# pragent pilot — AI Review bot

A minimal AI code-review bot for Gitea, running as a CI step on the existing
`act-runner`. This is the **pilot** — a small, self-contained reviewer that
predates the full `pragent` framework (whose design lives in
`docs/plans/2026-08-04-pragent-design.md`). The framework will later absorb
this; until then, this is what runs.

## How it works

1. You add `pragent-bot` to a repo and commit `.gitea/workflows/ai-review.yml`.
2. On a PR, you add the **`AI-REVIEW`** label.
3. Gitea Actions runs the workflow on the `act-runner`; it fetches the PR diff,
   asks `glm-5.2:cloud` (on-network via the headroom proxy) to review it, and
   posts the findings back as a PR review authored by `pragent-bot`.
4. Remove the label to stop re-reviews on further pushes.

Fail-open: the job always exits 0 and never blocks CI. Errors become a short
"review failed" comment.

## Onboard a repo (3 steps)

### 1. Add `pragent-bot` as collaborator

Repo → Settings → Collaborators → Add → `pragent-bot` → permission **Write**.
(Write is required to post reviews/comments.)

Or via API (with an admin/owner token):

```bash
curl -X PUT -H "Authorization: token $OWNER_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"permission":"write"}' \
  "http://100.74.17.70:30000/api/v1/repos/OWNER/REPO/collaborators/pragent-bot"
```

### 2. Add the `PRAGENT_BOT_TOKEN` secret

Repo → Settings → Actions → Secrets → New secret → name `PRAGENT_BOT_TOKEN`,
value = the bot's access token (ask the platform admin; stored mode-600 at
`~/.claude/.pragent-bot-token` on the admin host).

### 3. Commit the workflow

Copy `pilot/workflow-template.yml` into the target repo as
`.gitea/workflows/ai-review.yml` and commit it. That's it.

## Use it

Open a PR (or push to an open one), add the **`AI-REVIEW`** label. The review
appears within ~30–90s depending on diff size and model latency.

## What's intentionally NOT in the pilot

Deferred to the full framework (by design, see the design doc):

- Attention tiering (trivial/lite/full/oversized) and per-tier cost control.
- Multiple analyzer fan-out over a shared cached prompt prefix.
- Prior-comment synthesis (so each push re-posts; the latest review is tagged
  with the head SHA so it's easy to spot).
- Inline line comments and status checks.
- `pragent explain` / `replay` / analytics JSONL.
- A second forge (GitLab) and the provider matrix.

## Pieces

| File | Role |
|---|---|
| `pilot/ai_review.py` | The reviewer script (stdlib only). Single source of truth — fetched at runtime by each repo's workflow. |
| `pilot/workflow-template.yml` | The Gitea Action consumers copy into `.gitea/workflows/ai-review.yml`. |
| `tests/pilot/test_ai_review.py` | Unit tests for the pure helpers (no network). |

## Run the tests

```bash
cd ~/Projects/pragent
PYTHONPATH=pilot python3 -m pytest tests/pilot/    # if pytest available
# or, without pytest:
python3 - <<'PY'
import os, sys, importlib.util
sys.path.insert(0, os.path.abspath("pilot"))
import ai_review  # noqa: F401
spec = importlib.util.spec_from_file_location("t", "tests/pilot/test_ai_review.py")
m = importlib.util.module_from_spec(spec); spec.loader.exec_module(m)
fails = 0
for n in sorted(x for x in dir(m) if x.startswith("test_")):
    try: getattr(m, n)(); print("PASS", n)
    except Exception as e: fails += 1; print("FAIL", n, e)
print("failed:", fails)
PY
```

## Configuration knobs (env in the workflow)

| Env | Default | Purpose |
|---|---|---|
| `OLLAMA_MODEL` | `glm-5.2:cloud` | Model id passed to the headroom proxy. |
| `OLLAMA_MAX_TOKENS` | `6000` | Output token cap. |
| `DIFF_MAX_CHARS` | `150000` | Diff truncation cap (with a noted truncation marker). |
| `OLLAMA_URL` | `http://100.74.17.70:8789` | headroom proxy (tailnet). If the act-runner can't reach the tailnet IP, expose 8789 as an in-cluster Service+Endpoints and set this to the cluster DNS name. |