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.
In this repository25 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agent-skill-auditSkill
- api-contract-checkSkill
- build-rescueSkill
- change-narrativeSkill
- context-handoffSkill
- data-quality-gateSkill
- dataset-workshopSkill
- dependency-careSkill
- docs-syncSkill
- experiment-ledgerSkill
- failure-forensicsSkill
- model-diff-reviewSkill
- model-eval-designSkill
- multi-agent-crosscheckSkill
- parallel-task-designSkill
- performance-proofSkill
- release-readinessSkill
- repo-scoutSkill
- review-lensSkill
- safe-migrationSkill
- security-surfaceSkill
- spec-firstSkill
- sql-integrity-checkSkill
- test-driven-changeSkill
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
- Distinctive: line 11
Search authoritative sources first: original papers, official documentation, model cards, release notes, benchmark definitions, and source …
- Distinctive: line 22
Never invent citations or imply a paper says something that only an abstract or secondary report says.
- Distinctive: line 23
Do not treat leaderboard position as a complete measure of product fit.
- Distinctive: line 24
If source access is unavailable, say what you could not verify.
≈ “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.
Files
Research Evidence Map
Procedure
- Frame the question and decision it must support. Distinguish background curiosity from a choice that changes implementation.
- 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.
- Record publication/release dates and the date of the measured event separately. Check whether information applies to the exact model/version/hardware being considered.
- Extract claims into a table: claim, evidence, source, date/version, applicability, confidence, and counterevidence.
- Separate direct measurements from author claims, marketing statements, extrapolation, and your own inference.
- Identify missing comparisons, benchmark contamination risks, undisclosed settings, and reproducibility gaps.
- 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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
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- It didn't
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

