deep per the stage-4 design, favoring primary sources over blog rehashes; get actual numbers (prices, dates, magnitudes), not narratives. If the question genuinely requires NEW primary data — a survey, interviews, an experiment — say so: scope it as a limitation naming the method a human would use. Never fabricate respondents, quotes, or results. **8. Analyze.** Separate analysis from collection. Any load-bearing claim needs 2+ independent sources or an explicit "single-source, unverified" tag. Test each hypothesis against the evidence; say which survived and which died. **8.5. VERIFY — the mechanism, not a promise.** Stages 1–8 are judgment: a model can satisfy all of them and still produce a confident wrong brief, because nothing in them can fail. This stage can fail. It produces two artifacts that ship WITH the brief. First name the **load-bearing claims**: the ones the verdict rests on. If a claim were false, would the TL;DR change? If no, it is not load-bearing and does not need this treatment. Usually 3–8 of them. - **Re-fetch every load-bearing citation, by a different call than the one that found it**, asking whether the specific claim is present. Mark each **VERIFIED-VERBATIM** (the source states it), **CLOSE-PARAPHRASE** (states it in other words), **NOT-FOUND** (page loads, claim absent — the dangerous one), or **UNREACHABLE**. Never mark a citation verified because it *sounds* like what the source said. - **VERIFIED-VERBATIM requires the page's own text. A summarizing fetch tool cannot produce it** (measured 2026-08-22). Where the fetch tool converts the page and answers through a **separate small fast model** — as `WebFetch` does — what returns is that model's rendition, so "the source states it" would be a claim about the sub-model, not the source. In that measurement it *refused* a verbatim request outright for a public-domain 1851 text that a browser page-text read on the same URL returned in full. It **answers rather than failing**, so nothing marks the difference. To claim VERIFIED-VERBATIM, read the actual text — a browser page-text read, the raw file, or a quote you can see on the page. A fetch-tool answer supports **CLOSE-PARAPHRASE at best**. - **A NOT-FOUND from a fetch tool is provisional, not a kill.** The sub-model may have missed, compressed, or declined the passage rather than the source lacking it. Confirm with a real text read before a NOT-FOUND retires a load-bearing claim — killing a true claim on a summarizer's miss is the same class of error as keeping a false one, and it looks like diligence. Once confirmed, a NOT-FOUND kills that claim, not the source's other uses. - **Numbers register.** Every quantitative claim in one table: number · what it claims · source · date · **source-interest class** (INDEPENDENT / VENDOR-SELF-REPORTED / ADVERSARIAL / UNVERIFIABLE) · verification verdict. A vendor's number about its own product is evidence, and it is *interested* evidence; the register makes that visible instead of leaving it to prose. - **Two structural checks that catch what per-claim checking cannot:** 1. **Same-source dependency.** Do the headline finding and its main counterweight come from the SAME study or author? If so they rise and fall together, and the brief must say so — a pro and a con from one source is one data point wearing two hats. 2. **Interest concentration.** If more than half the load-bearing numbers are VENDOR-SELF-REPORTED, that belongs in the TL;DR, not a footnote. - **Zero-result cross-check.** "No such study exists" is the highest-value finding a brief can make and the easiest to fake with one bad query. Re-run every load-bearing negative a SECOND way (different phrasing, different tool, a citation-chain walk from the nearest real paper) before writing it down. Report which searches produced the negative. **9. Findings.** State findings distinct from interpretation. If the ask was "propose top N", rank candidates with a one-line tradeoff each. **10. Report.** Write and save the brief: - **Header** — title, absolute date, the question, who it's for (stage 5). - **TL;DR** (3–5 sentences, verdict first — which hypothesis won). - **Method** (2–3 lines: the stage-4 design, source types used, limitations including any primary-data gap from stages 6–7), plus the **effect log** from stage 10.6. - **Findings** (organized by theme, each with inline source links). - **Candidates/options ranked** (if asked) with a one-line tradeoff each. - **Verification table** (stage 8.5): load-bearing claim · source · verdict. - **Numbers register** (stage 8.5), with source-interest class per row. - **Thin / missing** — what SHOULD exist and does not: the modality nobody ran, the study nobody published, the number nobody reports. Name the searches that produced each negative. This section is usually the most valuable thing in a real brief and the first thing a shallow one omits. - **What would change this conclusion** (untested stage-3 falsifiers, open questions). - **Sources** (full list, dated). **10.5. COLD ASSESSMENT — someone who didn't write it reads it.** The author of a brief cannot see the shape of its own blind spot; that is what a blind spot is. Spawn a FRESH agent (Agent tool, general-purpose) and hand it ONLY: the draft brief, the stage-3 hypotheses, and the stage-8.5 artifacts. **Do NOT hand it** the collection transcript, which sources were already dismissed, or any account of how hard something was to find. It must be able to say "you never looked" without being told you did. Ask it exactly these, because they are the questions that catch real briefs: 1. Which two claims that look independent actually rest on the same source? 2. What is the source-interest concentration, and is it disclosed up front? 3. Which stage-3 hypothesis was never given a genuine chance to die — where was the evidence that *would* have killed it never sought? 4. Which of the source types the design named was never actually run? 5. What would a domain expert say is obviously missing? 6. Which claim is stated with more confidence than its verification verdict supports? Its findings feed **exactly one** more collection round, then stop. Unbounded assessor loops are how a brief eats a session. If it finds nothing, say so — that is a real result about the brief, not a wasted pass. **10.6. EFFECT LOG — the data that decides whether these stages stay.** Stages 8.5 and 10.5 cost real tokens. Whether they earn it is an empirical question nobody has answered, so answer it locally. Append to the brief, under Method: ``` Phase effects (2026-08-15): 8.5 verify — 10.5 assess — ``` Be strict about "no change" — a stage that reliably reports no change across several briefs is cost without evidence and should be cut, and that finding is worth as much as the stages themselves. Do NOT inflate an effect to justify a stage. Save to the docs path with today's absolute date, and add one line to the project record if the project keeps one. **Long runs**: if research will take >15 minutes, spawn background research agents in parallel by sub-topic (split along stage-4 lines — by hypothesis or theme), then compile when all report in (this matches the existing pre-market-report pipeline pattern). ## Output - The saved brief file, plus the TL;DR and ranked candidates inline in chat. ## Rules - DO NOT MAKE ANYTHING UP. Missing data is reported as missing; unrunnable methods (surveys, interviews, experiments) are reported as limitations, not simulated. - Prefer primary sources; date every source (staleness matters in markets). - Hypotheses come before deep collection — never retrofit them to findings. - Keep the chat response short — the file is the deliverable. - **A citation is not verified because it is plausible.** Only a re-fetch that finds the claim on the page verifies it. "It's the kind of thing that source would say" is how a fabricated citation survives review. **And only a read of the page's own text counts as finding it** — an answer from a summarizing fetch tool is one more model's paraphrase, which is the thing being guarded against, not the guard. Routing between the two: `fetch-first`. - **A statistic from a live, growing corpus is a reading, not a constant** — pin its state (a count and a timestamp) beside the number, or quote the invariant instead of the aggregate. - **Depth is verifiability, not length.** A longer brief with unverified claims is worse than a short one with a big Thin/missing section, because length reads as diligence. If stages 8.5 and 10.5 were skipped, the brief must say so in Method — a brief that silently skipped its own checks is the exact failure this skill exists to prevent.