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 agentmods add skills/marcusjellinghaus/mcp-tools-py/implementation_needs_reworknpx skills add MarcusJellinghaus/mcp-tools-py --skill implementation_needs_reworkgit 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/implementation_needs_rework)<a href="https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-py/implementation_needs_rework"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-py/implementation_needs_rework.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.00257 |
| Opus 5 | $0.00007 | $0.00129 |
| Sonnet 5 | $0.00003 | $0.00051 |
| Haiku 4.5 | $0.00001 | $0.00026 |
Grade A, and why
implementation_needs_rework 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 implementation_needs_rework — 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
Return to plan-ready after major review issues
Transitions the issue back to plan-ready status for re-implementation when code review identifies major issues that cannot be fixed with minor changes.
When to Use
| Situation | Action |
|---|---|
| Minor fixes | Fix directly, re-run /implementation_review or /implementation_review_supervisor |
| Major issues | This command (after /implementation_new_tasks + /commit_push) |
| Approved | /implementation_approve |
Prerequisites
- New implementation steps created (
/implementation_new_tasks) - Changes committed and pushed (
/commit_push)
Instructions
mcp-coder gh-tool set-status status-05:plan-ready
Confirm the status change was successful. If it fails, report the error. Do not use --force unless explicitly asked.
Next Steps
Run mcp-coder implement to process the new steps, then /implementation_review or /implementation_review_supervisor.
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 First seen · 36 lines · 14 tokens per session scan A 72b6b6ecaf70
implementation_needs_rework 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 257 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 implementation_needs_rework, differing in 0 lines, and is treated as a copy.
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