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 swDomass/AI_orchestrator --skill review-loopgit clone --depth 1 https://github.com/swDomass/AI_orchestratorWrote 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/swdomass/ai_orchestrator/review-loop)<a href="https://agentmods.dev/skills/swdomass/ai_orchestrator/review-loop"><img src="https://agentmods.dev/badge/skills/swdomass/ai_orchestrator/review-loop/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/swdomass/ai_orchestrator/review-loop"><img src="https://agentmods.dev/badge/skills/swdomass/ai_orchestrator/review-loop.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 | $0.00023 | $0.00702 |
| Opus 5.5 | $0.00009 | $0.00281 |
| Sonnet 5.5 | $0.00005 | $0.00140 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade A, and why
review-loop 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 1mo 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.
What it actually says
System Prompt Addition
Perform an iterative code review. Classify findings as:
- P1 (blocker): bugs, security issues, data loss risks — blocking
- P2 (important): performance problems, maintainability issues — blocking
- P3 (minor): style, naming, minor improvements — NOT blocking
After each round, fix all P1 and P2 findings, then re-review. The loop ends when no
P1 or P2 remains — not when the finding list is empty. Max 20 iterations
(config.TOOL_MAX_ITERATIONS).
P3 is not fixed here. The tool removes every P3 from the fix prompt, so no P3 is ever requested; they are collected across all iterations and appended once to the final output as an offer, with file:line — the user decides. Do not ask for P3 fixes in this loop.
Scope of that guarantee: it covers the prompt, not the model's whole context. With
CLAUDE_SESSION_ENABLED=true the review and fix calls share one conversation
(SessionContext, --resume), so a P3 named in an earlier review is still in history and
the fixing step can see it. What holds in both modes is the part that matters: no P3
reaches the fix prompt, and — because the success gate counts only blocking findings — a
P3 can never keep the loop running. A fresh session before every write call would close
the gap, at the cost of the prompt-cache benefit session reuse exists for; that trade was
declined deliberately.
Why: touching working code for cosmetics widens the diff without functional gain, and because the re-review re-reads the diff fresh, every P3 fix produces new diff that can surface new P3 — the loop feeds itself and burns iterations on style. Fixing P1/P2 only is the direct application of "minimal impact", not a shortcut.
Rundenreflexion (from iteration 2 on): before fixing, the fixer writes a
## Rundenreflexion section asking whether it is over-building or chasing an edge case,
then may defer any P2 finding as - [BEKANNTE GRENZE] <finding> — <reason>
instead of fixing it. A P1 can never be deferred this way — enforced in
tools/review_loop.py (validate_known_limits), not only in the prompt. A deferred
finding no longer blocks the following round; the next review prompt is told about it so
it is not re-reported as P2, and the final output lists it as "Bekannte Grenzen", separate
from the P3 offer.
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.
- 1mo ago Changed · +9 lines 673045ad42cb
- 1mo ago Changed · +22 lines · +4 tokens per session d8a19a2fbba3
- 1mo ago First seen · 24 lines · 19 tokens per session scan A 09484c75ee7a
review-loop is a skill published in the GitHub repository swDomass/AI_orchestrator (5 stars, last pushed 15d ago), licensed MIT. It adds 23 tokens to every session and 702 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 2026-08-31.
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