An iterative code-review process that performs several focused audit passes, keeps its findings between passes, and stops when the results converge.

Use it for quality audits, release-readiness checks, specification validation, cross-reference checks, and unattended review sessions.

It helps uncover issues that a single review may miss and checks whether code, specifications, documentation, and release requirements agree.

deep-review — Skill for Claude Code and OpenCode by MichelKerkmeester

Install

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.

Any agent

npx skills add MichelKerkmeester/skilled-agent-harness_spec-driven-loops --skill deep-review

Clone the repo

git clone --depth 1 https://github.com/MichelKerkmeester/skilled-agent-harness_spec-driven-loops

Made for: Claude Code, OpenCode.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-review/github.svg)](https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-review)

Your own site

<a href="https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-review"><img src="https://agentmods.dev/badge/skills/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-review"><img src="https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/deep-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 6,214 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00030	$0.06214
Opus 5	$0.00015	$0.03107
Sonnet 5	$0.00006	$0.01243
Haiku 4.5	$0.00003	$0.00621

Measured yesterday against content hash 727b2fd32d07, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 yesterday.

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.

.opencode/skills/system-deep-loop/deep-review/SKILL.md · 446 lines

How it starts

The opening of the file, as written. The whole thing — 446 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Autonomous Deep Review Loop

Iterative code review and quality auditing protocol with fresh context per iteration, externalized state, convergence detection, and severity-weighted findings (P0/P1/P2).

Runtime path resolution:

OpenCode/Copilot runtime: .opencode/agents/*.md
Claude runtime: .claude/agents/*.md

Convergence threshold semantics and sibling-parity notes (deep-review 0.10 vs deep-research 0.05 vs deep-ai-council 0.20) live in references/convergence/convergence.md §1 under "Threshold Semantics and Sibling Parity".

1. WHEN TO USE

When to Use This Skill

Use this skill when:

Code quality audit requiring multiple rounds across different review dimensions
Spec folder validation requiring cross-reference checks between docs and implementation
Release readiness check before shipping a feature or component
Finding misalignments between spec documents and actual code
Verifying cross-references across documentation, agents, commands, and code
Iterative review where each dimension's findings inform subsequent dimensions
Unattended or overnight audit sessions

When NOT to Use

Simple single-pass code review (use sk-code's code-review mode instead)
Known issues that just need fixing (go directly to implementation)
Implementation tasks (use sk-code or /speckit:implement)
Quick one-file checks (use direct Grep/Read)
Fewer than 2 review dimensions needed (single-pass suffices)

FORBIDDEN INVOCATION PATTERNS

This skill is invoked EXCLUSIVELY through the /deep:review command. The command's YAML workflow owns state, dispatch, and convergence.

NEVER:

Write a custom bash/shell dispatcher to parallelize iterations (ad-hoc shell fan-out)
Invoke any CLI executor (cli-codex, cli-claude-code, cli-opencode, cli-cursor, cli-devin, cli-pi) directly in a loop to simulate iterations
Manually write iteration prompts to /tmp and dispatch them via copilot -p
Dispatch the @deep-review LEAF agent via the Task tool for iteration loops (the agent is LEAF, a single iteration, and MUST be driven by the command's workflow)
Skip the state machine: deep-review-state.jsonl, deep-review-config.json, deltas/, prompts/, logs/
Manage iteration state outside the resolved local review packet under {spec_folder}/review/

Read the full file on GitHub · 446 lines

Files

What ships with it

60 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.

Changes

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.

yesterday Changed 727b2fd32d07
3d ago Changed · +2 lines d62f774b4e72
6d ago Changed c89a752104fe
8d ago First seen · 444 lines · 30 tokens per session scan A 52e57b365725

Subscribe to this mod's changes

deep-review is a skill published in the GitHub repository MichelKerkmeester/skilled-agent-harness_spec-driven-loops (35 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 6,214 once invoked, about $0.0002 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 2026-09-03.

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