Roll up everything since 0.6.0 into the 0.7.0 CHANGELOG and bump the version
(pyproject / __init__ / config.app_version). Rewrite the stale Unreleased Grok
entry — which described the now-deleted opencode runtime — to the shipped
reality: Grok agents on xAI's official grok CLI on a SuperGrok subscription,
plus the token auto-refresh, the self-healing CI loop, the Company Scorecard,
and the pr-reviewer / observability / usage / path-injection fixes. Also folds
the uv.lock claude-agent-sdk spec sync (>=0.2.105) merged via #216.
Bump the version to 0.6.0 across pyproject, the package, the config, and
the panel, and add the 0.6.0 CHANGELOG entry: inbound external/internal PR
review with a CEO decision queue and supersede, the panel feature-flags
card, the required-cells decomposition gate, the CEO-rejected
coordination-root deadlock fix, the panel UI pass, and registry-image
deploy.
Also refresh the locked dependencies, update the release-tag examples in
the README and deployment docs, and correct the package docstring's agent
count to 22.
Markdown and editors soft-wrap on their own, so the manual ~75-char line
breaks across the docs added nothing but noise. Join wrapped prose, list
items, and paragraphs into single lines across 67 docs — README, CLAUDE.md,
deployment, usage, the RAG knowledge base, and the agent role prompts.
Whitespace-only: code fences, tables, and blockquote alerts are byte-identical
and the change is token-verified (no content altered). Applied with a
deterministic reflow tool (committed separately).
Also lands two doc edits that were awaiting commit: the measured under-load
resource numbers in usage.md and the pr_reviewer additions to the
org-structure RAG doc.
README + deployment guide now show three ways to run RoboCo — pull the
pre-built images (docker-compose.registry.yml), build from source, or the
local-dev flow — instead of only the dev path. Add the ROBOCO_REGISTRY /
ROBOCO_VERSION knobs and the required ROBOCO_ENCRYPTION_KEY (which was
missing) to .env.example, and fix a stale piragi reference there.
usage.md drops the unmeasured per-agent RAM ceiling and records the figures
measured on the live stack: RAM is low and Ollama-dominated, storage is the
real cost (images share the agent-base layer). Idle numbers — peak under an
active task is best read live with docker stats.
A documentation audit against the code surfaced several stale claims:
- Agent count: the roster is 19 AI agents (the UX/UI cell has two devs,
ux-dev-1 + ux-dev-2), not 18 / a single UX dev. Fixed in README,
CLAUDE.md, base.md, and docs/ux_ui.
- API: domain routes are mounted under /api, not /api/v1 (the /api/v1
prefix is the agent gateway only); dropped the non-existent /api/v1/test
group; fixed the orchestrator-status path in deployment.md.
- Quick Start uvicorn target is roboco.api.app:app (the api package
deliberately does not export app).
- Verb table: the developer PR verb is open_pr (renamed from
submit_for_qa); the lifecycle's canonical module is
foundation/policy/lifecycle.py (enforcement/task_lifecycle.py is a shim).
- Backend team stack: vector store is PostgreSQL + pgvector (via piragi),
not Qdrant; mypy targets roboco/, not src/.
- .env.example: replaced the phantom Qdrant/OpenAI blocks with the real
Ollama/RAG settings.
Renn F