Fixes#757
## Changes
Separate operations from code into {project}-ops repo pattern. Added OPS_REPO_ROOT infrastructure (env.sh, load-project.sh, formula-session.sh with ensure_ops_repo helper). Updated all 8 agent scripts and 7 formulas to read/write vault items, journals, evidence, prerequisites, RESOURCES.md, and knowledge from the ops repo. Added setup_ops_repo() to disinto init for automatic ops repo creation and seeding. Removed migrated data from code repo (vault data dirs, planner journal/memory/prerequisites, supervisor journal/best-practices, evidence, RESOURCES.md). Updated all documentation. 55 files changed, ShellCheck clean, all 38 phase tests pass.
Co-authored-by: openhands <openhands@all-hands.dev>
Reviewed-on: https://codeberg.org/johba/disinto/pulls/767
Reviewed-by: Disinto_bot <disinto_bot@noreply.codeberg.org>
Add mandatory Addressables and Observables sections to AGENTS.md so all
agents have a concrete inventory of what the factory has produced.
- AGENTS.md: add Addressables table (website, repo, skill, GitHub org)
and empty Observables section
- run-gardener.toml: add portfolio lifecycle duties (add, promote,
remove, flag) to the grooming step
- run-planner.toml: reference portfolio as planning input
- run-predictor.toml: reference portfolio for weakness detection
- Add lib/tea-helpers.sh with tea_file_issue, tea_relabel, tea_comment,
tea_close — thin wrappers preserving secret scanning on write ops
- Add tea 0.9.2 binary to docker/agents/Dockerfile
- Configure tea login in docker/agents/entrypoint.sh from FORGE_TOKEN/FORGE_URL
- Derive TEA_LOGIN in lib/env.sh (codeberg vs local forgejo)
- Source tea-helpers.sh conditionally when tea binary is available
- Migrate predictor formula from inline curl to tea CLI commands
- Register tea-helpers.sh in smoke test function resolution
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add lib/build-graph.py that builds a NetworkX DiGraph from project docs
and forge API, runs structural analyses (orphans, cycles, disconnected
clusters, thin objectives, bottlenecks), and outputs a JSON report.
Predictor and reviewer agents now call build-graph.py before launching
their Claude sessions and inject the report as context.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add a re-evaluate-backlog step to the predictor formula between
collect-signals and analyze-and-predict. For each open prediction/backlog
issue, the predictor now reads the original context and planner comments,
extracts the assumptions that made it "watch, don't act", and re-checks
those conditions against current system state.
Three outcomes:
- CONDITIONS_CHANGED → file new prediction/unreviewed, close old as superseded
- STALE (30+ days, conditions stable) → close as prediction/actioned
- UNCHANGED_RECENT → skip (existing behavior)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
