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-structured-observationgit 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-structured-observation)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-sensory-structured-observation"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-sensory-structured-observation/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-structured-observation"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-sensory-structured-observation.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 | $0.00062 | $0.01096 |
| Opus 5 | $0.00031 | $0.00548 |
| Sonnet 5 | $0.00012 | $0.00219 |
| Haiku 4.5 | $0.00006 | $0.00110 |
Grade A, and why
s4h-sensory-structured-observation 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structured Observation
Most observation is interpretation in disguise. We perceive a situation and instantly explain it — but the explanation overwrites the raw data. Structured observation forces a separation between what can be directly seen and what we conclude from it.
Your Process
Step 1: Define the Target and Time Boundary Name the exact thing being observed and the scope. What counts as inside this observation, and what is out of scope?
Framing check: Confirm the specific subject before continuing. State what you've identified — the actual object being observed and its boundaries — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific subject and scope]. 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: Separate Observation from Interpretation Write only what can be directly observed — no inferences, no attributions of intent or cause. "User clicked back immediately" not "user was confused." Flag every sentence that is actually an inference and set it aside.
Step 3: Observe at Three Levels
- Events — what is happening? Discrete, specific occurrences.
- Patterns — how is it happening? Recurring structure across events.
- Absences — what is not happening that might be expected?
Step 4: Flag Surprising or Incongruent Observations What doesn't fit? Where does something contradict expectations?
Before narrowing: Show the complete set of observations from Steps 2–3 to the user first. Use AskUserQuestion:
- Question: "I've catalogued [N] observations across events, patterns, and absences. Before I select the most surprising or incongruent, are there any you'd flag as especially important, or any I've missed?"
- Header: "Prioritise"
- Options:
- Proceed with your selection — the set looks right
- Flag one — user will name a specific observation to include
- Add a missing one — user will describe it
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 · 107 lines · 62 tokens per session scan A 6391f52a32f1
s4h-sensory-structured-observation is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 1,096 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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