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-hype-scoutgit 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-hype-scout)<a href="https://agentmods.dev/skills/pantani/tdmcp/tdmcp-hype-scout"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/tdmcp-hype-scout/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-hype-scout"><img src="https://agentmods.dev/badge/skills/pantani/tdmcp/tdmcp-hype-scout.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 | $0.00276 | $0.02632 |
| Opus 5 | $0.00138 | $0.01316 |
| Sonnet 5 | $0.00055 | $0.00526 |
| Haiku 4.5 | $0.00028 | $0.00263 |
Grade A, and why
tdmcp-hype-scout 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 8d 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-hype-scout — 100% identical, 0 lines differ
- tdmcp-hype-scout — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tdmcp-hype-scout — external trend ideation orchestrator
Coordinate a fan-out of trend scouts + one synthesizer to produce a single prioritized hype tool backlog for tdmcp, grounded in cited community evidence and vetted against the real codebase. This harness finds and ranks trend-driven tool ideas; the tdmcp-pipeline harness builds the chosen ones. Keep the boundary crisp: hype-scout answers "what's the TD community hyped about, and which of that could we easily turn into a tool?", pipeline answers "build this tool".
Boundary vs tdmcp-feature-discovery: that skill does internal gap analysis (what's missing in tdmcp vs roadmap). This skill does external hype analysis (what's trending in the TD community). They are complementary — both can run and feed into each other.
Execution mode: sub-agent fan-out → fan-in
| Stage | Mode | Why |
|---|---|---|
| Scout (×5) | sub-agent (fan-out, parallel) | scouts are fully isolated — each owns one surface, no inter-comms needed; mirrors the proven tdmcp-feature-discovery shape |
| Synthesize | sub-agent (×1) | a single reasoning-heavy consolidation pass over result files — no producer↔reviewer loop, so no team needed |
No TeamCreate here — scouts pass results 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-trend-scout (×up to 5) |
custom | td-trend-scout |
_workspace/hype-scout/01_scout_<surface>.md |
td-hype-synthesizer |
custom | td-hype-synthesize |
_workspace/hype-scout/HYPE_TOOL_BACKLOG.md |
The five surfaces:
community-showcase— TD forum, Instagram, Vimeo (finished work)tutorials— YouTube channels + courses (what's being taught now)generative-ai— StreamDiffusion/ComfyUI/realtime-ML bridges into TDhardware-interactive— LiDAR / depth / hand-tracking / sensorsvfx-aesthetics— dominant visual languages of 2025-2026
Workflow
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.
- 8d ago First seen · 163 lines · 276 tokens per session scan A 44e611a6f25b
tdmcp-hype-scout is a skill published in the GitHub repository Pantani/tdmcp (39 stars, last pushed 27d ago), licensed MIT. It adds 276 tokens to every session and 2,632 once invoked, about $0.0014 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 2026-09-03.
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