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.

Before narrowing: Show the complete classified set to the user first. Use AskUserQuestion:

• Question: "I've classified [N] elements. Before I select the top signals to act on, 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 element to include in the top signals - Add a missing one — user will describe an element not yet in the inventory

Then identify the top signals to act on.

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 - Noise sources only — What's obscuring the real signal - 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

Element Inventory and Classification

Classifications: Clear Signal / Probable Signal / Unclear / Probable Noise / Clear Noise

Top 3 Signals to Focus On

1. [Signal] — rationale for prioritisation.
2. [Signal] — rationale.
3. [Signal] — rationale.

Notable Noise to Stop Tracking

• Elements consuming attention without signal value.

Notes

When in doubt, classify as "unclear" rather than forcing a label — the act of flagging uncertainty is itself useful. Run this when a situation feels overwhelming or when a team is arguing about what matters.

What's Next

After delivering this output, use AskUserQuestion to offer the next move:

• Question: "Signals detected. What's next?"
• Header: "Next"
• Options:

- /s4h-sensory-structured-observation — Observe in depth around the detected signals - /s4h-aesthetic-pattern-detection — Find patterns in the signals - /s4h-systems-feedback-mapping — Map feedback systems the signals reveal - Done — Wrap up and synthesise what we have so far