What is dotnet-mcaf-human-review-planning?

A review-planning guide for large code changes created by AI. It maps user and system flows, finds risky boundaries, and ranks the files a person should inspect first.

Use it to create a saved, ordered plan for manually reviewing a generated folder or feature area. It is not for small pull-request reviews or automatic bug detection.

A human cannot always read every line of a large generated change. This helps focus review time on the parts most likely to cause problems.

How managedcode/PrompterOne configures Codex

Reuse

Borrowing it

Nothing to install: this file belongs to managedcode/PrompterOne. 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/managedcode/PrompterOne/main/.codex/skills/dotnet-mcaf-human-review-planning/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/managedcode/PrompterOne

Made for: 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/managedcode/prompterone/dotnet-mcaf-human-review-planning/github.svg)](https://agentmods.dev/skills/managedcode/prompterone/dotnet-mcaf-human-review-planning)

Your own site

<a href="https://agentmods.dev/skills/managedcode/prompterone/dotnet-mcaf-human-review-planning"><img src="https://agentmods.dev/badge/skills/managedcode/prompterone/dotnet-mcaf-human-review-planning/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/managedcode/prompterone/dotnet-mcaf-human-review-planning"><img src="https://agentmods.dev/badge/skills/managedcode/prompterone/dotnet-mcaf-human-review-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,201 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	$0.00082	$0.01201
Opus 5.5	$0.00033	$0.00480
Sonnet 5.5	$0.00016	$0.00240
Haiku 4.5	$0.00008	$0.00120

Measured 1mo ago against content hash da229bc6e15e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dotnet-mcaf-human-review-planning 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.

Origin

Copies of this mod

3 near-identical copies found in the catalogue:

dotnet-mcaf-human-review-planning — 98% identical, 2 lines differ
mcaf-human-review-planning — 97% identical, 4 lines differ
mcaf-human-review-planning — 89% identical, 25 lines differ

.codex/skills/dotnet-mcaf-human-review-planning/SKILL.md · 124 lines

How it starts

The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MCAF: Human Review Planning

Trigger On

a large AI-generated code drop needs a human review plan
the reviewer cannot inspect every line and needs prioritization
the user asks which files are highest risk before doing manual review
the user names a generated folder and wants a saved review plan for it

Value

produce a concrete project delta: code, docs, config, tests, CI, or review artifact
reduce ambiguity through explicit planning, verification, and final validation skills
leave reusable project context so future tasks are faster and safer

Do Not Use For

normal small pull-request review
automated bug finding without creating a human review sequence

Inputs

the target folder, feature area, or bounded context under review
the main user journeys or operational flows involved
any known architecture context, adjacent entities, or existing system rules
any exact output path the user wants for the saved plan

Quick Start

Read the nearest AGENTS.md and confirm scope and constraints.
Run this skill's Workflow through the Ralph Loop until outcomes are acceptable.
Return the Required Result Format with concrete artifacts and verification evidence.

Workflow

Read enough of the target area and its immediate boundaries to understand the generated code before planning review.
Map the natural flow of operations first:
- sign up or authentication
- create
- update
- register or configure
- execute primary business action
- complete, archive, or finalize
Use that flow to derive the most efficient human review sequence.
Use the reviewer's domain knowledge as a force multiplier:
- compare the generated code against known architecture and existing entities
- look for places where the new feature should behave like nearby existing flows
- prioritize boundaries where generated code may drift from established system rules
Identify high-risk review zones:
- entry points and orchestration layers
- persistence and state transitions
- cross-boundary integrations
- permissions, validation, and invariants
- side effects such as email, payments, jobs, or notifications
Produce two separate outputs:
- prioritized review flow
- prioritized files or modules to inspect
Present both outputs in chat.
If the user asks for a durable artifact, save the plan to the exact docs path they requested; otherwise use docs/AREA/HUMAN_REVIEW_PLAN.md.

Read the full file on GitHub · 124 lines

Files

What ships with it

3 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.

1mo ago First seen · 124 lines · 82 tokens per session scan A da229bc6e15e

Subscribe to this mod's changes

dotnet-mcaf-human-review-planning is a skill published in the GitHub repository managedcode/PrompterOne (45 stars, last pushed 5mo ago), licensed MIT. It adds 82 tokens to every session and 1,201 once invoked, about $0.0003 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 2026-08-30.

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PrompterOne: Skill for Codex