---
name: mcaf-human-review-planning
description: "Plan a human review for a large AI-generated code drop by reading the target area, tracing the natural user and system flows, identifying the riskiest boundaries, and prioritizing the files a human should inspect first. Use when the codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list."
compatibility: "Requires repository read access; may write a `HUMAN_REVIEW_PLAN.md` file under docs, or to an exact docs path the user specifies, when the user asks for a saved review plan."
---

# 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

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

## Workflow

1. Read enough of the target area and its immediate boundaries to understand the generated code before planning review.
2. Map the natural flow of operations first:
   - sign up or authentication
   - create
   - update
   - register or configure
   - execute primary business action
   - complete, archive, or finalize
3. Use that flow to derive the most efficient human review sequence.
4. 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
5. 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
6. Produce two separate outputs:
   - prioritized review flow
   - prioritized files or modules to inspect
7. Present both outputs in chat.
8. 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`.

## Deliver

- a prioritized human review sequence
- a prioritized list of files or modules to inspect first
- both sections presented separately in chat
- a saved `HUMAN_REVIEW_PLAN.md` when requested

## Validate

- the plan is grounded in actual code reading, not only the folder names
- the review order follows actual user or system flows
- high-risk files are explained, not only listed
- priorities account for likely mismatch against existing architecture or analogous entities
- the plan helps a human skip low-value line-by-line review
- the saved plan is readable without extra chat context

## Ralph Loop

Use the Ralph Loop for every task, including docs, architecture, testing, and tooling work.

1. Plan first (mandatory):
   - analyze current state
   - define target outcome, constraints, and risks
   - write a detailed execution plan
   - list final validation skills to run at the end, with order and reason
2. Execute one planned step and produce a concrete delta.
3. Review the result and capture findings with actionable next fixes.
4. Apply fixes in small batches and rerun the relevant checks or review steps.
5. Update the plan after each iteration.
6. Repeat until outcomes are acceptable or only explicit exceptions remain.
7. If a dependency is missing, bootstrap it or return `status: not_applicable` with explicit reason and fallback path.

### Required Result Format

- `status`: `complete` | `clean` | `improved` | `configured` | `not_applicable` | `blocked`
- `plan`: concise plan and current iteration step
- `actions_taken`: concrete changes made
- `validation_skills`: final skills run, or skipped with reasons
- `verification`: commands, checks, or review evidence summary
- `remaining`: top unresolved items or `none`

For setup-only requests with no execution, return `status: configured` and exact next commands.

## Load References

- read `references/review-plan-format.md` for the output shape
- read `references/risk-signals.md` when deciding what deserves human attention first

## Example Requests

- "Plan a human review for this 40K-line AI-generated feature."
- "I cannot review every file. Tell me what to inspect first."
- "Trace the signup-to-completion flow and save a HUMAN_REVIEW_PLAN.md."
- "Look through the generated folder, give me two separate prioritized review lists, and save them under docs for this area."