implementation_review_supervisor

implementation_review_supervisor is a skill for Claude Code from MarcusJellinghaus/mcp-tools-py. It costs 14 tokens per session (1,429 once invoked), scanned A, a copy of implementation_review_supervisor, MIT.

An automated code-review workflow in which a supervisor delegates review work to engineer agents and records the findings.

In plain words
What is it for?
Use it to review an implementation and decide which findings require fixes or escalation.
Why use it?
It provides a structured review against the issue, project decisions, and software-engineering guidance without having the supervisor write code.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions CLAUDE.md; mentions subagents.

Good fit Use it to review an implementation and decide which findings require fixes or escalation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/marcusjellinghaus/mcp-tools-py/implementation_review_supervisor
Install

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.

Any agent
npx skills add MarcusJellinghaus/mcp-tools-py --skill implementation_review_supervisor
Clone the repo
git clone --depth 1 https://github.com/MarcusJellinghaus/mcp-tools-py

Made for: Claude Code.

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.

agentmods badge for implementation_review_supervisor

README.md
[![agentmods](https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-py/implementation_review_supervisor.svg)](https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-py/implementation_review_supervisor)
Your own site
<a href="https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-py/implementation_review_supervisor"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-py/implementation_review_supervisor.svg" alt="Measured on agentmods" height="20"></a>
Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,429 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 94% copy Near-identical to another mod 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00014 $0.01429
Opus 5 $0.00007 $0.00714
Sonnet 5 $0.00003 $0.00286
Haiku 4.5 $0.00001 $0.00143

Measured 8d ago against content hash 3728b8864199, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

implementation_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 8d 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

This is a copy

94% identical to implementation_review_supervisor — 4 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.

.claude/skills/implementation_review_supervisor/SKILL.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Automated Implementation Review (Code 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 implementation work to the engineer.

Setup:

  1. Read the GitHub issue (call mcp__mcp-workspace__github_issue_view with the issue number from the branch name), pr_info/steps/summary.md, and pr_info/steps/Decisions.md (if it exists) to understand requirements and design decisions.
  2. Read the knowledge base files:
    • .claude/knowledge_base/software_engineering_principles.md
    • .claude/knowledge_base/python.md
  3. Check for existing pr_info/implementation_review_log_*.md files to determine the next run number {n}.
  4. Create pr_info/implementation_review_log_{n}.md with a header.

Your Role:

  • Delegate: Launch subagents to do the work. Do not execute code, read files, or run tests yourself.
  • Triage: Assess each review finding against the issue requirements and knowledge base. Skip items that are out of scope, cosmetic, or speculative. Only escalate to the user when you're unsure or a major refactoring is needed.
  • Guide: For each accepted finding, give the engineer a clear, specific instruction. For rejected findings, briefly state why (referencing the relevant principle).
  • Scope: Stay close to the relevant issue. Don't let the review drift into unrelated improvements.

Pre-flight: Task Tracker Check

  • Check pr_info/TASK_TRACKER.md for unchecked items under ## Tasks only. Ignore other sections (## Pull Request or ## Code Review, etc.) — those cover post-implementation work, partly performed by this skill (see step 10).
  • If any ## Tasks items are unchecked, stop and tell the user:

    Open implementation tasks remain. Run /implementation_finalise first.

Prerequisites:

  • Code must exist. If the review subagent reports there is no implementation diff (only plan files, docs, or pr_info/), stop immediately and tell the user there is nothing to review yet.

Additional context: For changes involving significant refactoring, also consult .claude/knowledge_base/refactoring_principles.md.

Workflow:

  1. Launch a new engineer subagent → /implementation_review
  2. /discuss the findings — triage each item, decide accept/skip
  3. Tell the engineer to implement the accepted changes. If a major refactoring is needed, stop and talk to the user.
  4. Update pr_info/implementation_review_log_{n}.md with this round's findings, decisions, and changes.
  5. Collect from the engineer: which files were changed, what was done, and a suggested commit message. Then launch the commit agent with this context. The commit agent should verify only the expected files are modified before committing.
  6. Launch the engineer → /check_branch_status
  7. LOOP: If any code was changed this round, you MUST launch a fresh engineer subagent and repeat from step 1. Only proceed to step 8 when a round produces zero code changes. Do NOT stop or wait for user input between rounds — the loop is automatic.
  8. Run run_vulture_check and run_lint_imports_check yourself. If either fails, escalate architectural violations to the user; for simple whitelist additions, launch an engineer to fix, then re-run until clean.
  9. Add a ## Final Status section to the log. Commit and push the log via the commit agent.
  10. Launch the engineer → /check_branch_status to verify CI, rebase need, and overall readiness. Include the result in the completion message.
  11. Perform any PR-section tasks this skill covers — typically PR review or Code review. Once done, tick them in pr_info/TASK_TRACKER.md and commit via the commit agent (separate commit from the log). Leave unrelated tasks like PR summary alone.
  12. Notify the user with a short completion message: rounds run, commits produced, whether any issues remain, and branch status (CI, rebase needed).

Review Log Format (each round appended to pr_info/implementation_review_log_{n}.md):

Read the full file on GitHub · 79 lines

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.

  1. 8d ago First seen · 79 lines · 14 tokens per session scan A 3728b8864199

Subscribe to this mod's changes

implementation_review_supervisor is a skill published in the GitHub repository MarcusJellinghaus/mcp-tools-py (18 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 1,429 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to implementation_review_supervisor, differing in 4 lines, and is treated as a copy.

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