workspace-metabolism v0.2.0

workspace-metabolism v0.2.0

One policy file controls the whole lifecycle of files in an AI-driven
workspace: classify, audit, clean (recyclable), rollback, and purge — every
step leaves a hash-chained audit trail. Python 3.11+, zero dependencies,
Windows / Linux / macOS.

The one-liner: loops keep the agent running; metabolism keeps the workspace
alive.

What's new in v0.2.0

• wm init: scaffold a metabolism.json policy file like git init.
  The tool scans your workspace and grades common directories (source/docs
  keep, logs/tmp/cache auto, archive/staging approve).
• Auto-discovery: metabolism.json / .wm.json in the workspace root are
  found automatically, so --registry is optional.
• Policy JSON Schema in schema/metabolism.schema.json; new optional
  governance fields: owner, intent, review_after.
• wm explain <path>: the nutrition label for any path — what grade,
  what retention, whether it is a candidate right now, and why.
• wm health: a 0-100 workspace health score (auditability 25,
  governance 25, rot burden 35, recycle readiness 15) with --json and
  --badge (shields.io) output.
• wm mcp: a zero-dependency MCP stdio server so agents can run
  micro-metabolism themselves. Clean stays dry-run unless the caller
  explicitly passes execute=true.
• Ritual and CI: examples/micro_metabolism.py (the end-of-loop
  question) and examples/ci-audit.yml (a weekly health gate that fails
  below a configurable score).
• Community: ROADMAP, CONTRIBUTING, issue templates, an English essay
  (docs/publish/agentic-metabolic-engineering-essay.md) and a stack diagram
  in the narrative.

What's unchanged

• Safety model: clean is dry-run by default; G3 needs --approve +
  --approver; rollback verifies per-file SHA-256 and refuses to overwrite;
  purge is the only real delete, retention-gated and recycle-area-only.
• Zero dependencies; CI on Ubuntu / Windows / macOS (Python 3.11 & 3.12).

Install: pip install workspace-metabolism

Docs: README ·
philosophy ·
narrative