signal-vs-noise-filter

name	signal-vs-noise-filter
description	Use when a situation contains many facts, feelings, events, rumors, or weak cues and the real signal must be separated from noise. Use when applying signal detection, signal-versus-noise filtering, pattern relevance review, or decision-signal extraction.
license	Apache-2.0

Signal vs Noise Filter

Use this skill to identify which observations should change the diagnosis or action plan.

Method Notes

• A signal is information that changes the likely explanation, priority, risk, timing, or next action.
• Noise may be true but not decision-relevant.
• The method works best after an initial evidence map or fact clarification.

Required Inputs

Collect or infer these inputs before execution:

• decision question or action choice
• list of observations, events, facts, cues, and concerns
• baseline expectation before the event
• timing of key changes
• available evidence strength

If an input is missing, mark it as missing, state the assumption used, and add a validation action.

When Not To Use

Do not use to repair unreliable evidence or fill basic situation gaps. Use evidence-map when facts, claims, sources, and confidence are mixed; use 5w1h-analysis when who, what, when, where, why, or how is missing. Use this method only after the input is good enough to judge decision relevance.

Step-by-Step Execution

Step	Required input	How to execute	Output
State the baseline	Prior expectation, normal pattern, status quo.	Clarify what would have happened if nothing material changed.	Baseline expectation.
List candidate signals	Observations, events, user concerns, timing.	Convert each item into a candidate signal. Remove duplicates.	Candidate signal list.
Rate decision relevance	Decision question, action options.	Ask whether this item changes explanation, priority, risk, timing, stakeholder stance, or validation need.	Relevance rating.
Rate evidence strength	Source, directness, recency, corroboration.	Mark each candidate as strong, medium, weak, or missing.	Evidence-strength rating.
Separate noise	Low-relevance or low-evidence items.	Keep true-but-not-actionable items in a noise list so they do not dominate the plan.	Noise list.
Name the core signal	High-relevance and sufficiently evidenced items.	Select the 1-3 signals most likely to change action.	Core signal diagnosis.

Output Template

### 1. Baseline
- What we expected before the change:
- What changed:
- Why this matters:

### 2. Candidate Signal Table
| Candidate signal | Evidence strength | Decision relevance | Changes what? | Signal / noise / watchlist |
|---|---|---|---|---|
|  | strong / medium / weak / missing | high / medium / low | explanation / priority / risk / timing / stakeholder / validation |  |

### 3. Core Signals
1.
2.
3.

### 4. Noise and Watchlist
- Noise to avoid overreacting to:
- Watchlist item that needs more evidence:

### 5. Action Implication
- What the signal means:
- What it does not prove:
- Next validation:

Quality Gate

• A signal must change a decision, not only feel interesting.
• Do not downgrade a real signal because it is politically uncomfortable.
• Do not upgrade a weak cue because it fits the preferred story.
• Name what the signal does not prove.
• Keep the final signal list short enough to guide action.