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 varunk130/ai-gtm-skill-library --skill signal-radargit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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/varunk130/ai-gtm-skill-library/signal-radar)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/signal-radar"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/signal-radar/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/varunk130/ai-gtm-skill-library/signal-radar"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/signal-radar.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 | $0.00048 | $0.01105 |
| Opus 5.5 | $0.00019 | $0.00442 |
| Sonnet 5.5 | $0.00010 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
signal-radar 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.
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.
Signal Radar (PULSE Framework)
Detect, classify, and score macro-market signals before competitors act. Surfaces technology shifts, regulatory changes, buyer behavior evolution, and ecosystem dynamics.
When to Use
- Quarterly strategic planning
- Pre-launch market assessment
- Competitive threat monitoring
- New market entry evaluation
- Board-level market briefings
What You'll Need
Critical inputs (ask if not provided):
- Industry/market to monitor
- Product category or domain
- Known competitors (top 3-5)
Nice-to-have:
- Recent analyst reports or news
- Internal customer feedback themes
- Current strategic priorities
Process
Step 1: Signal Collection
Classify every market signal into 5 vectors:
| Vector | What It Tracks | Examples |
|---|---|---|
| T-Signals (Technology) | New protocols, platforms, standards, AI capabilities | "New foundation model released", "New API standard adopted" |
| R-Signals (Regulatory) | Compliance mandates, policy changes, industry standards | "GDPR enforcement update", "AI regulation passed" |
| B-Signals (Buyer behavior) | Purchasing patterns, channel preferences, budget shifts | "Buyers demanding self-serve trials", "CFO approval now required" |
| E-Signals (Ecosystem) | Partner moves, supply chain changes, platform updates | "Company A acquires competitor", "Company B launches competing service" |
| C-Signals (Cultural) | Workforce trends, social attitudes, macro-economic shifts | "Remote work permanent", "AI trust concerns rising" |
For each signal found, document:
- Signal description (what happened)
- Source and date
- Vector classification (T/R/B/E/C)
- Initial relevance assessment
Step 2: PULSE Scoring
Score each signal on 5 dimensions (1-10):
| Dimension | What It Measures | Scoring Guide |
|---|---|---|
| Pattern | Is this a one-off or repeating pattern? | 1=isolated event, 5=emerging trend, 10=established pattern |
| Urgency | Time horizon before impact | 1=years away, 5=6-12 months, 10=imminent (under 3 months) |
| Leverage | How much can we exploit this? | 1=no fit, 5=moderate advantage, 10=perfect strategic fit |
| Scope | How many segments/geos affected? | 1=niche, 5=our core market, 10=entire industry |
| Evidence | Quality of supporting data | 1=rumor, 5=multiple credible sources, 10=confirmed with data |
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 · 92 lines · 48 tokens per session scan A e873af6c0cfd
signal-radar is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 48 tokens to every session and 1,105 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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