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 matteotitta/genesys-skills --skill signal-scangit clone --depth 1 https://github.com/matteotitta/genesys-skillsWrote 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/matteotitta/genesys-skills/signal-scan)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/signal-scan"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/signal-scan/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/matteotitta/genesys-skills/signal-scan"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/signal-scan.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 | $0.00005 | $0.02234 |
| Opus 5 | $0.00003 | $0.01117 |
| Sonnet 5 | $0.00001 | $0.00447 |
| Haiku 4.5 | $0.00001 | $0.00223 |
Grade A, and why
signal-scan 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 9d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Signal scan
Answer "what happened with {topic} in the last N days?" for any person, company, product, technology, or market term. The brief is ranked by what people actually engaged with — upvotes, likes, views, GitHub stars, prediction-market odds — not by SEO. Knowledge type: temporal-signal-brief (per .claude/rules/ontology.md); maturity: emergent (each run is fresh, time-bound — briefs are not locked).
Stolen MCP-native from mvanhorn/last30days-skill (MIT). The method is the steal — entity-resolution-before-search, peer expansion, story clustering, engagement ranking. The 1,036-line Python engine is not: every surface maps to an MCP we already run (Exa, Firecrawl, GitHub, youtube-transcript, Apify). Provenance + verdict table: .claude/discovery/0626-last30days-skill-steal-analysis.md.
When to run
Invoke for: what happened with {topic} in the last 30 days, recent activity on {company}, what's new with {technology}, signal scan on {market}, pre-call prospect prep, weekly newsletter sourcing, competitor recency checks.
Do NOT invoke for:
- Internal decision history / "what did we decide" →
/think(searches our own decision logs). - "What did we discuss in past sessions" →
/recall(session DB). - A deep, static, 13-dimension competitor dossier →
/competitor-research. (This skill is shallow + recent + topic-agnostic; it feeds that skill's "Recent changes" header.) - A curated newsletter from links you already collected →
/gtme-pulse(this skill sources the raw signal that feeds it).
Brain-first (mandatory): before any external call, run the .claude/rules/brain-first-lookup.md ladder — /recall {topic} + grep client folders. A locked client doc may already hold what a scan would rediscover. Annotate the brief if you went external after a brain miss.
Inputs + flags
Required: topic — the thing to scan (person / company / product / technology / market term). If ambiguous (e.g. "Pivot", "Base", "Bolt"), confirm the disambiguator before running.
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.
- 9d ago First seen · 134 lines · 238 tokens per session scan A 6ed559bea605
signal-scan is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 2,234 once invoked, about $0.0000 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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