Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Survey one surface of tdmcp (artist controls, library/packaging, CLI/DX, AI/LLM, or TouchDesigner depth) for candidate NEW features — inventory what exists, cross-check the roadmap, apply the gap-finding lenses, vet each idea, and emit a structured, novelty- and confidence-labelled candidate list to _workspace/discovery/. Use when a td-surveyor agent is scouting a surface during the feature-discovery harness.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.
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).
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.
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.
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 |
src/tools/layer1/ (artist generators), src/tools/layer2/ (building blocks: mixing, reactivity, live-control surfaces, animation). Excludes the vault/recipe/packaging concern (→ library). Lenses that bite hardest: artist-workflow, untapped-TD, competitor-parity.src/tools/vault/, the recipe library recipes/ + src/recipes/, and the reusable-component/packaging tools (manage_component .tox save/load, scaffold_extension, add_custom_parameters). The home of the v0.5.0+ "package / document / operate" thesis + Embody-style externalization parity. Lenses: artist-workflow (reuse across shows), competitor-parity, DX. Gaps = library browse/save, portable bundles, templates, marketplace, project docs that live as assets.src/cli/agent.ts (command map), src/index.ts (subcommands: install-bridge, chat/llm-run, doctor, repl, watch, preview), package.json scripts, the installer. Lenses: DX, artist-easy-install, polish.src/prompts/* (every prompt), the local-LLM copilot in src/index.ts (chat/llm-run) + TDMCP_LLM_* in src/utils/config.ts, and the curated tool subset the copilot exposes. Lenses: AI-leverage, DX, competitor-parity.src/tools/layer3/ (atomic CRUD + inspection + raw-python), the Python bridge td/ (REST endpoints, events hook, exec), and the KB src/knowledge/data (resources tdmcp://operators/…). Covers unwrapped operators, parameter-mode/expression fidelity, and bridge powers (logs, process, perform mode) — but not the packaging/reuse layer (→ library). Use search_operators to find operators that exist in TD but aren't wrapped by any tool. Lenses: untapped-TD, DX, competitor-parity.Every candidate gets one label:
<tool>) — a concrete extension of a shipped tool/command/prompt.Write each candidate exactly like this so the synthesizer can parse it:
### <feature_name>
- **Delivers:** <one line — what it does>
- **Why:** <artist / user / DX value — the outcome>
- **Surface:** controls | library | cli | ai | td-depth
- **Layer/target:** <e.g. Layer 1 — src/tools/layer1/<name>.ts · or CLI `cmd` · or prompt · or bridge endpoint>
- **Effort:** S | M | L
- **Impact:** Low | Med | High
- **Confidence:** High | Med | Low <!-- High = gap confirmed against your inventory AND every operator confirmed in the KB; Med = gap clear but an operator/API is unconfirmed; Low = plausible but unverified -->
- **Novelty:** NEW | ROADMAP (Phase X) | EXTENSION (of <tool>)
- **Depends on:** <shipped primitives / other candidates / none>
- **Probe-first risk:** <what must be validated live in TD before locking the API — or "none">
Write the file incrementally so a mid-run interruption (a dropped socket, a timeout) still leaves usable partial work: create _workspace/discovery/01_survey_<surface>.md early with its header, then append each entry as you confirm it — don't hold the whole survey in memory to write once at the end.
The finished file contains:
UNVERIFIED — probe live) the moment an operator or API is unconfirmed. The synthesizer leads with High-confidence items, so this field carries weight.search_operators backing it.还没有评价。欢迎分享你的使用感受。
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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