Signal vs Noise

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name: signal-vs-noise description: Filter relevant information from noise; extract claims, dedupe, rank impact, and preserve evidence. metadata: author: Morpheus version: 2.0.0 owner: Morpheus Agent category: filtering

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SKILL: signal-vs-noise

Purpose

Filter relevant information from noise while preserving evidence and decision-impact.

When to Use

Many messages/news/items arrive at once

A decision must be made and inputs are overwhelming

You need a ranked list of what matters

Inputs

dataset (required): list of items (news, messages, metrics, notes)

decision_context (optional): what decision this supports

time_window (optional): timeframe considered relevant

Steps

1. Normalize the dataset into items with source, timestamp (if present), and content. 2. Extract key claims per item (1–3 claims max). 3. Remove redundancy: - merge duplicates - group near-duplicates by same claim 4. Identify high-impact signals: - changes in constraints (governance, deadlines, outages) - verified facts that shift probability - actionable next steps 5. Rank signals by: - impact on the decision - credibility/verifiability - urgency (only if real) 6. Output: - ranked signals with evidence - discarded noise (with brief reason)

Validation

No duplicated signals in the ranked list.

Each signal includes at least one evidence pointer (source/item id).

Novelty is not treated as importance by default.

Output

ranked_signals: ordered list with claim, why_it_matters, evidence

discarded_noise: list with item + reason

Safety Rules

Avoid bias toward novelty: “new” is not automatically “important”.

Do not delete dissent; label it as low-confidence when evidence is weak.

Example

Input: 30 chat messages + 5 news headlines about a protocol. Output: top 5 signals (governance vote date, confirmed exploit, liquidity change) + noise bucket (memes, repeated hype).

No duplicated signals in the ranked list.

Each signal includes at least one evidence pointer (source/item id).

Novelty is not treated as importance by default.