What is conduct-deep-reviewing-loop?

A structured review process for implementation plans, using several independent specialist reviewers and repeated review rounds.

Use it to audit a plan, route reviews based on dependencies, record required changes, and decide whether the plan passes review.

It reduces the risk that a plan misses important issues or dependencies by checking it from multiple perspectives and rechecking revised sections.

How loerei/chronicle-mcp configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to loerei/chronicle-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/loerei/chronicle-mcp/main/.agents/skills/conduct-deep-reviewing-loop/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/loerei/chronicle-mcp

Made for: Claude Code, Codex.

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/loerei/chronicle-mcp/conduct-deep-reviewing-loop/github.svg)](https://agentmods.dev/skills/loerei/chronicle-mcp/conduct-deep-reviewing-loop)

Your own site

<a href="https://agentmods.dev/skills/loerei/chronicle-mcp/conduct-deep-reviewing-loop"><img src="https://agentmods.dev/badge/skills/loerei/chronicle-mcp/conduct-deep-reviewing-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/loerei/chronicle-mcp/conduct-deep-reviewing-loop"><img src="https://agentmods.dev/badge/skills/loerei/chronicle-mcp/conduct-deep-reviewing-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,539 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.

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	$0.00022	$0.01539
Opus 5	$0.00011	$0.00770
Sonnet 5	$0.00004	$0.00308
Haiku 4.5	$0.00002	$0.00154

Measured 5d ago against content hash e793384d255c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

conduct-deep-reviewing-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 5d 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.

.agents/skills/conduct-deep-reviewing-loop/SKILL.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Conduct Deep Reviewing Loop

Multi-agent review loop using isolated domain reviewers, topological dependency routing, and independent gatekeeping to verify Directive Artifacts (DA).

Execution Architecture

Layer	Agent	Primary Responsibility
Layer 1	Main Agent	Spawns Layer 2 Host, applies clean DA mutations from `Changelog.md`, presents final output.
Layer 2	Review Host & Critical Gate	Dynamically selects active reviewers in `Reviewer_Choice_Rationale.md`, summons active reviewers using invariant prompts, purges `reports/` before passes, isolates host artifacts in `host/`, executes Reviewer-level DAG routing, enforces Tier Batch Gate negotiation and in-place fix pre-verification, terminates subagent processes upon tier batch resolution, executes Snapshot Delta Backfill for skipped roles (upstream and untouched), writes `Analyzation.md`, `Changelog.md`, and `Untouched_Reviewers.md`.
Layer 3	Domain Reviewers	Independent specialist subagents (up to 11 roles across 4 Tiers) executing domain audits per `<Role>-REVIEWER-GUIDE.md`.

Workflow

flowchart TD
    Start["Round 1: Full DAG Sweep"] --> Eval{"All Roles PASS?"}
    Eval -->|"No"| Apply["Layer 1: Apply Changelog.md to DA"]
    Eval -->|"Yes"| Accumulate["PassCount += 1"]
    Apply --> TargetRun["Round N+1: Targeted Re-Review<br/>(Run affected roles excluding Untouched_Reviewers)"]
    CheckTarget{"Targeted Roles PASS?"}
    TargetRun --> CheckTarget
    CheckTarget -->|"No"| Apply
    CheckTarget -->|"Yes (Pending Skipped Roles)"| Backfill["Snapshot Delta Backfill<br/>(Topologically summon skipped roles on SN)"]
    Backfill --> BackfillCheck{"Skipped Roles PASS?"}
    BackfillCheck -->|"No"| Apply
    BackfillCheck -->|"Yes"| Accumulate
    CheckTarget -->|"Yes (100% Roster Audited)"| Accumulate
    Accumulate --> SPCheck{"PassCount >= SP?"}
    SPCheck -->|"No"| FullSweep["Next Full Sweep Round<br/>(Run active roles on static DA)"]
    FullSweep --> SweepCheck{"All Active Roles PASS?"}
    SweepCheck -->|"No"| Apply
    SweepCheck -->|"Yes"| Accumulate
    SPCheck -->|"Yes"| FinalPass["Issue FINAL_PASS & Conclude"]

Read the full file on GitHub · 81 lines

Files

What ships with it

39 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.

5d ago Changed · +5 lines e793384d255c
10d ago First seen · 76 lines · 22 tokens per session scan A 8e30782b0ec5

Subscribe to this mod's changes

conduct-deep-reviewing-loop is a skill published in the GitHub repository loerei/chronicle-mcp (0 stars, last pushed 5d ago), licensed MIT. It adds 22 tokens to every session and 1,539 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 2026-08-31.

Planning

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle-mcp: Skill for Claude Code