summarizer

Core Principle

Good summaries preserve meaning while eliminating noise. Bad ones drop critical points or miss the audience.

Summarization = Compression + Preservation + Adaptation

Protocol

Analyze → Select technique → Extract → Compress → Format → Verify

1. Analyze

Before summarizing, determine: - Length of source — tweet vs book chapter - Complexity — technical, narrative, data-heavy - Audience — expert, general, executive, student - Purpose — quick overview, decision support, study aid

2. Select Technique

Match technique to content (see techniques.md):

Content type	Best technique
Simple/short	Zero-shot direct
Technical/complex	Chain-of-thought
Audience-specific	Role-based
Consistent style needed	Few-shot
Strict requirements	Instruction-heavy

3. Extract

Identify what matters: - Core argument or thesis - Key supporting points (3-5 max) - Critical data or evidence - Conclusions and implications

Rule: If you can't identify the core argument, you don't understand it yet.

4. Compress

Apply compression levels: - TLDR — 1 sentence, core message only - Brief — 2-3 sentences, message + key support - Standard — paragraph, covers main points - Extended — multiple paragraphs, preserves nuance

5. Format

Match output to purpose (see formats.md): - Bullet points for scanning - Paragraph for reading - Structured sections for reports - Tweet-length for social

6. Verify

Before delivering, check: - [ ] Core message preserved? - [ ] Key points included? - [ ] Nothing critical dropped? - [ ] Appropriate for audience? - [ ] Right length for purpose?

Output Markers

📝 SUMMARY ([level]: [word count])
[Content]

💡 KEY POINTS
• [Point 1]
• [Point 2]

⚠️ OMITTED (if relevant)
[What was cut and why]

Decline When

Source is ambiguous, contradictory without resolution, or summarizing would lose essential nuance the user needs.

References: techniques.md, formats.md