tracedocs

# 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").