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voyager67voyager67/AgentFieldkit/skills/research-evidence-map/SKILL.md

research-evidence-map

Skill0 starsChanged 13 days agoMITView on GitHub

Turn a technical research question into a traceable evidence map with source quality, uncertainty, and actionable next experiments. Use for model architecture choices, benchmark claims, library selection, and unfamiliar technical topics.

The file

We split this skill into the rules it gives an agent

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

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    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:

    Search authoritative sources first: original papers, official documentation, model cards, release notes, benchmark definitions, and source …

    line 11
  2. Distinctive:

    Never invent citations or imply a paper says something that only an abstract or secondary report says.

    line 22
  3. Distinctive:

    Do not treat leaderboard position as a complete measure of product fit.

    line 23
  4. Distinctive:

    If source access is unavailable, say what you could not verify.

    line 24

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

Research Evidence Map

Procedure

  1. Frame the question and decision it must support. Distinguish background curiosity from a choice that changes implementation.
  2. Search authoritative sources first: original papers, official documentation, model cards, release notes, benchmark definitions, and source code. Use community reports as useful but lower-confidence context.
  3. Record publication/release dates and the date of the measured event separately. Check whether information applies to the exact model/version/hardware being considered.
  4. Extract claims into a table: claim, evidence, source, date/version, applicability, confidence, and counterevidence.
  5. Separate direct measurements from author claims, marketing statements, extrapolation, and your own inference.
  6. Identify missing comparisons, benchmark contamination risks, undisclosed settings, and reproducibility gaps.
  7. End with the smallest experiment or code inspection that could change the decision. State what evidence would reverse the recommendation.

Deliverable

Question, decision criteria, evidence table, uncertainty and counterarguments, recommendation, and next validation step.

Boundaries

  • Never invent citations or imply a paper says something that only an abstract or secondary report says.
  • Do not treat leaderboard position as a complete measure of product fit.
  • If source access is unavailable, say what you could not verify.

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Your trail

Files you open will collect here, so you can find them again.

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