agentleFS
kuya-eggkuya-egg/Monozukuri/genchi-genbutsu/SKILL.md

genchi-genbutsu

Skill11 starsChanged 2 months agoMITView on GitHub

Inspect the real repository, environment, dependencies, and runtime evidence before designing or changing software. Use for greenfield construction, unfamiliar codebases, uncertain behavior, or any task where assumptions could create rework.

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 8 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈8

    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:

    Follow the relevant input through callers, transformations, side effects, storage, external calls, and failure paths.

    line 48
  2. Distinctive:

    Run the narrowest useful existing check: a reproduction, focused test, type check, build, benchmark, health check, or representative …

    line 54
  3. Distinctive:

    Read the project's actual usage and authoritative dependency documentation when behavior is uncertain.

    line 61
  4. Very specific:

    Every important claim must be traceable to a file, command, test result, runtime observation, or authoritative source.

    line 73
  5. Distinctive:

    Do not read the entire repository when a bounded slice answers the question.

    line 79
  6. Distinctive:

    Do not treat comments, issue text, or AI output as stronger than behavior.

    line 80
  7. Distinctive:

    Do not modify code during reconnaissance except for a clearly labeled, minimal probe that is required to observe the system.

    line 81
  8. Distinctive:

    Do not convert an absence of evidence into evidence of absence.

    line 83
  9. Distinctive:

    If a referenced skill is not installed, apply its named lens inline instead of trying to invoke it.

    line 91

≈ “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

Genchi Genbutsu

Genchi genbutsu means going to the source. In software, make decisions from the actual repository, environment, runtime behavior, and authoritative documentation instead of from memory or plausible-looking generated output. This is a Japanese-inspired engineering metaphor, not a cultural universal.

Use this skill when

  • the codebase or toolchain is unfamiliar;
  • a project is being built from scratch;
  • the request references behavior that has not been observed;
  • an API, dependency, framework, or command is uncertain;
  • a bug or performance issue needs evidence; or
  • a previous attempt relied on assumptions and created rework.

Outcome

Produce a bounded reconnaissance report with:

  • the relevant repository or environment surface;
  • existing implementations, consumers, and conventions;
  • commands, tests, builds, or runtime observations used as a baseline;
  • constraints and failure boundaries;
  • facts separated from inference;
  • unknowns that still need resolution; and
  • the narrowest safe seam for the next change.

For greenfield work, the report covers the domain, users, deployment target, available services, language and toolchain constraints, operational ownership, and the smallest behavior worth proving first.

Workflow

1. Map the surface

Inspect the directory structure, entrypoints, package manifests, lockfiles, configuration, schemas, routes, commands, tests, and deployment files relevant to the request. Search before creating a new helper, module, route, or service.

2. Trace behavior

Follow the relevant input through callers, transformations, side effects, storage, external calls, and failure paths. Identify who depends on observable behavior, including behavior that is not documented.

3. Establish a baseline

Run the narrowest useful existing check: a reproduction, focused test, type check, build, benchmark, health check, or representative command. Record the exact command and result. If no check exists, state that explicitly and create the smallest observation needed to reduce uncertainty.

4. Verify external facts

Read the project's actual usage and authoritative dependency documentation when behavior is uncertain. Never invent an API from a familiar name or copy a snippet without reconciling versions and local conventions.

5. Report and stop

Return only findings relevant to the request. Name the evidence, assumptions, unknowns, risks, and recommended next seam. Do not begin unrelated cleanup because reconnaissance exposed it.

Evidence standard

Every important claim must be traceable to a file, command, test result, runtime observation, or authoritative source. Say not verified when evidence is missing. A clean report is more valuable than a confident guess.

Boundaries

  • Do not read the entire repository when a bounded slice answers the question.
  • Do not treat comments, issue text, or AI output as stronger than behavior.
  • Do not modify code during reconnaissance except for a clearly labeled, minimal probe that is required to observe the system.
  • Do not convert an absence of evidence into evidence of absence.

Handoff

Hand the next engineer a compact map of the system and a baseline they can rerun. Recommend nemawashi if intent remains unclear, kanso if design choices remain, or kata when the next slice is ready to implement.

If a referenced skill is not installed, apply its named lens inline instead of trying to invoke it.

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.