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runxhqrunxhq/runx/skills/deep-research/SKILL.md

deep-research

Skill97 starsChanged 33 days agoApache-2.0View on GitHub

Produce a decision-ready deep-research brief from bounded governed evidence and preserve every material source binding.

196 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

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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    Also in 5 projects that use this

  • nicepkgSkill

    ph-community-outreach

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  • W-MoorerSkill

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  • W-MoorerSkill

    academic-pipeline

    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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  • stophobiaSkill

    surprise-me

    Create a delightful, unexpected "wow" experience for the user by dynamically discovering and creatively combining other enabled skills. Triggers when the user says "surprise me" or any request expressing a desire for an unexpected creative showcase. Also triggers when the user is bored, wants inspiration, or asks for "something interesting".

    Also in 4 projects that use this

  • +4 more from nicepkg
  • +1 more from W-Moorer

The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 4 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 177 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use deep-research for architecture choices, market or ecosystem questions, product bets, trust decisions, or other consequential analysis …

    line 28
  2. Distinctive:

    Do not use this skill for an unbounded literature review, a daily trend recap, or research whose sources have not been fetched through a …

    line 34
  3. Very specific:

    Only a ready research packet proceeds to ghostwrite.

    line 45
  4. Distinctive:

    Stop before drafting when no valid source survives research admission.

    line 72
  5. Distinctive:

    Do not turn operator preference into a verified market or product fact.

    line 74
  6. Distinctive:

    Do not add a publish or delivery state to a local brief.

    line 75

“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

Turn one important question into a durable operator brief: what the answer is, what evidence supports it, what remains uncertain, and what posture the reader should take next. Use this when a quick answer is too shallow but an open-ended report would obscure the decision.

The output should feel like a thoughtful memo, not a narration of the research process. It leads with the decision and operational implication, then exposes the evidence, inference, alternatives, and unresolved questions that justify that posture.

Composes

<!-- Generated from the native execution closure; run pnpm core-skills:composes:generate. -->
  • ghostwrite#draft
  • research#local-files
  • research#research

When to use it

Use deep-research for architecture choices, market or ecosystem questions, product bets, trust decisions, or other consequential analysis that needs a reader-ready synthesis. Use plain research when the evidence packet itself is the desired artifact. Use content-pipeline when the primary outcome is public content for a known channel.

Do not use this skill for an unbounded literature review, a daily trend recap, or research whose sources have not been fetched through a governed reader.

How the chain works

  1. The caller supplies the exact question and audience plus either governed source packets or bounded local paths. The local-files runner reads those paths through Runx's native filesystem boundary; it does not tunnel local evidence through HTTP.
  2. The canonical research skill admits and indexes those sources, separates evidence from inference, and verifies every citation and recommendation.
  3. Only a ready research packet proceeds to ghostwrite. The writing stage turns decision support into a clear brief without introducing unsupported facts.
  4. The final artifact preserves the research and content packet bindings. It remains local and is not a publication claim.

Local research and drafting need no approval because they do not cross an external boundary. If the brief is later sent, posted, or published, the provider delivery skill owns approval, idempotency, acknowledgement, and readback.

Inputs and result

  • objective is the exact decision question.
  • source_packets are bounded governed evidence. The local-files runner accepts bounded paths below a selected local root instead. Operator context, URLs, bare paths, and model memory do not substitute for admitted evidence.
  • audience and channel shape the memo's presentation.
  • domain, operator_context, and target_entities keep the analysis scoped but do not count as evidence.

The result contains the citation-validated research_packet and, when evidence is ready, the evidence-bound content_draft_packet. It may finish at needs_more_evidence or not_worth_publishing rather than manufacture a memo.

Stop conditions

  • Stop before drafting when no valid source survives research admission.
  • Preserve uncertainty that could change the recommendation.
  • Do not turn operator preference into a verified market or product fact.
  • Do not add a publish or delivery state to a local brief.
  • Route any outward action to its owning provider skill with the exact artifact digest and evidence packet intact.

Example

For “Should this provider become a native Runx tool?”, the source set might include its official API contract, two existing consumer implementations, and runtime measurements. The brief should recommend native ownership, package ownership, or no change; show which evidence supports that boundary; and name the remaining operational risk. It should not become a generic provider profile or imply adoption merely because the API exists.

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