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ek33450505ek33450505/claude-agent-team/skills/deep-research/SKILL.md

deep-research

Skill8 starsChanged 32 days agoMITView on GitHub

Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report. When the user wants a deep, multi-source, fact-checked research report on any topic. BEFORE invoking, check if the question is specific enough to research directly — if underspecified (e.g., "what car to buy" without budget/use-case/region), ask 2-3 clarifying questions to narrow scope. Then pass the refined question as args, weaving the answers in.

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.

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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

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 177 edited versions we read keep these.

  • 2

    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:

    Scope-check first: If the question is underspecified (missing budget, region, use-case, timeframe, etc.), ask the user 2-3 clarifying …

    line 26
  2. Distinctive:

    Honest failure is the contract: When the service is busy the harness returns *unverified* claims and says so.

    line 80

“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 Skill

A multi-agent research harness: it decomposes a question into search angles, fans out parallel web searches, fetches and extracts falsifiable claims from sources, adversarially verifies every claim with a 3-vote skeptic panel, and synthesizes a cited report.

The workflow lives in deep-research.workflow.js next to this file. It is the single source of truth for the harness — edit it there, then (if you keep a repo copy) run bash install.sh to redeploy.

Usage

/deep-research <your research question>

How to run it

  1. Scope-check first. If the question is underspecified (missing budget, region, use-case, timeframe, etc.), ask the user 2-3 clarifying questions before running. Then fold the answers into a single refined question string.
  2. Invoke the workflow via the Workflow tool, pointing scriptPath at the workflow file in this skill's directory and passing the refined question as args:

    Workflow({
      scriptPath: "~/.claude/skills/deep-research/deep-research.workflow.js",   // expand ~ to $HOME
      args: "<the refined research question>"
    })

    Pass the question as a plain string in args (not JSON-encoded). The workflow reads it via the global args. This skill's instruction to call Workflow is itself the explicit opt-in for multi-agent orchestration.

  3. Relay the result. The workflow returns { summary, findings, refuted, unverified, sources, stats }. Present the summary and findings with their sources; surface unverified claims clearly tagged as unverified (verification couldn't render a verdict — NOT refuted), and keep refuted claims in a transparency section.

Output contract (important)

The harness reports three claim outcomes, never two:

  • confirmed — a quorum of valid votes, fewer than 2 refuting.
  • refuted — a quorum of valid votes with ≥2 actually voting to refute.
  • unverified — too few valid verdicts to adjudicate (agent/tooling failure or abstentions). These are returned with their sources and an explicit "unverified" tag. Never present an unverified claim as confirmed, and never report it as refuted. A run where verification couldn't execute returns the extracted claims marked unverified — it does not collapse to an empty findings list with a "refuted" headline.

stats.fallbacksFired / stats.fallbackRecovered show how often a low-fan-out subagent (scope/ search/fetch/synthesize) failed to emit structured output and the free-text→structure fallback recovered it. A high unverified count with confirmed: 0 means verification couldn't run (usually server load) — say so plainly when relaying results; do not dress it up as a finding.

Design notes

  • Cost scales with the question, dialed back for load (2026-06-08). Default fan-out: 5 angles, ≤10 fetched sources, top-12 claims × 3 verifier votes (~50–65 subagent calls). Verify is the heaviest phase, so it runs in sequential batches (VERIFY_BATCH claims each, direct call) to pace requests under the server's transient rate limiter — each batch fully drains before the next.
  • Resilience, by phase:
    • Verify uses a direct structured call with no fallback amplification. A live post-mortem (run wf_2b84c096) showed the free-text→structure fallback only multiplied load under rate-limiting (75 votes → 225 agents, 0 recovered). A failed/rate-limited vote → abstain → the claim is unverified (never refuted) — honest degradation, not a fabricated verdict.
    • Scope / Search / Fetch / Synthesize (low fan-out) keep structuredWithFallback(): if a subagent does its web work then ends without StructuredOutput, the work is re-run as a free-text pass and a structure-only agent renders it — so one hiccup doesn't drop a good source.
  • Honest failure is the contract. When the service is busy the harness returns unverified claims and says so; it does not fabricate confirmations or refutations. See the two 2026-06-08 post-mortem notes in the workflow header.

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