Marcos
30d2a3d7da
Skills — the primary now loads conditionally (each one is input tokens), per a load table in pragent.md: - attention-tiering: classify every PR trivial/lite/full/oversized BEFORE reading anything, and cap file reads, linter runs and subagent fan-out per tier. This is the cost governor; the other skills defer to its budget. - linter-playbook: per-ecosystem detect-and-run commands scoped to changed files, the never-install rule, and how to turn a diagnostic into a finding instead of pasting tool output. - security-lens: the inline security checklist for when @security isn't worth delegating, built around a source -> sink test each finding must pass. - malicious-change: hostile-PR detection — injection aimed at the reviewer, install/CI-time hooks, obfuscated payloads, dependency confusion, logic backdoors. Complements the runtime containment added in the previous commit: that stops the agent being hijacked, this makes it report the attempt. - comment-craft: how to write problem/fix/suggestion so a maintainer can act in one read, and what to cut. pilot/cost_model.py — prices a review against published Claude and OpenAI rates (fetched 2026-08-18). Prompt sizes are measured from the factory files rather than guessed; per-tier workloads come from the tiering budgets. The model is explicit about the thing that actually dominates an agent loop: the whole conversation is resent every step, so caching moves ~2.3x of the bill. Blended over a 5/35/55/5 mix with caching on: ~$0.61/PR on Opus 5 or GPT-5.6 Sol, ~$0.24 on Sonnet 5 or Terra, ~$0.12 on Haiku 4.5, ~$0.02 on Luna. At 350 PRs/month that's ~$212 / ~$85 / ~$43 / ~$8.50. Tests: 101 -> 122. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01B11e8TZZxJyzHW7jj7KWUN
125 lines
5.7 KiB
Markdown
125 lines
5.7 KiB
Markdown
# pragent
|
||
|
||
An extensible, forge-agnostic PR review framework. Not a product — a toolkit that teams
|
||
extend with their own review dimensions.
|
||
|
||
**Status:** design approved; framework build deferred. A **pilot** is live on
|
||
`glm-5.2:cloud` with two delivery paths:
|
||
|
||
- **Central webhook service** (preferred, least per-repo setup): a Gitea
|
||
user-level webhook posts PR events to an always-on in-cluster service that
|
||
gates on the `AI-REVIEW` label. Onboarding a repo = add `pragent-bot`
|
||
collaborator + create the label + label a PR. See
|
||
[`pilot/README-webhook.md`](pilot/README-webhook.md).
|
||
- **CI-step** (legacy): a per-repo Gitea Action fetches the reviewer script at
|
||
runtime. See [`pilot/README.md`](pilot/README.md).
|
||
|
||
The framework design remains at
|
||
[`docs/plans/2026-08-04-pragent-design.md`](docs/plans/2026-08-04-pragent-design.md);
|
||
the pilot is its bootstrap and will be superseded by `pragent review` when the
|
||
framework build resumes.
|
||
|
||
## What it is
|
||
|
||
`pragent` runs as a CI step. It reads a pull request, decides how much attention the
|
||
change deserves, runs the analyzers that apply, and posts ranked findings back to the
|
||
forge.
|
||
|
||
```
|
||
pragent init # one-time repo scan → .pragent/profile.yml (committed, reviewable)
|
||
pragent review # the CI step: tier → analyze → aggregate → publish
|
||
pragent explain # why did this PR get this tier / these findings?
|
||
pragent replay # re-run a past PR against a new prompt or model (the eval loop)
|
||
pragent doctor # config, credentials, and adapter health
|
||
```
|
||
|
||
## Why not CodeRabbit / Greptile / Qodo
|
||
|
||
Those are good products with fixed review dimensions and per-seat pricing. `pragent`
|
||
targets the case where a platform team needs to **add its own dimensions** — an internal
|
||
compliance rule, a service-catalog ownership check, a house performance idiom — without
|
||
forking a vendor's reviewer. Cost lands in the same range (~$25/dev/month at 350 PRs/mo
|
||
for 20 devs), but the analyzers, the data, and the analytics are yours.
|
||
|
||
## Attention tiers
|
||
|
||
Every PR is classified before any expensive work happens. Deterministic rules decide
|
||
first; an ambiguous case gets one cheap model call as tie-breaker.
|
||
|
||
| Tier | What it means | Cost/PR |
|
||
|---|---|---|
|
||
| `trivial` | lockfile bumps, generated code, docs typos | ~$0.005 |
|
||
| `lite` | small change, no risk paths | ~$0.08 |
|
||
| `full` | the default for real changes | ~$0.80–2.00 |
|
||
| `oversized` | too big to review whole; structural summary + deep pass on the hot subset | ~$5 ceiling |
|
||
|
||
Every tier decision records *why*, so a surprising outcome is explainable rather than
|
||
mysterious.
|
||
|
||
## What it costs
|
||
|
||
The pilot runs on `glm-5.2:cloud` through the on-network headroom proxy, so today it
|
||
bills nothing per token — but the token *work* is real, and `pilot/cost_model.py`
|
||
prices it against published API rates. The factory's prompt sizes are measured from
|
||
the files in this repo; the per-tier workloads come from the `attention-tiering`
|
||
budgets. Blended over a 5/35/55/5 tier mix, prompt caching on:
|
||
|
||
| Model | per PR | 350 PRs/month |
|
||
|---|---:|---:|
|
||
| Claude Opus 5 / GPT-5.6 Sol | ~$0.61 | ~$212 |
|
||
| Claude Sonnet 5 / GPT-5.6 Terra | ~$0.24 | ~$85 |
|
||
| Claude Haiku 4.5 | ~$0.12 | ~$43 |
|
||
| GPT-5.6 Luna | ~$0.02 | ~$8.5 |
|
||
|
||
Run `python3 pilot/cost_model.py --help` for other mixes and PR volumes. The
|
||
dominant cost is the agent loop resending its own context each step, not the diff —
|
||
turning prompt caching off multiplies the bill by ~2.3x, which is why the tiering
|
||
skill caps steps, file reads, and subagent fan-out per tier.
|
||
|
||
## Extension points
|
||
|
||
Five, all documented in the design doc. Teams override or add; nobody forks.
|
||
|
||
1. **Analyzers** — drop a YAML + prompt in `.pragent/analyzers/`, or install from npm
|
||
2. **Forge adapters** — Gitea, GitLab, GitHub, local diff
|
||
3. **Tier policy** — thresholds and the path risk map, per repo or per org
|
||
4. **Profile enrichers** — extend what `pragent init` learns about a repo
|
||
5. **Emitter sinks** — JSONL by default, OpenTelemetry, or your own
|
||
|
||
Org config can lock keys, so a repo cannot quietly disable the security analyzer.
|
||
|
||
## Design principles
|
||
|
||
- **Polyglot by construction.** Language knowledge lives in the repo profile, not in the
|
||
reviewer. A new language is a profile change, not a core change.
|
||
- **One shared prompt prefix.** All analyzers for a PR share a byte-identical cached
|
||
prefix. This is what makes fan-out affordable; it is enforced, not hoped for.
|
||
- **Everything is traceable.** Tier reasons, token counts, cost, latency, and finding
|
||
outcomes are recorded per run. False-positive rate is measurable per analyzer.
|
||
- **Fail open.** A budget ceiling or an analyzer crash yields a partial review with a
|
||
clear note, never a blocked pipeline with no explanation.
|
||
- **The reviewed code is untrusted input.** The reviewer runs an agent over a
|
||
branch anyone with PR access can write. So it holds no credentials in its
|
||
environment, the checkout is stripped of files an agent runtime would load as
|
||
instructions, PR-authored text is fenced as data, and reviewer config is read
|
||
from the base branch. See "Threat model" in
|
||
[`pilot/README-webhook.md`](pilot/README-webhook.md).
|
||
|
||
## Stack
|
||
|
||
TypeScript + Node, built on the [`pi`](https://github.com/badlogic/pi-mono) agent SDK.
|
||
Shipped as an npm package and an OCI image, so CI runners need no local Node install.
|
||
|
||
## Roadmap
|
||
|
||
1. Walking skeleton — local diff, one analyzer, rules-only tiering
|
||
2. Gitea end to end — adapter, Woodpecker step, PR comments, status checks
|
||
3. Profile + full tier — `pragent init`, shared-prefix caching, analyzer fan-out
|
||
4. Extensibility hardening — plugin loading, config layering, `explain` / `replay`
|
||
5. Second forge — GitLab adapter, Jenkins recipe
|
||
6. Analytics maturity — OTel export, feedback loop, eval harness
|
||
|
||
## License
|
||
|
||
TBD.
|