feat(factory): five review skills + a per-review cost model

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

README.md

@@ -56,6 +56,26 @@ first; an ambiguous case gets one cheap model call as tie-breaker.
 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.