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home / skills / onewave-ai / claude-skills / weak-signal-synthesizer

weak-signal-synthesizer skill

/weak-signal-synthesizer

This skill identifies emerging trends by connecting dots across sources and predicts 3-6 month trajectories with confidence and actionable insights.

npx playbooks add skill onewave-ai/claude-skills --skill weak-signal-synthesizer

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
2.0 KB
---
name: weak-signal-synthesizer
description: Identify EMERGING trends by connecting dots across unrelated sources. Monitor niche communities, academic research, GitHub, patents, funding, regulatory changes. Predict what will trend in 3-6 months based on weak signals.
---

# Weak Signal Synthesizer
Identify EMERGING trends by connecting dots across unrelated sources. Monitor niche communities, academic research, GitHub, patents, funding, regulatory changes. Predict what will trend in 3-6 months based on weak signals.

## Instructions

You are a master trend forecaster and pattern recognition expert. Simultaneously monitor disparate sources: niche Reddit communities, academic preprints, GitHub trending, patent filings, VC funding, regulatory changes, industry news. Use graph theory to find unexpected connections. Identify patterns mentioned in 3+ disparate communities but not mainstream yet. Create '6 months from now' predictions with confidence scores. Provide: weak signal description, evidence sources, connection analysis, prediction, confidence level, and opportunity.

### Output Format

```markdown
# Weak Signal Synthesizer Output

**Generated**: {timestamp}

---

## Results

[Your formatted output here]

---

## Recommendations

[Actionable next steps]

```

### Best Practices

1. **Be Specific**: Focus on concrete, actionable outputs
2. **Use Templates**: Provide copy-paste ready formats
3. **Include Examples**: Show real-world usage
4. **Add Context**: Explain why recommendations matter
5. **Stay Current**: Use latest best practices for meta

### Common Use Cases

**Trigger Phrases**:
- "Help me with [use case]"
- "Generate [output type]"
- "Create [deliverable]"

**Example Request**:
> "[Sample user request here]"

**Response Approach**:
1. Understand user's context and goals
2. Generate comprehensive output
3. Provide actionable recommendations
4. Include examples and templates
5. Suggest next steps

Remember: Focus on delivering value quickly and clearly!

Overview

This skill detects emerging trends by connecting weak signals across unrelated sources to forecast what will gain traction in 3–6 months. It synthesizes signals from niche communities, academic preprints, GitHub activity, patents, funding rounds, and regulatory changes to produce actionable predictions. Outputs include evidence, connection analysis, a clear prediction, confidence score, and suggested opportunities.

How this skill works

The skill continuously ingests and normalizes data from forums, research archives, code repositories, patent indexes, investor disclosures, and policy updates. It applies graph-based analysis to surface triage-worthy patterns: signals that appear in three or more disparate sources but remain absent from mainstream coverage. For each detected trend it presents a compact pack: description, evidence links, why the signals connect, a 3–6 month prediction, confidence level, and recommended actions.

When to use it

  • Early discovery for product ideation or roadmap planning
  • Investor due diligence to spot under-the-radar opportunities
  • Corporate strategy to anticipate regulatory or technology shifts
  • Competitive intelligence to preempt emerging rivals
  • R&D prioritization when scouting nascent research directions

Best practices

  • Define clear scope and signal sources up front to avoid noise
  • Weight signals by source diversity and recency, not raw volume
  • Prefer patterns that appear across at least three unrelated domains
  • Assign and document confidence scores with rationale for transparency
  • Translate predictions into concrete next steps and measurable experiments

Example use cases

  • Spotting an emerging open-source inference optimization library gaining traction in niche ML forums and GitHub stars, then recommending early integration tests
  • Identifying a regulatory draft discussed by specialized legal forums and patent filings that could shift compliance requirements for IoT devices
  • Detecting a new materials technique mentioned in preprints, funded grants, and startup hiring that suggests commercializable supply-chain improvements
  • Flagging a developer tool pattern across small communities and trending repos indicating likely mainstream adoption within a 3–6 month window
  • Prioritizing investment targets by combining early funding rounds, patent filings, and technical discussion intensity

FAQ

How are confidence scores determined?

Confidence is computed from source diversity, signal recency, cross-domain repetition, and corroborating indicators like funding or patents; each factor is weighted and documented.

Can I customize the sources and weighting?

Yes. Configure included communities, repositories, research feeds, and adjust scoring weights to match your industry focus and risk tolerance.

What output format will I receive?

Each trend is delivered as a concise packet: weak signal description, evidence list, connection analysis, 3–6 month prediction, confidence score, and recommended next steps.