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-feature-discoverygit 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-feature-discovery)<a href="https://agentmods.dev/skills/pantani/tdmcp/tdmcp-feature-discovery"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/tdmcp-feature-discovery/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-feature-discovery"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/tdmcp-feature-discovery.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 | $0.00197 | $0.01976 |
| Opus 5 | $0.00098 | $0.00988 |
| Sonnet 5 | $0.00039 | $0.00395 |
| Haiku 4.5 | $0.00020 | $0.00198 |
Grade A, and why
tdmcp-feature-discovery 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 12d 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-feature-discovery — 100% identical, 0 lines differ
- tdmcp-feature-discovery — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tdmcp-feature-discovery — new-feature ideation orchestrator
Coordinate a fan-out of surveyors + one synthesizer to produce a single prioritized feature backlog for tdmcp, deduped and reconciled against the roadmap. This harness finds and ranks ideas; the tdmcp-pipeline harness builds the chosen ones. Keep the boundary crisp: discovery answers "what should we consider next?", pipeline answers "build this."
Execution mode: sub-agent fan-out → fan-in
| Stage | Mode | Why |
|---|---|---|
| Survey | sub-agent (fan-out, parallel) | the five surveyors are fully isolated — each owns one surface, no inter-comms needed; the textbook fan-out case (mirrors the pipeline's design/build stages) |
| Synthesize | sub-agent (×1) | a single reasoning-heavy consolidation pass over result files — no producer↔reviewer loop, so no team needed |
No TeamCreate here — surveys are pure result-passing via files, so sub-agents are the right tool over team overhead. All Agent calls use model: "opus".
Agent roster
| Agent | Type | Skill | Output |
|---|---|---|---|
td-surveyor (×up to 5) |
custom | td-feature-survey |
_workspace/discovery/01_survey_<surface>.md |
td-synthesizer |
custom | td-feature-synthesize |
_workspace/discovery/FEATURE_BACKLOG.md |
The five surfaces: controls (Layer 1/2 creation & performance), library (vault + recipes + .tox/component packaging + distribution), cli (CLI/DX), ai (prompts + local-LLM copilot), td-depth (Layer 3 + bridge + operator KB).
Workflow
Phase 0 — context check (follow-up support)
- Check whether
_workspace/discovery/exists. - Decide the run mode:
- No
_workspace/discovery/→ fresh run. Go to Phase 1. - Exists + user asks to refresh/deepen/re-prioritize part → partial re-run. Re-invoke only the affected surveyor(s) and/or the synthesizer, passing prior artifact paths so they refine rather than rewrite.
- Exists + a materially new ask (e.g. post-release, new competitor) → new run. Move the old dir to
_workspace/discovery_<YYYYMMDD_HHMMSS>/, then Phase 1.
- No
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.
- 12d ago First seen · 92 lines · 197 tokens per session scan A 730a2b0f7948
tdmcp-feature-discovery is a skill published in the GitHub repository Pantani/tdmcp (39 stars, last pushed 27d ago), licensed MIT. It adds 197 tokens to every session and 1,976 once invoked, about $0.0010 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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