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 tdmcp-implementation-learninggit 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/tdmcp-implementation-learning)<a href="https://agentmods.dev/skills/pantani/tdmcp/tdmcp-implementation-learning"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/tdmcp-implementation-learning/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/tdmcp-implementation-learning"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/tdmcp-implementation-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00113 | $0.01989 |
| Opus 5.5 | $0.00045 | $0.00796 |
| Sonnet 5.5 | $0.00023 | $0.00398 |
| Haiku 4.5 | $0.00011 | $0.00199 |
Grade A, and why
tdmcp-implementation-learning 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 1mo 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:
- tdmcp-implementation-learning — 100% identical, 0 lines differ
- tdmcp-implementation-learning — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tdmcp-implementation-learning - post-implementation learning harness
Coordinate a focused study of a completed tdmcp implementation, then turn the evidence into a prioritized improvement backlog. This harness answers: "What did this implementation teach us, and what should tdmcp improve next?"
Use it after a feature has already been built, merged, tested in TouchDesigner, used with hardware, reviewed in a PR, or exercised in a real installation.
Boundary
This harness studies and routes improvements. It does not own arbitrary feature implementation.
- Shipped/merged implementation learning:
tdmcp-implementation-learning. - Chosen new feature build:
tdmcp-pipeline. - Broad repo quality or command health:
tdmcp-quality-audit. - Known coverage gaps:
tdmcp-test-coverage. - Docs, roadmap, or changelog sync:
tdmcp-docs-roadmap-update. - Continued Kinect wall harp work:
tdmcp-kinect-wall-harp.
Execution mode: sub-agent fan-out -> fan-in
No TeamCreate. Use coordinated sub-agents with file handoffs.
- Scope: lead only. Determine target feature, evidence sources, and artifact directory.
- Study: sub-agent fan-out. Code, runtime, and quality surfaces can be inspected independently.
- Synthesis: one sub-agent. One owner dedupes findings and ranks next actions.
- Handoff: lead only. The user gets a compact decision-ready report.
All agent calls use model: "opus" unless the caller has a stricter local
policy.
Agent roster
tdmcp-implementation-learning-lead:_workspace/implementation-learning/<slug>/00_scope.mdand final handoff.tdmcp-implementation-cartographer:_workspace/implementation-learning/<slug>/01_map.md.tdmcp-implementation-runtime-analyst:_workspace/implementation-learning/<slug>/02_runtime_lessons.md.tdmcp-implementation-quality-analyst:_workspace/implementation-learning/<slug>/03_quality_gaps.md.tdmcp-implementation-synthesizer:_workspace/implementation-learning/<slug>/04_backlog.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.
- 1mo ago First seen · 236 lines · 113 tokens per session scan A 152515235e66
tdmcp-implementation-learning is a skill published in the GitHub repository Pantani/tdmcp (55 stars, last pushed 2mo ago), licensed MIT. It adds 113 tokens to every session and 1,989 once invoked, about $0.0005 per session on Opus 5.5. 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.
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