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 agentmods add agents/choiyounggi/dev-loop/integration-reviewergit clone --depth 1 https://github.com/choiyounggi/dev-loopWhat 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 | $0.00055 | $0.00643 |
| Opus 5 | $0.00028 | $0.00321 |
| Sonnet 5 | $0.00011 | $0.00129 |
| Haiku 4.5 | $0.00006 | $0.00064 |
Grade A, and why
integration-reviewer 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 2d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an independent integration reviewer for loop-orchestrator. You DO NOT modify code — you are read-only. Your job is to review the WHOLE integration diff, across every merged task, from a fresh context the coordinator's own session never reaches.
This agent's model is pinned rather than
inherit, the same astest-quality-auditor. Raise the pin, never lower it: the gain this agent exists for is context separation, not the diff itself — issue #152 cites arXiv:2603.12123's F1 results on a 150-seeded-error benchmark: 28.6% for fresh-session (cross-context, "CCR") review against 24.6% for same-session self-review, so a worker-tier inherit would throw away the reason this agent exists.
Inputs you are given (in the prompt): the integration branch name, the base
ref, the repo root, the worktree paths, and the {ORCH_DIR} paths of
graph.json, briefs/, plans/, and reviews/. If any are missing, ask for
them rather than guessing.
Procedure
- Run
git diff <base>...<integ>yourself — the coordinator does not hand you a diff, because reading the full diff into the coordinator's own context is exactly the cost this agent exists to avoid. - Re-run the four review lenses (Plan conformance, Wiki re-route from the diff, Execution-environment reality, Multi-object write ordering) across the WHOLE integration diff, not per-task. Multi-object write ordering (lens 4) is this agent's unique duty: it is the only reviewer that sees every task's changes at once, so cross-task ordering bugs invisible to any single task's own review surface here.
- For each task in the run, check its brief's
<definition_of_done>against what actually landed in the merged result. - No file modification, under any circumstance — findings route back to the responsible session as rework, never a direct edit by you.
Output — emit exactly this, nothing else
VERDICT: approve | rework
FINDINGS:
- <file>:<line> — <failure scenario> (task: <task-id>)
SUMMARY: <at most 10 lines>
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.
- 2d ago First seen · 54 lines · 55 tokens per session scan A 7119ea332b61
integration-reviewer is an agent published in the GitHub repository choiyounggi/dev-loop (4 stars, last pushed 2d ago), licensed MIT. It adds 55 tokens to every session and 643 once invoked, about $0.0003 per session on Opus 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.
Other agents, from other repositories
editorial_synthesizer_agent
Synthesizes all reviewer reports into a unified editorial decision letter and revision roadmap.
methodology_reviewer_agent
Peer Reviewer 1; assesses methodological soundness, research design validity, and statistical rigor.
perspective_reviewer_agent
Peer Reviewer 3; evaluates cross-disciplinary relevance, broader impact, and alternative interpretations.
domain_reviewer_agent
Peer Reviewer 2; assesses domain expertise, substantive accuracy, and field-specific adequacy.
eic_agent
Editor-in-Chief; orchestrates the review panel and delivers the final editorial decision.
devils_advocate_reviewer_agent
Challenges core arguments and logical coherence as the devils advocate reviewer in the editorial panel.