tracedocs

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

1. Analyzes the codebase (stack, scripts, entry points, env-var names, deploy signals, tests).
2. Builds an evidence map (source map, assumptions, generation log) with confidence labels.
3. Writes the Markdown manuals and an index.json manifest; never invents deployment steps.
4. 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").