Menu

Individual skill

s4h-sensory-signal-detection

0upvotes

Separates meaningful signal from background noise — finding what actually matters among everything present. Triggers: 'what actually matters here', 'separate signal from noise', 'too much information', 'find the signal', 'what should I focus on', 'what's relevant'.

skills/s4h-sensory-signal-detection/SKILL.md

Read pinned source

Source commit a30df8b67698ccb03ef5e35d68c2eddc5321ad3f

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
Yes
Observed behavior
Not checked yet
Last checked
Nov 5, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
240
Repository last updated
Jul 15, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# 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"
Back to top