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 mixer-scene-contractgit 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/mixer-scene-contract)<a href="https://agentmods.dev/skills/pantani/tdmcp/mixer-scene-contract"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/mixer-scene-contract/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/mixer-scene-contract"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/mixer-scene-contract.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 | $0.00084 | $0.00384 |
| Opus 5 | $0.00042 | $0.00192 |
| Sonnet 5 | $0.00017 | $0.00077 |
| Haiku 4.5 | $0.00008 | $0.00038 |
Grade A, and why
mixer-scene-contract 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:
- mixer-scene-contract — 100% identical, 0 lines differ
- mixer-scene-contract — 100% identical, 0 lines differ
What it actually says
mixer-scene-contract
Design the structured contract for approved mixer scene changes. This skill is for design/spec work, not implementation.
Context to read
src/automation/showDirectorSchema.tssrc/automation/showDirectorRuntime.tssrc/cli/agent.tsaroundshow-directortests/unit/showDirector.test.tstests/unit/cliAgent.test.ts
Design rules
- Keep mixer scene changes separate from generic
arm_effect. - The MVP operation is "arm scene/cue for approval", not autonomous execution.
- Require explicit names or IDs:
show_name,snapshot_name,cue_name,scene_id, orsetlist_ref. Do not design fuzzy live lookup as execution. - Preserve blocked/operator-only semantics for
mixer_gain,pa_mute, andaudio_routing. - Every accepted request needs an audit entry with request, intent, decision, approval ID, operator, and adapter target.
Output
Write _workspace/ai-party-mixer/01_contract.md with:
- proposed schema fields and examples;
- approval and audit model;
- CLI dry-run examples;
- mapping to action plans;
- compatibility notes;
- unit-test checklist.
Quality bar
The result should be specific enough that a builder can implement it without asking what fields exist or how approval flows.
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 · 45 lines · 84 tokens per session scan A 71d0d375a8c4
mixer-scene-contract is a skill published in the GitHub repository Pantani/tdmcp (41 stars, last pushed 27d ago), licensed MIT. It adds 84 tokens to every session and 384 once invoked, about $0.0004 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
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-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
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.
terraform-module-library
Build reusable Terraform modules for AWS, Azure, GCP, and OCI infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, or implementing reusable IaC components.
telnyx-iot-curl
Manage IoT SIM cards, eSIMs, data plans, and wireless connectivity. Use when building IoT/M2M solutions. This skill provides REST API (curl) examples.
telnyx-networking-curl
Configure private networks, WireGuard VPN gateways, internet gateways, and virtual cross connects. This skill provides REST API (curl) examples.