taar-agent-taskforce 0.1.0

TAAR — Thirsty's Active Agent Runner: a local-first governed agent taskforce for Project-AI, with a GitHub Workflow Guardian.

TAAR — Thirsty's Active Agent Runner

A local-first, governed agent taskforce. Part of the Project-AI constitutional architecture. TAAR runs micro-agents against a repository under strict registry law: one task per agent, minimal authority, maximum evidence, fail closed, everything classified. Includes a GitHub Workflow Guardian that verifies GitHub Actions workflows before they run.

What TAAR does

• Runs reader agents that inspect your repo and facility (git state, secrets, stale paths, lint/type/test surface, GitHub workflows) and produce hash-sealed evidence bundles.
• Runs writer agents that turn validated evidence into Markdown reports and digests — every report cites its source evidence hash.
• Keeps an append-only audit spine (JSONL, hash per record) of every admission, denial, success, failure, and classification escalation.
• Classifies everything: OPEN < CONTROLLED < RESTRICTED < SECRET < PHANTOM < BLACK. Secrets are redacted at capture. Artifacts with no producing run record are phantoms and turn the lane BLACK.
• Detects unsafe GitHub Actions patterns: broad permissions, secret echoing, pull_request_target risk, unpinned actions, shell injection, self-hosted/privileged runners, ungated deploys.

What TAAR does NOT do

The first swarm is report-only. It cannot and will not: merge, push, publish, deploy, delete, apply patches, create branches, or mutate source. These capabilities are rejected at registry validation — the constraint is structural. taar workflows harden prints draft suggestions and writes no patch files.

Install

pip install taar-agent-taskforce

Not yet on PyPI — until the first release ships, install from a checkout:

pip install .

Python 3.12+. Dependencies: pyyaml, typer, rich.

Quick start

cd your-repo
taar init                      # seed registry/ + taar.toml (explicit, refuses overwrite)
taar status                    # facility mode, 44 agents, validation state
taar run heartbeat-reader      # evidence bundle
taar run heartbeat-report-writer   # report citing the evidence hash
taar evidence                  # list bundles
cat .project-ai/automation/reports/facility/heartbeat-latest.md

Workflow Guardian

taar workflows explain         # what your workflows can do
taar workflows scan            # findings (exit 1 on critical)
taar workflows classify        # one-word risk verdict
taar workflows harden          # draft suggestions, nothing applied
taar workflows evidence        # governed run with full evidence bundle

Or in CI via the composite action — see action.yml and docs/TAAR_FOR_GITHUB_ACTIONS.md.

First swarm demo

for a in heartbeat-reader heartbeat-report-writer git-status-reader \
         git-status-writer phantom-reader phantom-report-writer \
         governance-reader governance-digest-writer workflow-reader \
         workflow-report-writer; do taar run "$a"; done
taar status && taar quarantine

How governance works

Every run passes one gate (taar/executor.py): registry valid → agent and task known and enabled → facility mode permits → capabilities granted → every command declared, allowed, and granted (exact match) → output paths in bounds → writer evidence present, hash-valid, and classification admissible → no lock conflict. Any failure denies, and the denial is audited. BLACK/PHANTOM evidence feeds no writer except the explicitly granted handler. SECRET evidence feeds only the declared secret writer, redacted.

Layout

taar/       runtime and CLI          registry/   the governed universe
checks/     reader built-ins         writers/    writer built-ins
tests/      87 contract tests        docs/       the canon
examples/   real evidence samples + workflow examples
scripts/    registry generator + Windows scheduler scripts

Docs

Start with docs/TAAR_REBUILD_DIRECTIVE.md, then docs/LOCAL_AGENT_RULEBOOK.md, docs/RUNNER_CONTRACT.md, docs/CLASSIFICATION_MODEL.md, and docs/GITHUB_WORKFLOW_GUARDIAN.md.

License

MIT. Author: Jeremy "Thirsty" Karrick — Project-AI / Thirsty's Projects LLC.