SKILL FILE

Research Synthesis Engine with AI

Combine insights from multiple research sources — interviews, surveys, support tickets — into a unified synthesis with patterns and recommendations.

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What this skill file teaches Claude

Drop one markdown file into your repo. Claude Code learns how to run this entire workflow.

1

Multi-source ingestion

Combine interview snapshots, survey responses, support tickets, analytics, and qualitative notes in one synthesis.

2

Pattern detection

Surface themes that recur across sources — weighted by frequency and source quality.

3

Contradictions flagged

Where sources disagree, the synthesis calls it out instead of averaging it away.

4

Actionable recommendations

Each pattern translates into a "what to do about it" with rough effort/impact framing.

5

Source-linked evidence

Every claim in the synthesis is traceable back to the underlying research artefact.

What you can build with this

After a research sprint

Just finished 8 interviews + a survey + a competitive scan? Synthesise the lot into one strategic doc.

Quarterly research reviews

Look across the quarter's research and find the emerging patterns nobody had bandwidth to notice in the moment.

Build a business case

When you need evidence from multiple channels to justify an investment, synthesis is the wrapper.

Get the full skill file

Everything above is 80% of the skill file. Download the complete version with full implementation details, agent prompts, and ready-to-run scripts.

Common questions

At least 3 distinct sources — and ideally from different methods (qualitative + quantitative). One interview alone is a snapshot, not a synthesis.
Yes, and that's often the most useful output. Contradictions get flagged explicitly so you can investigate why — instead of being smoothed away.
It can summarise quant alongside qual, but isn't a stats tool. For numeric significance testing, use a stats package; for narrative synthesis, this skill is faster.
The synthesis will flag low-confidence patterns and tell you where the evidence is thin. Honest about gaps rather than confident about everything.
Repositories store research. Synthesis Engine PROCESSES research. You can use it on top of Dovetail exports — or directly on raw notes if you don't use a repository.

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