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 td-feature-surveygit 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/td-feature-survey)<a href="https://agentmods.dev/skills/pantani/tdmcp/td-feature-survey"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/td-feature-survey/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/td-feature-survey"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/td-feature-survey.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 | $0.00096 | $0.01925 |
| Opus 5 | $0.00048 | $0.00962 |
| Sonnet 5 | $0.00019 | $0.00385 |
| Haiku 4.5 | $0.00010 | $0.00193 |
Grade A, and why
td-feature-survey 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 11d 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:
- td-feature-survey — 100% identical, 0 lines differ
- td-feature-survey — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
td-feature-survey — scout one surface for new features
You are surveying one assigned surface of tdmcp and returning a grounded, generous list of features it could gain. Breadth + grounding is the goal; the td-synthesizer prunes and ranks later. Work the surface methodically so you neither miss obvious gaps nor re-propose shipped tools.
Procedure
1. Confirm the surface
Your assignment is one of controls, library, cli, ai, td-depth. Survey only that surface. If you spot a strong idea on another surface, drop it in a one-line "cross-surface" footnote — don't fully work it up (its owner will).
2. Inventory what already exists
Read your surface's source (map below) and list what ships today. You cannot propose something that already exists, so build the "exists" set first. For tools, the registry under src/tools/ is authoritative; for CLI, src/cli/agent.ts; for prompts, src/prompts/; for operators, the KB.
3. Roadmap pass
Read docs/ROADMAP.md. Note every item on your surface that is shipped (☑), in progress (◐), or planned (☐ — Phase 13 / "deferred to v0.6.0+"). You will label candidates against this.
4. Gap-finding lenses
Run every candidate idea through these lenses — a good survey uses all of them, not just one:
| Lens | Question | Where it bites for tdmcp |
|---|---|---|
| Competitor parity | What do 8beeeaaat / Embody / dotsimulate LOPs have that we don't? | network-as-JSON round-trip, token-cheap reads, annotations, perform mode |
| Untapped TD capability | Which TD operators / Python APIs / bridge powers aren't wrapped yet? | operators absent from the create-able set; bridge endpoints (logs, process, events) |
| Artist-workflow hole | What's painful or missing in a real live show? | the VJ/live thesis — audio/beat/camera reactivity, recovery, hands-free, output |
| DX / token cost | What makes an agent slow, expensive, or error-prone here? | compact reads, batch ops, surgical edits, diagnostics |
| AI leverage | What could the model do that no prompt/tool exposes? | multimodal critique, repair loops, set planning, project explain |
| Polish / robustness | What breaks trust at the edges? | export fidelity, safety/panic, portable bundles, validation |
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.
- 11d ago First seen · 81 lines · 96 tokens per session scan A dd68df173187
td-feature-survey is a skill published in the GitHub repository Pantani/tdmcp (39 stars, last pushed 26d ago), licensed MIT. It adds 96 tokens to every session and 1,925 once invoked, about $0.0005 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 2026-08-30.
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