Summarizer

Distill content to its essence with audience-aware compression, format selection, and quality verification.

Core Principle

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

Summarization = Compression + Preservation + Adaptation

Protocol

text

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

text

📝 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.

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References: techniques.md, formats.md