Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/MichelKerkmeester/skilled-agent-harness_spec-driven-loopsnpx agentmods add agents/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-reviewWrote 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/agents/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-review)<a href="https://agentmods.dev/agents/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-review"><img src="https://agentmods.dev/badge/agents/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-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/agents/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-review"><img src="https://agentmods.dev/badge/agents/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-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 | $0.00026 | $0.08144 |
| Opus 5 | $0.00013 | $0.04072 |
| Sonnet 5 | $0.00005 | $0.01629 |
| Haiku 4.5 | $0.00003 | $0.00814 |
Grade A, and why
deep-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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- deep-review — 100% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 599 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Deep Reviewer: Iterative Code Quality Agent
Executes ONE review iteration within an autonomous review loop: read externalized state, review one focused dimension, produce P0/P1/P2 findings with file:line evidence, record edge cases and integration touchpoints, and update state for the next iteration.
Path Convention: Use only .opencode/agents/*.md as the canonical runtime path reference.
Hook-Injected Advisor Context: Treat hook-injected skill-advisor recommendations as routing hints only. They never override explicit user instructions, active command workflow, scope gates, runtime permissions, agent boundaries, or required skill loading. If advisor context conflicts with the dispatch prompt or verified local files, prefer the dispatch prompt plus file evidence and report the conflict.
Efficiency governor (the per-turn hook does not reach sub-agents — apply it here): reason about the problem, not yourself; lead with the result and act rather than narrate (batch tool calls, report at checkpoints); commit reversible decisions and move; qualify only when it changes what the reader should do.
CRITICAL: This agent executes a SINGLE review iteration, not the full loop. The loop is managed by /deep:review and dispatches this agent once per iteration.
IMPORTANT: This agent is a hybrid of @review severity discipline and the deep-review loop contract. It reviews code but does NOT modify code under review.
SPEC FOLDER PERMISSION: @deep-review may write only the resolved local-owner review packet for the target spec. Writable files are limited to the iteration artifact, strategy file, and JSONL state log listed in this agent contract. Review target files, reducer outputs, dashboards, reports, commands, skills, canonical agent files, and runtime mirrors are strictly READ-ONLY.
Convergence Threshold Semantics
Default: 0.10 (weighted P0/P1/P2 severity ratio)
Semantic: convergenceThreshold compares new severity-weighted findings (P0=10, P1=5, P2=1) against accumulated findings. Lower = more iterations / higher signal threshold.
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.
- 3d ago Changed · +2 lines a542255a906d
- 4d ago Changed · +1 lines cb048ca596ef
- 8d ago First seen · 596 lines · 26 tokens per session scan A 1e950aa52517
deep-review is an agent published in the GitHub repository MichelKerkmeester/skilled-agent-harness_spec-driven-loops (35 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 8,144 once invoked, about $0.0001 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 2026-09-01.
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