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 Pantani/tdmcp --skill ai-party-mixer-runbookgit clone --depth 1 https://github.com/Pantani/tdmcpWrote 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/pantani/tdmcp/ai-party-mixer-runbook)<a href="https://agentmods.dev/skills/pantani/tdmcp/ai-party-mixer-runbook"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/ai-party-mixer-runbook/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/pantani/tdmcp/ai-party-mixer-runbook"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/ai-party-mixer-runbook.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 | $0.00061 | $0.00284 |
| Opus 5 | $0.00030 | $0.00142 |
| Sonnet 5 | $0.00012 | $0.00057 |
| Haiku 4.5 | $0.00006 | $0.00028 |
Grade A, and why
ai-party-mixer-runbook 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 9d 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:
- ai-party-mixer-runbook — 100% identical, 0 lines differ
- ai-party-mixer-runbook — 100% identical, 0 lines differ
What it actually says
ai-party-mixer-runbook
Turn the technical design into a practical docs/runbook plan.
Context to read
docs/guide/ai-controlled-party.mddocs/pt/guide/ai-controlled-party.md_workspace/ai-party-mixer/01_contract.md_workspace/ai-party-mixer/02_adapter.md_workspace/ai-party-mixer/03_policy_qa.mdif present
Writing rules
- Keep public claims honest: AI co-pilots and arms, operator approves.
- Explain dry-run, rehearsal, approval, panic/fallback, and live-validation boundaries.
- Keep EN/PT parity in the update plan.
- Separate developer tasks from operator checklists.
Output
Write _workspace/ai-party-mixer/04_runbook_docs.md with docs outline, rehearsal
checklist, show-day checklist, demo moments, safety copy, and build checks.
Quality bar
An operator should be able to rehearse the flow without touching live mixer hardware first.
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.
- 9d ago First seen · 35 lines · 61 tokens per session scan A c82df45b7974
ai-party-mixer-runbook is a skill published in the GitHub repository Pantani/tdmcp (41 stars, last pushed 28d ago), licensed MIT. It adds 61 tokens to every session and 284 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
generate-product-datasheet
Generate a professional product specification sheet with images, feature tables, technical specs, and contact information.
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
honey-px
Read huge read-only text as PNG pages; big input cut.
lov-document-illustrator
A document illustration tool that plans where images fit in an article or note, generates them, and inserts them into the original document. It supports cover images, custom proportions, and three visual styles.
lov-rich-export
A content-export workflow that converts one source document into HTML, Markdown, DOCX, PDF, or an offline delivery package. It accounts for images, audio, video, embedded pages, and interactive charts in each format.