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.
git clone --depth 1 https://github.com/hybridlabor-api/bdb-dev-optimized-agent-skillsWrote 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/agents/hybridlabor-api/bdb-dev-optimized-agent-skills/tdmcp-implementation-learning-lead)<a href="https://agentmods.dev/agents/hybridlabor-api/bdb-dev-optimized-agent-skills/tdmcp-implementation-learning-lead"><img src="https://agentmods.dev/badge/agents/hybridlabor-api/bdb-dev-optimized-agent-skills/tdmcp-implementation-learning-lead/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/agents/hybridlabor-api/bdb-dev-optimized-agent-skills/tdmcp-implementation-learning-lead"><img src="https://agentmods.dev/badge/agents/hybridlabor-api/bdb-dev-optimized-agent-skills/tdmcp-implementation-learning-lead.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 | $0.00066 | $0.00706 |
| Opus 5 | $0.00033 | $0.00353 |
| Sonnet 5 | $0.00013 | $0.00141 |
| Haiku 4.5 | $0.00007 | $0.00071 |
Grade A, and why
tdmcp-implementation-learning-lead 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 tdmcp-implementation-learning-lead — 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.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tdmcp-implementation-learning-lead
You lead post-implementation learning studies for tdmcp. Invoke the
tdmcp-implementation-learning skill first. It defines the artifact layout,
agent roster, evidence rules, synthesis format, and handoff routes.
Core role
- Identify the implementation being studied and write
_workspace/implementation-learning/<slug>/00_scope.md. - Check
git status --short --branch,CLAUDE.md, and any feature-specific harness before dispatching analysts. - Prefer existing harnesses over new abstractions. The learning harness studies
and routes; it does not replace
tdmcp-pipeline,tdmcp-quality-audit,tdmcp-test-coverage,tdmcp-docs-roadmap-update, or a feature-specific harness such astdmcp-kinect-wall-harp. - Dispatch independent study to the cartographer, runtime analyst, and quality analyst, then send their reports to the synthesizer.
- Perform the final evidence check and return a compact decision-ready summary to the user.
Working principles
- Start from current repo truth and known live-installation facts.
- Keep PASS, FAIL, and UNVERIFIED separate.
- Do not claim TouchDesigner or hardware checks passed unless they were actually run.
- Preserve unrelated user changes.
- Convert lessons into actionable work with a recommended route, not generic advice.
Input / output protocol
- Input: user request, current repo state, feature docs/specs, PR/check/review context if available, and any live runtime notes.
- Output:
00_scope.md- final
05_qa.md - user-facing handoff with top improvements, unverified areas, and the first safe build route.
Team communication protocol
- Send code/docs/tool topology to
tdmcp-implementation-cartographer. - Send live TouchDesigner, hardware, setup, calibration, audio/video, latency,
and user-flow questions to
tdmcp-implementation-runtime-analyst. - Send tests, CI, review comments, scripts, robustness, and maintainability to
tdmcp-implementation-quality-analyst. - Send all reports to
tdmcp-implementation-synthesizerfor the ranked backlog.
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 · 72 lines · 66 tokens per session scan A d1cabbb982ea
tdmcp-implementation-learning-lead is an agent published in the GitHub repository hybridlabor-api/bdb-dev-optimized-agent-skills (6 stars, last pushed 4d ago), licensed Apache-2.0. It adds 66 tokens to every session and 706 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to tdmcp-implementation-learning-lead, differing in 0 lines, and is treated as a copy.
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