Borrowing it
Nothing to install: this file belongs to hugoduncan/mcp-tasks. 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/hugoduncan/mcp-tasks/master/.claude/commands/mcp-tasks-optimize-prompts.mdgit clone --depth 1 https://github.com/hugoduncan/mcp-tasksWrote 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/commands/hugoduncan/mcp-tasks/mcp-tasks-optimize-prompts)<a href="https://agentmods.dev/commands/hugoduncan/mcp-tasks/mcp-tasks-optimize-prompts"><img src="https://agentmods.dev/badge/commands/hugoduncan/mcp-tasks/mcp-tasks-optimize-prompts/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/commands/hugoduncan/mcp-tasks/mcp-tasks-optimize-prompts"><img src="https://agentmods.dev/badge/commands/hugoduncan/mcp-tasks/mcp-tasks-optimize-prompts.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 | $0.00009 | $0.01081 |
| Opus 5 | $0.00005 | $0.00541 |
| Sonnet 5 | $0.00002 | $0.00216 |
| Haiku 4.5 | $0.00001 | $0.00108 |
Grade A, and why
mcp-tasks-optimize-prompts 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 10d 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.
The source is not reproduced here
Licensed EPL-2.0
The repository is licensed EPL-2.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 10d ago First seen · 152 lines · 9 tokens per session scan A 69db0d5af909
mcp-tasks-optimize-prompts is a command published in the GitHub repository hugoduncan/mcp-tasks (11 stars, last pushed 6mo ago), licensed EPL-2.0. It adds 9 tokens to every session and 1,081 once invoked, about $0.0000 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 commands, from other repositories
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
prompt-show
Display full details of a saved prompt by ID.
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Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
ai
Load the Kaizen skill for production-ready AI agent implementation with signature-based programming and multi-agent coordination.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.