# Knowledge Synthesis For Small Result Sets 1 5 Results

> Sub-skill of knowledge-synthesis: For Small Result Sets (1-5 results) (+3).

- Skill: `vamseeachanta/knowledge-synthesis-for-small-result-sets-1-5-results` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/knowledge-synthesis-for-small-result-sets-1-5-results`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/knowledge-synthesis-for-small-result-sets-1-5-results/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/knowledge-synthesis-for-small-result-sets-1-5-results

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# For Small Result Sets (1-5 results) (+3)

## For Small Result Sets (1-5 results)


Present each result with context. No summarization needed — give the user everything:
```
[Direct answer synthesized from results]

[Detail from source 1]
[Detail from source 2]

Sources: [full attribution]
```


## For Medium Result Sets (5-15 results)


Group by theme and summarize each group:
```
[Overall answer]

Theme 1: [summary of related results]
Theme 2: [summary of related results]

Key sources: [top 3-5 most relevant sources]
Full results: [count] items found across [sources]
```


## For Large Result Sets (15+ results)


Provide a high-level synthesis with the option to drill down:
```
[Overall answer based on most relevant results]

Summary:
- [Key finding 1] (supported by N sources)
- [Key finding 2] (supported by N sources)
- [Key finding 3] (supported by N sources)

Top sources:
- [Most authoritative/relevant source]
- [Second most relevant]
- [Third most relevant]

Found [total count] results across [source list].
Want me to dig deeper into any specific aspect?
```


## Summarization Rules


- Lead with the answer, not the search process
- Do not list raw results — synthesize them into narrative
- Group related items from different sources together
- Preserve important nuance and caveats
- Include enough detail that the user can decide whether to dig deeper
- Always offer to provide more detail if the result set was large