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-information-signal-noisegit 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-information-signal-noise)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-information-signal-noise"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-information-signal-noise/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-information-signal-noise"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-information-signal-noise.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 | $0.00066 | $0.01679 |
| Opus 5 | $0.00033 | $0.00839 |
| Sonnet 5 | $0.00013 | $0.00336 |
| Haiku 4.5 | $0.00007 | $0.00168 |
Grade A, and why
s4h-information-signal-noise 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Information: Signal–Noise
Every source is a mixture of signal and noise. Signal is the variation that carries information about what you care about. Noise is everything else — random variation, artefacts, irrelevant fluctuation, measurement error, distortion in the channel. The same data point can be signal in one context and noise in another; the same message can be clear in one medium and garbled in another.
Claude Shannon's foundational contribution was showing that signal-to-noise ratio (SNR) is a precisely definable quantity, and that a channel's capacity to transmit information is mathematically bounded by its SNR. The insight travels well beyond telecommunications. Any situation where useful information must be extracted from a noisy background has the same structure: evidence vs. noise in a research base, insight vs. artefact in a dataset, the core message vs. verbal interference in a communication, the relevant metric vs. random fluctuation in a business dashboard. This skill applies SNR thinking to find what's actually there.
Norbert Wiener's cybernetics framework added the feedback dimension: systems that can detect and suppress their own noise are more robust. The question is not just "what is the signal?" but "what is the system doing to amplify or attenuate it?"
Your Process
Step 1: Define Signal Before anything else, get precise about what you are trying to detect. "Signal" is not "useful information in general" — it is the specific variation or pattern that would update your picture of the thing that matters. Name the target signal explicitly: what would a perfect source of this signal look like?
Framing check: Confirm the target signal and the source before continuing. State what you've identified — what signal you're looking for, in what source, and why it matters — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the signal, source, and purpose]. 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
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 · 121 lines · 66 tokens per session scan A a8c3841ecdaa
s4h-information-signal-noise is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 1,679 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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