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).
When to Use This Skill
- Onboarding to, or documenting, an unfamiliar codebase
- Producing durable, in-repo docs for operation, deployment, and maintenance
- Preparing an AI-agent-ready knowledge handoff (with
index.json) - Turning a repo into a study guide whose claims are traceable to source
What This Skill Does
- Analyzes the codebase (stack, scripts, entry points, env-var names, deploy signals, tests).
- Builds an evidence map (source map, assumptions, generation log) with confidence labels.
- Writes the Markdown manuals and an
index.jsonmanifest; never invents deployment steps. - Runs a quality check (paths exist, commands sourced, no secret values, gaps documented).
How to Use
Basic Usage
Use tracedocs to generate evidence-grounded study docs for this repository. Write the output to study-docs/.
Example
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").