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SkillMedevSkillMedev/ai-engineer-toolkit/skills/deep-research/SKILL.md

deep-research

Skill0 starsChanged 38 days agoMITView on GitHub

Runs a thorough multi-source research process - decompose the question, gather independent sources, cross-check, synthesize - and delivers a structured, cited brief with confidence levels and gaps. Use when someone asks "research this topic in depth", "give me everything on X", "what does the evidence say about Y", or needs a broad question answered from secondary sources with citations. Do NOT use for designing a study that collects new data - use primary-research instead; for distilling an already-gathered pile of 20+ sources into themes, use research-synthesis; for verifying one discrete claim, use fact-checker.

196 other projects use a version of this.

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Projects with it also use community-led-growth, ph-community-outreach and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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    Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

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  • +4 more from nicepkg
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The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 177 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Do not invent or embellish a citation - one fabricated source invalidates the entire brief.

    line 57
  2. Distinctive:

    Do not count correlated sources as corroboration.

    line 58
  3. Distinctive:

    Do not average disagreeing sources into a mushy middle.

    line 59
  4. Distinctive:

    Do not let the gathering order set the narrative - the first three sources found are the most findable ones, not the most representative.

    line 60
  5. Distinctive:

    Do not blur evidence and inference.

    line 61

“Common” is measured: kept by at least 6 in 10 of the 177 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Deep Research

Go wide, then deep: triangulate across independent sources and report what was actually found, including the gaps. The costly failure this skill prevents is the confident brief built on an echo - a dozen articles that all trace to one blog post or one press release, presented as consensus. "Not found" is a legitimate finding; a smooth answer papering over thin evidence is not.

Inputs to collect

  1. The research question, restated until the user confirms it. Ambiguity here multiplies through every later step.
  2. What decision or output the research feeds. A brief informing a purchase differs from one informing a publication.
  3. Scope boundaries: geography, industry, population, and what is explicitly out of scope.
  4. Depth and deadline. Default: 3-6 sub-questions, at least two independent sources per sub-question.
  5. Known sources or prior work to build on. Treat supplied sources as leads to appraise, not settled evidence.

Label any assumed scope as an assumption.

Operating procedure

Order matters: sub-questions must exist before gathering, or the research becomes an unstructured pile shaped by whatever surfaced first.

  1. Frame. Restate the question and define what a good answer looks like - the form (a number, a ranked list, a causal story) and the bar (how sure it needs to be).
  2. Map. Decompose into 3-6 sub-questions and research each one, rather than the topic as a blob. If a sub-question splits again, it was two sub-questions.
  3. Gather. Collect diverse, independent sources per sub-question; prefer primary sources - original data, papers, filings, direct statements - over summaries. Two sources that cite the same origin are one source. Record recency and potential bias for each as you go, not retroactively.
  4. Cross-check. For each sub-question, mark where sources agree, disagree, or run thin. Disagreement is signal: hypothesize why (different definitions, populations, time periods, incentives) instead of averaging it away.
  5. Synthesize. Answer the original question with the weight of evidence. Separate evidence from inference and label the inferences. Assign each finding a confidence level: High (multiple independent primary sources agree), Medium (mixed or secondary-heavy support), Low (single source or inference).
  6. Audit before delivery. Re-check each citation is real and locatable, and that every High-confidence finding actually has the sourcing it claims.

Worked example (findings-table format)

Question: "Is four-day-workweek adoption growing among knowledge-work employers, and what happens to output?"

Sub-question            Finding                                   Confidence  Sources
Adoption trend          Growing from a small base; most           Medium      Coordinated-trial reports (primary);
                        employers remain on 5 days                            national labor-survey data (primary)
Output effect           Trials report maintained revenue and                  Trial-organization data (primary);
                        lower attrition - but trial firms         Medium      independent academic follow-up
                        self-selected, so effects may not
                        generalize
Who it fails for        Thin evidence for client-facing and       Low         Single practitioner survey; rest is
                        shift-based work                                      anecdote - GAP: no controlled study found

Note the third row: the honest output is a labeled gap, not a stretched conclusion.

Deliverable

Produce a research brief containing: a summary answering the question in a few sentences up top; findings organized by sub-question, each with citations and a confidence tag; a confidence-and-gaps section stating what is well-supported, what is uncertain, and what is unknown; and a source list with enough detail to locate each item plus a one-line note on its independence and bias.

Do NOT

  • Do not invent or embellish a citation - one fabricated source invalidates the entire brief. Never cite something not actually consulted.
  • Do not count correlated sources as corroboration; trace what each actually relies on before calling anything consensus.
  • Do not average disagreeing sources into a mushy middle. Present the disagreement and the best hypothesis for why it exists.
  • Do not let the gathering order set the narrative - the first three sources found are the most findable ones, not the most representative.
  • Do not blur evidence and inference. A reader must be able to tell which sentences report sources and which report reasoning.

Quality bar

Before shipping: every sub-question has at least two genuinely independent sources or an explicit gap note; every finding carries a confidence tag consistent with its sourcing; disagreements are surfaced with a hypothesis, not smoothed over; the summary is answerable-from and consistent with the findings; every citation is locatable from the detail given.

Escalation

Route adjacent jobs: designing a study to collect new data is primary-research; compressing an existing corpus of sources into themes and tensions is research-synthesis; a single checkable claim is fact-checker; academic-literature-specific reviews are literature-review or systematic-review. Findings that will drive legal, medical, or financial decisions need professional review before action.

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