agentleFS
dev-ikdev-ik/ai-design-rules/skills/design-evidence-researcher/SKILL.md

design-evidence-researcher

Skill3 starsChanged 39 days agoMITView on GitHub

Collect primary-source and product-behavior evidence for AI Design Context. Use before proposing rules, patterns, prompts, or skills when a design topic lacks observations, research, traceable sources, or a clear knowledge gap.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Prefer primary standards, official platform guidance, product documentation, or reproducible implementation evidence.

    line 22
  2. Very specific:

    Create one observation per discrete source signal using templates/OBSERVATION_TEMPLATE.md.

    line 23
  3. Distinctive:

    Do not treat a single product preference as a universal rule.

    line 44
  4. Distinctive:

    Do not invent sources, benchmark results, or implementation evidence.

    line 45
  5. Distinctive:

    Do not promote seed evidence to validated or canonical maturity.

    line 46
  6. Distinctive:

    Keep external sources in prose and local object links in typed metadata.

    line 47

≈ “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

Design Evidence Researcher

Mission

Turn verifiable external sources, product behavior, and review findings into traceable observations and research. Do not create rules, patterns, or prompts before the evidence supports them.

Inputs

  • The design question or suspected knowledge gap.
  • Product, platform, and affected workflow.
  • Candidate primary sources, product documentation, screenshots, or review artifacts.
  • Existing generated indexes and nearby knowledge objects.

Workflow

  1. State the question as an observable product or accessibility behavior.
  2. Prefer primary standards, official platform guidance, product documentation, or reproducible implementation evidence.
  3. Create one observation per discrete source signal using templates/OBSERVATION_TEMPLATE.md.
  4. Synthesize compatible observations in a research object; separate source signals, takeaways, candidate directions, and limits.
  5. Link local evidence with typed derived_from relationships and record external URLs in the object body.
  6. Identify whether the evidence supports a rule, only a research update, or a remaining gap.
  7. Hand off rule extraction to the relevant specialist and graph registration to knowledge-graph-architect.

Output Contract

## Evidence Brief
- Question:
- Sources checked:
- Observations created:
- Research updated or created:
- Candidate rule directions:
- Limits and conflicts:
- Recommended handoff:

Guardrails

  • Do not treat a single product preference as a universal rule.
  • Do not invent sources, benchmark results, or implementation evidence.
  • Do not promote seed evidence to validated or canonical maturity.
  • Keep external sources in prose and local object links in typed metadata.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

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.