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 human-avatar/skills-for-humanity --skill s4h-sensory-signal-detectiongit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-sensory-signal-detection)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-sensory-signal-detection"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-sensory-signal-detection/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/human-avatar/skills-for-humanity/s4h-sensory-signal-detection"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-sensory-signal-detection.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 | $0.00065 | $0.01121 |
| Opus 5 | $0.00032 | $0.00561 |
| Sonnet 5 | $0.00013 | $0.00224 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
s4h-sensory-signal-detection 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Signal Detection
In any rich environment — data, feedback, conversation, a market — most of what is present is noise. Signal is what varies with the thing you're trying to understand; noise varies independently. The challenge is not finding more information, it's knowing which information is doing real work.
Your Process
Step 1: Inventory Everything Present List all the data, observations, or inputs available. Don't filter yet — complete the inventory first.
Framing check: Confirm the specific subject before continuing. State what you've identified — the actual environment or dataset being analyzed and what outcome or phenomenon the user is trying to understand — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific environment/dataset and the outcome you're testing against]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Variance Test For each item: does it vary with the outcome or phenomenon you're trying to understand? Signal co-varies with what you care about. Noise varies on its own schedule.
Step 3: Persistence Test Is this item consistently present across time and contexts, or did it appear once? Persistent patterns are more likely to be signal. One-off observations may be noise, anomaly, or coincidence.
Step 4: Specificity Test Is this item unique to this situation, or is it always present? Always-present background conditions are usually noise. What is specific to the case is more likely signal.
Step 5: Counterfactual Test If this item changed or disappeared, would the outcome change? If yes: probable signal. If the outcome would be the same regardless: probable noise.
Step 6: Classify and Summarise Assign a classification to each item and present the full classified inventory to the user.
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 · 102 lines · 65 tokens per session scan A 3bd7eb1d5fb0
s4h-sensory-signal-detection is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 1,121 once invoked, about $0.0003 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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