Turn a codebase into Markdown guides for operation, deployment, architecture, and maintenance, plus an index.json for AI agents. Operational claims include source references and confidence labels, while unverified deployment details remain documented gaps.
Turn any codebase into evidence-grounded Markdown docs plus a machine-readable index.json. Every claim cites its source; never invents deployment steps.
Review the instructions the agent will receive before you install.
Turn any codebase into an evidence-grounded documentation package (overview,
operation, deployment, learning, architecture, API/data, troubleshooting,
maintenance) plus a machine-readable index.json for AI agents. Every
operational/deployment claim cites a source file and a confidence label
(Verified / Inferred / Unknown / Needs confirmation); it never invents
deployment steps and records gaps instead.
Full skill, references, templates, and a validated sample output: https://github.com/wxggzz/tracedocs (MIT).
index.json)index.json manifest; never invents
deployment steps.Use tracedocs to generate evidence-grounded study docs for this repository. Write the output to study-docs/.
User: "Document ./my-app with tracedocs"
The skill scans the repo and writes a study-docs/ package (00-10 manuals +
index.json + _evidence/), citing each operational claim's source and
labelling its confidence - and explicitly noting anything it cannot verify
(for example, "no deployment configuration found in the repo").
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