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 MarcusJellinghaus/mcp-tools-py --skill plan_reviewgit clone --depth 1 https://github.com/MarcusJellinghaus/mcp-tools-pyWrote 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-tools-py/plan_review)<a href="https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-py/plan_review"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-py/plan_review/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-tools-py/plan_review"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-py/plan_review.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 | $0.00010 | $0.00344 |
| Opus 5 | $0.00005 | $0.00172 |
| Sonnet 5 | $0.00002 | $0.00069 |
| Haiku 4.5 | $0.00001 | $0.00034 |
Grade A, and why
plan_review 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 5d 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
100% identical to plan_review — 0 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.
What it actually says
Review Implementation Plan
First, ensure we're up to date:
Call mcp__mcp-workspace__git with command "fetch" and args ["origin"].
Use mcp__mcp-workspace__git with command "status" to check working directory state.
Confirm and display the current feature branch name.
Then review the plan:
Please review the project plan for a new feature in folder pr_info/steps.
Please revise the project plan with a balanced level of detail.
Please let me know if any complexity could be reduced.
Please let me know any questions / comments or suggestions you might have.
Please consider the already discussed and decided decisions (if any) under decisions. We do not need to challenge them again unless absolutely necessary.
Focus on:
- Completeness of implementation steps
- Appropriate level of detail
- Opportunities for simplification (KISS principle)
- Test coverage strategy
- Step granularity — each step should produce exactly one commit. Flag steps with multiple independent parts (A, B, C) that should be split.
- Potential risks or blockers
- Requirement changes — flag new dependencies (
pyproject.toml) that should be applied during planning, not deferred to implementation
For planning standards, see .claude/knowledge_base/planning_principles.md.
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.
- 5d ago Changed · -1 lines 99c8bebf2f9d
- 9d ago First seen · 41 lines · 10 tokens per session scan A 07757275246e
plan_review is a skill published in the GitHub repository MarcusJellinghaus/mcp-tools-py (18 stars, last pushed 2d ago), licensed MIT. It adds 10 tokens to every session and 344 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to plan_review, differing in 0 lines, and is treated as a copy.
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