s4h-sensory-structured-observation
Applies disciplined observation to a situation — suspending interpretation to see what's actually there before deciding what it means.
Skill by human-avatar
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/human-avatar-s4h-sensory-structured-observation
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/s4h-sensory-structured-observation
What's inside
- Skill name
s4h-sensory-structured-observation- Description
- Applies disciplined observation to a situation — suspending interpretation to see what's actually there before deciding what it means. Triggers: 'observe this carefully', 'structured observation', 'what do you actually see', 'suspend interpretation', 'look more carefully'.
- License
- MIT
- Instructions
- 103 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# 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 These are the most information-rich observations — prioritise them. **Step 5: Generate Interpretations** Only after completing Steps 2–4: generate multiple possible interpretations for each key observation. Aim for at least two competing explanations. **Step 6: Identify the Most Testable Interpretation** Which interpretation makes the most specific, falsifiable prediction? That is the one to act on first. --- ## Human Check-in Before proceeding, use the `AskUserQuestion` tool. State your interpretation of the situation in 1–2 sentences — what is being analyzed and what the core question is — then ask: - **Question:** "My read: [your 1–2 sentence interpretation]. How do you want to proceed?" - **Header:** "Scope" - **Options:** - **Full analysis** — Complete all steps, reasoning shown throughout - **Key findings only** — Bottom-line output, skip step-by-step detail - **Initial observations only** — What's actually there before any interpretation is applied - **Reframe** — The read is off; correct it and the analysis will follow the corrected framing Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis. ## Output Format ### Observations | Level | Observation (no interpretation) | |-------|----------------------------------| | Event | ... | | Pattern | ... | | Absence | ... | ### Surprising or Incongruent Observations - List each, with a note on why it is surprising. ### Interpretations per Key Observation | Observation | Interpretation A | Interpretation B | |-------------|-----------------|-----------------| | ... | ... | ... | ### Most Testable Interpretation State it as a prediction: "If [interpretation] is correct, then [specific observable consequence]." --- ## Notes Run this before diagnosis, analysis, or decision. The discipline has most value when you feel you already understand the situation — that feeling is usually a sign that interpretation has already overtaken observation. --- ## What's Next After delivering this output, use `AskUserQuestion` to offer the next move: - **Question:** "Observation complete. What's next?" - **Header:** "Next" - **Options:** - `/s4h-sensory-detail-mining` — Mine details from what structured observation revealed - `/s4h-sensory-signal-detection` — Detect signals in the observed - `/s4h-aesthetic-coherence-check` — Check coherence of what was observed - **Done** — Wrap up and synthesise what we have so far
From human-avatar/skills-for-humanity, MIT.
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Signals
- Stars
- 235
- Forks
- 24
- Open issues
- 0
- Last commit (this folder)
- May 31, 2026 (5 months ago)
- Last release
- May 31, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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s4h-sensory-structured-observation skill. Agent Mart. https://agentmart.dev/skills/human-avatar-s4h-sensory-structured-observation (last updated 2026-10-24)