# Deep Research > Running structured, multi-source research that produces a trustworthy, cited synthesized answer. Use when a question needs more than a single lookup — comparing options, validating a claim, surveying a market or technology, or briefing a decision where being wrong is costly. Covers scoping (and narrowing) the question, fanning out across sources, judging source quality, adversarially verifying each key claim, separating fact from inference, and reporting with explicit confidence. Keywords deep research, multi-source, fact-check, verify, synthesize, cited report, source credibility, due diligence. - Skill: `5dive-ai/deep-research` (Agent Skill) - Install (CLI): `npx skillmds@latest add 5dive-ai/deep-research` - Raw SKILL.md: https://api.skillmd.com/api/skills/5dive-ai/deep-research/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: 5dive-ai (https://skillmd.com/u/5dive-ai) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/5dive-ai/deep-research --- # Deep Research Guide Produce a **trustworthy synthesized answer**, not a pile of links. The output of research is a decision-grade brief: a clear claim, the evidence behind it, citations, and an honest map of what is still unknown. Speed without verification is a liability — a confident wrong answer costs more than a slow right one. > Core principle: default to skeptical. Treat every key claim as guilty until corroborated. > Your job is not to confirm a hypothesis — it's to find what would break it. ## Workflow — plan → gather → verify → synthesize Run these as distinct phases. Don't start writing the report while you're still gathering, and don't gather before the question is scoped. ### 1. Plan — scope the question Before searching, pin down what's actually being asked. Most bad research answers a question nobody asked. - **State the question in one sentence.** If you can't, it's not scoped yet. - **Name the decision it serves.** "Which X should we pick?" needs different evidence than "Is X true?". The decision sets the bar for confidence. - **List the answer's shape.** A number? A recommendation? A comparison table? A yes/no with caveats? Knowing the shape tells you when you're done. - **Set boundaries**: time window (recency that matters), geography, scale, definitions of fuzzy terms. **Narrow an underspecified question before spending effort.** If the ask is "what should I buy / which tool / is this a good idea" without budget, use-case, constraints, or context, ask 2–3 clarifying questions first. Researching the wrong question thoroughly is still wrong. Then decompose into **sub-questions** — the 3–7 things that must each be answered for the whole to hold. Research the sub-questions; assemble the answer. ### 2. Gather — fan out across sources - **Cast wide before going deep.** Run several differently-worded queries; don't anchor on the first source's framing. Search for the *counter*-claim too ("X is overrated", "problems with X") — not just confirmation. - **Go to primary sources.** Prefer the original study, filing, spec, dataset, or official doc over an article summarizing it. Summaries drift; numbers get garbled in retelling. - **Triangulate.** A claim is only as strong as the number of *independent* sources that confirm it. Three outlets all citing one press release is one source, not three. - **Capture as you go**: for each fact, note the source, the date, and a direct quote/figure. You cannot cite what you didn't record. ### 3. Verify — adversarial check (the step people skip) For each **key claim** (the ones the conclusion rests on), actively try to refute it: - **Find the origin.** Trace the claim to its source. Where did this number actually come from? Who measured it, how, and when? - **Look for the strongest disagreement.** Who says the opposite, and why? A claim you can't find any dissent on is either settled or you haven't looked hard enough. - **Check the math and the units.** Percentages without a base, totals that don't add up, growth rates with no time frame, and apples-to-oranges comparisons are the common tells. - **Test recency.** Is this still true? Prices, rankings, "fastest/largest/only" claims, and policy facts decay. A correct 2019 fact can be a wrong 2026 answer. - **Watch for self-interest.** Vendor benchmarks, sponsored studies, and anything selling something get an extra round of scrutiny. If a claim survives a genuine attempt to break it, it's load-bearing. If it doesn't, demote it to "reported but unverified" or drop it. ### 4. Synthesize — write the brief - **Lead with the answer.** First line: the conclusion / recommendation. Decision-makers read top-down and may stop after the first paragraph — make it count. - **Then the why**, structured by sub-question, each point carrying its citation. - **Separate fact from inference explicitly** (see below). - **Close with confidence + unknowns.** ## Source-credibility checklist Score each source before you lean on it: - **Primary or secondary?** Original data/document > reporting on it > commentary on the reporting. - **Authority** — does the author/org actually have standing on this topic, or are they out of their lane? - **Recency** — is it current enough for a claim that changes over time? Note the date, always. - **Independence** — funded by, owned by, or selling the thing in question? Conflicts bias. - **Method transparency** — can you see how they got the number (sample, methodology, sources), or are you trusting an assertion? - **Corroboration** — do independent sources agree? Outliers need explaining, not silent dropping. - **Track record** — has this source been reliable/retracted before? Rough hierarchy (context-dependent, not absolute): peer-reviewed studies, official filings/standards, and primary datasets at the top; reputable journalism and expert analysis in the middle; anonymous posts, marketing, and AI-generated content summaries near the bottom. A low-tier source can still be right — it just needs corroboration before it carries weight. ## Separating fact from inference Be ruthless about which is which; conflating them is how research misleads. - **Fact** — directly stated by a credible source, with a citation. ("Revenue was $4.2M in 2025 [source].") - **Inference** — your reasoning *from* facts. Label it. ("This implies ~30% YoY growth, assuming the 2024 figure of $3.2M is comparable.") - **Assumption** — something you're taking as given without evidence. Name it so the reader can challenge it. ("Assuming the same accounting basis across years.") - **Unknown** — a gap you couldn't fill. State it; don't paper over it. Phrases that signal you're doing it right: "according to…", "this suggests…", "I could not find…", "sources disagree on…". ## Reporting confidence and unknowns End every brief with an explicit confidence statement. Vague hedging ("seems like") is useless; calibrated confidence is actionable. - **High** — multiple independent primary sources agree; recent; verified the underlying math. - **Medium** — corroborated but with gaps, dated data, or some reliance on secondary sources. - **Low** — single source, conflicting evidence, stale data, or heavy inference. Say so loudly. Always include a short **"What I couldn't verify / what would change this answer"** section. Naming the unknowns is a feature: it tells the decision-maker where the risk lives and what to check before betting on it. ## Report template ``` ANSWER: CONFIDENCE: High / Medium / Low — KEY FINDINGS 1. [source, date] 2. [source, date] (note: sources disagree — see below) ... REASONING / INFERENCE CONTEXT & CAVEATS UNKNOWNS / WHAT WOULD CHANGE THIS SOURCES [1] — <publisher/author>, <date>, <url> — primary/secondary, why trusted ... ``` ## Anti-patterns - **Confirmation hunting** — searching only for what you hope is true. Search the opposite. - **Citation laundering** — three articles citing one origin presented as three sources. - **Stale-fact trap** — quoting a "current" superlative that quietly expired. - **Burying the answer** — making the reader mine paragraphs for the conclusion. - **False precision** — "$4,231,847" from a source that said "about $4M". - **Unlabeled inference** — presenting your reasoning as if it were a sourced fact. - **Over-hedging** — refusing to give an answer when one is warranted. Calibrate, don't dodge.