Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tikalk/adlc-team-skills --skill factory-reviewgit clone --depth 1 https://github.com/tikalk/adlc-team-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tikalk/adlc-team-skills/factory-review)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/factory-review"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/factory-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/factory-review"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/factory-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.03512 |
| Opus 5.5 | $0.00015 | $0.01405 |
| Sonnet 5.5 | $0.00007 | $0.00702 |
| Haiku 4.5 | $0.00004 | $0.00351 |
Grade A, and why
factory-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
factory-review
What this skill does
factory-review is the PR-review compliance engine of the software factory. It acts as the automated component of The Great Filter, reviewing Pull Requests against organizational policy-as-code (REVIEW.md) and babysitting agent-opened PRs until they are ready for merge.
It operates as a Kind-B control-plane skill integrated with the PR hosting provider (GitHub / GitLab) via the tracker-agnostic integration layer (factory-mission/references/tracker-integration.md).
In --self-heal mode, it enters a three-sub-agent converge loop mirroring factory-mission Phase 5's test/code/converge pattern. Three separate sub-agents — Review Agent (read-only), Fix Agent (writeable src/), Converge Agent (independent judge) — are dispatched per iteration via ADR-336 lanes, communicating only through the comment bus (ADR-330). The converge step returns DONE / CONTINUE / SPEC_CORRECTION_NEEDED, bounded by a circuit breaker and score-regression counter.
When to use
- Before any PR is merged, to run identical, consistent compliance checks.
- To have the agent automatically address reviewer comment threads and push fixes.
- To manage agent-opened PRs (sweeping unresolved threads, resolving failing tests) until they are ready for final sign-off.
- With
--self-healto proactively fix Important findings before a human reviews the PR.
When NOT to use:
- To evaluate test execution correctness (use
factory-mission/ evals judges instead). - To bypass branch protection (this skill never approves or merges PRs).
Process
1. Policy-as-Code Configuration
- Read the
REVIEW.mdfile from the repository root. If absent, create a default template (seereferences/review-policy.md). - The policy defines:
- Review passes: Bugs & logical errors, security vulnerabilities, and compliance against design documents (
docs/adlc/product/PRD.md/docs/adlc/architect/AD.md). - Severity weights: What constitutes an Important block (e.g., memory leak, security risk, spec deviation) vs. a Nit (formatting, style).
- Skip lists: Generated paths, vendor files, and CI-validated paths.
- Review passes: Bugs & logical errors, security vulnerabilities, and compliance against design documents (
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago Changed · +2 lines 2894ea1fabb3
- 10d ago Changed · +2 lines 0be68865b324
- 15d ago Changed cee1a25c0489
- 24d ago Changed d8ef883d72f4
- 25d ago First seen · 189 lines · 37 tokens per session scan A 4cf3b065663e
factory-review is a skill published in the GitHub repository tikalk/adlc-team-skills (141 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 3,512 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-21.
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