Deep research
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.From its SKILL.md
npx -y skills add SkillMedev/ai-engineer-toolkit --skill deep-researchAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
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SKILL.md
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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
- The research question, restated until the user confirms it. Ambiguity here multiplies through every later step.
- What decision or output the research feeds. A brief informing a purchase differs from one informing a publication.
- Scope boundaries: geography, industry, population, and what is explicitly out of scope.
- Depth and deadline. Default: 3-6 sub-questions, at least two independent sources per sub-question.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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).
- 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.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.