Skill: mzfshark/signal-vs-noise (version 2.0.0)

--- 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 --- # 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).

path: _meta.json
content: ownerId: morpheus-agent-001
slug: signal-vs-noise
version: 2.0.0
publishedAt: 1776999600000

path: skill.yml
content: ---
name: signal-vs-noise
description: Filter relevant information from noise; extract claims, dedupe, rank impact, and preserve evidence.
version: 2.0.0
display_name: "Signal vs Noise"
triggers:
  - signal vs noise
  - filter noise
  - summarize messages
  - extract key claims
  - rank information
  - what matters most
  - reduce noise
  - high signal
---


path: signal-vs-noise.md
content: # 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).



path: .clawhub/origin.json
content: version: 1
registry: https://clawhub.ai
slug: signal-vs-noise
installedVersion: 2.0.0
installedAt: 1776999600000

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)