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Agent skill

Mcaf Human Review Planning

by managedcode in managedcode/Storage

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…

MITAuto-check passedAgent Workflows

Install Mcaf Human Review Planning

skills CLI
$ npx skills add managedcode/Storage --skill mcaf-human-review-planning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install managedcode/Storage mcaf-human-review-planning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/managedcode/Storage.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/mcaf-human-review-planning .claude/skills/mcaf-human-review-planning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mcaf-human-review-planning
GitHub stars
138
Token cost
~1.4k tokens
SKILL.md length
678 words
Files
3 (incl. references)
Skills in repo
41
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Works in 3 steps: Read the nearest AGENTS.md and confirm… → Run this skill's Workflow through the… → Return the Required Result Format with…
  • The codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list
  • SKILL.md covers Trigger On, Value, Do Not Use For and Inputs, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mcaf Human Review Planning is an agent skill from managedcode/Storage. 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.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/review-plan-format.md` and `references/risk-signals.md`). Compatibility notes: Requires repository read access; may write a HUMANREVIEWPLAN.md file under docs, or to an exact docs path the user specifies, when the user asks for a saved…

It sits in Agent Workflows. The repository describes itself as: Storage library provides a universal interface for accessing and manipulating data in different cloud blob storage providers. The licence is MIT.

When your agent uses it

  • The codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list

Example prompts

  • “/mcaf-human-review-planning”

Requirements

  • Compatibility (from SKILL.md): 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.

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit 5d32121. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • 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.

    From compatibility in the SKILL.md frontmatter.

Context cost

Mcaf Human Review Planning loads about 1.4k tokens when it runs, and up to ~1.9k if it reads all its reference files. Until then it costs about 94 tokens; SKILL.md has 678 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from managedcode/Storage at commit 5d32121, republished under its MIT licence (© managedcode). 678 words, ~1,364 tokens.

Download SKILL.mdSave it as .claude/skills/mcaf-human-review-planning/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
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
Show full SKILL.md (299 more words)Show less

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."

© managedcode, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in .codex/skills/mcaf-human-review-planning of managedcode/Storage.

  • SKILL.md
  • references/review-plan-format.md
  • references/risk-signals.md

Open the folder on GitHubat commit 5d32121

Compare with similar skills

Mcaf Human Review Planning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Mcaf Human Review Planning compared with similar skills
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Mcaf Human Review Planning this skillmanagedcode/Storage138—~1.4kAutomated safety check: PassMIT
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Hook Development for Claude Code Pluginsanthropics/claude-plugins-official38k11 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k35 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers296k2 repos~5.1kAutomated safety check: PassMIT
Claude Code Agent Developmentanthropics/claude-plugins-official38k8 repos~2.8kAutomated safety check: PassApache-2.0

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Categories

Questions about Mcaf Human Review Planning

What does Mcaf Human Review Planning do?

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…. Mcaf Human Review Planning is an agent skill from managedcode/Storage. 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.

When should I use Mcaf Human Review Planning?

Mcaf Human Review Planning fits situations like: the codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list.

How do I install Mcaf Human Review Planning in Claude Code?

Run `npx skills add managedcode/Storage --skill mcaf-human-review-planning -a claude-code`. Or copy the skill folder (.codex/skills/mcaf-human-review-planning in managedcode/Storage) into .claude/skills/mcaf-human-review-planning in your project. Claude Code loads it when a task matches its description.

How do I install Mcaf Human Review Planning in Codex?

Run `npx skills add managedcode/Storage --skill mcaf-human-review-planning -a codex`. Or copy the skill folder (.codex/skills/mcaf-human-review-planning in managedcode/Storage) into .agents/skills/mcaf-human-review-planning in your project. Codex loads it when a task matches its description.

Can I use Mcaf Human Review Planning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add managedcode/Storage --skill mcaf-human-review-planning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mcaf-human-review-planning, .gemini/skills/mcaf-human-review-planning, .github/skills/mcaf-human-review-planning and .opencode/skills/mcaf-human-review-planning in your project.

What does Mcaf Human Review Planning need to run?

SKILL.md names no scripts, command-line tools or credentials: Mcaf Human Review Planning is instructions for the agent only. Compatibility (from SKILL.md): 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..

Does Mcaf Human Review Planning access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Mcaf Human Review Planning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mcaf Human Review Planning use?

Mcaf Human Review Planning is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mcaf Human Review Planning use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 531 tokens, read only when the agent opens those files.

What are the alternatives to Mcaf Human Review Planning?

Skills that share tags, products or a category with Mcaf Human Review Planning: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 296k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mcaf Human Review Planning?

managedcode (a GitHub organization) maintains it in managedcode/Storage, which has 138 GitHub stars. The repository holds 41 skills in this directory. The repository was last updated on October 7, 2026.

Source: managedcode/Storage on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.