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 lucasmaher-hash/touch-designer-mcp --skill tdmcp-implementation-learninggit clone --depth 1 https://github.com/lucasmaher-hash/touch-designer-mcpWrote 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/lucasmaher-hash/touch-designer-mcp/tdmcp-implementation-learning)<a href="https://agentmods.dev/skills/lucasmaher-hash/touch-designer-mcp/tdmcp-implementation-learning"><img src="https://agentmods.dev/badge/skills/lucasmaher-hash/touch-designer-mcp/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/lucasmaher-hash/touch-designer-mcp/tdmcp-implementation-learning"><img src="https://agentmods.dev/badge/skills/lucasmaher-hash/touch-designer-mcp/tdmcp-implementation-learning.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 | $0.00113 | $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.
This is a copy
100% identical to tdmcp-implementation-learning — 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 — 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 lucasmaher-hash/touch-designer-mcp (0 stars, last pushed 3mo 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. It is 100% identical to tdmcp-implementation-learning, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…