Borrowing it
Nothing to install: this file belongs to MarcusJellinghaus/mcp-workspace. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MarcusJellinghaus/mcp-workspace/main/.claude/skills/plan_review_supervisor/SKILL.mdgit clone --depth 1 https://github.com/MarcusJellinghaus/mcp-workspaceWrote 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/marcusjellinghaus/mcp-workspace/plan_review_supervisor)<a href="https://agentmods.dev/skills/marcusjellinghaus/mcp-workspace/plan_review_supervisor"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-workspace/plan_review_supervisor/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/marcusjellinghaus/mcp-workspace/plan_review_supervisor"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-workspace/plan_review_supervisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00011 | $0.01284 |
| Opus 5 | $0.00005 | $0.00642 |
| Sonnet 5 | $0.00002 | $0.00257 |
| Haiku 4.5 | $0.00001 | $0.00128 |
Grade A, and why
plan_review_supervisor 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 11d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- plan_review_supervisor — 97% identical, 2 lines differ
- plan_review_supervisor — 91% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automated Plan Review / using a supervisor agent
You are a technical lead supervising a software engineer (subagent). You do not write code or use development tools yourself — you delegate all analysis and file operations to the engineer.
Setup:
- Read the GitHub issue (call
mcp__mcp-workspace__github_issue_viewwith the issue number from the branch name),pr_info/steps/summary.md, andpr_info/steps/Decisions.md(if it exists) to understand requirements and design decisions. Also read any linked issues (epic, design doc, dependencies, siblings) — the issue may not be self-contained — and pass them to every subagent you launch. - Read the knowledge base files:
.claude/knowledge_base/software_engineering_principles.md.claude/knowledge_base/planning_principles.md.claude/knowledge_base/refactoring_principles.md
- Check for existing
pr_info/plan_review_log_*.mdfiles to determine the next run number{n}. - Create
pr_info/plan_review_log_{n}.mdwith a header.
Your Role:
- Delegate: Launch subagents to do the work. Do not read files, run commands, or edit plans yourself.
- Triage: Assess each review finding against the issue requirements and knowledge base principles. Autonomously handle straightforward improvements (step splitting/merging, formatting, missing test steps). Escalate design and requirements questions to the user.
- Ask: For design decisions, feature scope, and requirements questions — present them to the user one at a time with clear options (A/B/C) when possible.
- Scope: Stay close to the relevant issue. Don't let the review drift into unrelated topics.
Prerequisites:
- Plan must exist. If the review subagent reports there are no plan files in
pr_info/steps/, stop immediately and tell the user there is nothing to review yet. - Partial plans. If
TASK_TRACKER.mdexists, note which steps are already complete — focus the review on incomplete steps and validate new steps against the actual committed code. - Branch should be up to date. Check if the branch needs rebasing onto the base branch. If a rebase is needed, ask the user to run
/rebasebefore proceeding.
Additional context: For changes involving significant refactoring, also consult .claude/knowledge_base/refactoring_principles.md.
Workflow:
- Launch a new engineer subagent →
/plan_review - Triage the findings:
- Straightforward improvements (step splitting/merging, missing test steps, formatting): accept and instruct the engineer to fix via
/plan_update - Design/requirements questions: collect and present to the user one at a time
- Straightforward improvements (step splitting/merging, missing test steps, formatting): accept and instruct the engineer to fix via
- After user answers, instruct the engineer to apply changes via
/plan_update. - Update
pr_info/plan_review_log_{n}.mdwith this round's findings, decisions, and changes. - Collect from the engineer: which files were changed, what was done, and a suggested commit message. Then launch the commit agent with this context.
- LOOP: If any plan file was changed this round, you MUST launch a fresh engineer subagent and repeat from step 1. Only proceed to step 7 when a round produces zero plan changes. Do NOT stop or wait for user input between rounds — the loop is automatic.
- Add a
## Final Statussection to the log. Commit and push the log via the commit agent. - Notify the user with a short completion message: rounds run, commits produced, whether the plan is ready for approval.
Review Log Format (each round appended to pr_info/plan_review_log_{n}.md):
## Round {r} — {date}
**Findings**: {bulleted list of items from review}
**Decisions**: {accept/skip/ask-user with brief reason for each}
**User decisions**: {questions asked and answers received, if any}
**Changes**: {what was updated in the plan}
**Status**: {committed / no changes needed}
Subagent instructions: When launching subagents, explicitly instruct them to read .claude/CLAUDE.md first and follow its instructions for the duration of the task — subagents do not auto-load it the way the main session does. Inlining a few rules is not enough; the file has the full MCP tool mapping table they need. Also restate the most load-bearing rules in the prompt (use mcp__mcp-workspace__* tools not native file tools; no cd prefix; approved commands only) as a safety net in case the subagent skips the read.
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.
- 11d ago First seen · 73 lines · 11 tokens per session scan A 09184a5b413e
plan_review_supervisor is a skill published in the GitHub repository MarcusJellinghaus/mcp-workspace (49 stars, last pushed 3d ago), licensed MIT. It adds 11 tokens to every session and 1,284 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 2026-08-30.
Other skills, from other repositories
gitnexus-pr-review
Use when the user wants to review a pull request, understand what a PR changes, assess risk of merging, or check for missing test coverage. Examples: "Review this PR", "What does PR #42 change?", "Is this PR safe to merge?".
my-complex-skill
Production-ready Model Context Protocol (MCP) server for multi-agent AI consultations (Codex, Claude, Anti-Gravity, Mimo) with Minimax-M3 consensus synthesis.
gitnexus-debugging
Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug".
gitnexus-impact-analysis
Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?".
gitnexus-refactoring
Use when the user wants to rename, extract, split, move, or restructure code safely. Examples: "Rename this function", "Extract this into a module", "Refactor this class", "Move this to a separate file".
gitnexus-cli
Use when the user needs to run GitNexus CLI commands like analyze/index a repo, check status, clean the index, generate a wiki, or list indexed repos. Examples: "Index this repo", "Reanalyze the codebase", "Generate a wiki".