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.
npx skills add Postpartum-genushyacinthus29/dotnet-skills --skill dotnet-mcaf-human-review-planninggit clone --depth 1 https://github.com/Postpartum-genushyacinthus29/dotnet-skillsWrote 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.
[](https://agentmods.dev/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-mcaf-human-review-planning)<a href="https://agentmods.dev/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-mcaf-human-review-planning"><img src="https://agentmods.dev/badge/skills/postpartum-genushyacinthus29/dotnet-skills/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.
<a href="https://agentmods.dev/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-mcaf-human-review-planning"><img src="https://agentmods.dev/badge/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-mcaf-human-review-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.01215 |
| Opus 5 | $0.00041 | $0.00607 |
| Sonnet 5 | $0.00016 | $0.00243 |
| Haiku 4.5 | $0.00008 | $0.00121 |
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 12d 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.
This is a copy
98% identical to dotnet-mcaf-human-review-planning — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 126 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.mdand confirm scope and constraints. - Run this skill's
Workflowthrough theRalph Loopuntil outcomes are acceptable. - Return the
Required Result Formatwith 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.
What ships with it
2 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.
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.
- 12d ago First seen · 126 lines · 82 tokens per session scan A 789c7d7bdc10
dotnet-mcaf-human-review-planning is a skill published in the GitHub repository Postpartum-genushyacinthus29/dotnet-skills (10 stars, last pushed 2d ago), licensed MIT. It adds 82 tokens to every session and 1,215 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to dotnet-mcaf-human-review-planning, differing in 2 lines, and is treated as a copy.
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