Skill: Market Research Synthesiser
A research synthesis skill for grouping messy inputs into patterns, tensions, implications, and next questions without losing uncertainty.
Use cases
Research & Analysis, Strategy & Planning
Platforms
Claude, GPT, Model-Agnostic
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Workspace-ready resource
Copy the raw material, then check the setup, output contract, and failure modes.
# Market Research Synthesiser Skill Working rules:- Separate evidence from interpretation.- Group findings into patterns, not a source-by-source recap.- Surface contradictions and uncertainty instead of smoothing them away.- End with implications and next questions. Output standard:1. What the research says2. What it likely means3. Tensions or contradictions4. Implications5. What needs validating nextWorkspace translation
Turn this resource into an inspectable run.
Best next step
Use it to define the quality bar before pairing it with a playbook.
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Use the skill as a capability pack for a specific job.
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Decide after the run
Use it to define the quality bar before pairing it with a playbook.
Quality bar
Before using this, check the contract.
What input does this require?
What output should it produce?
Where can it fail?
What should a human review?
Recommended path
When to Use This
Use this when you are combining interviews, market notes, competitive data, or internal research and need signal faster than a manual synthesis pass.
It suits strategy, product marketing, founder research, and consultancy work where the inputs are broad but the output still needs a clean argument.
Why It Works
The skill forces pattern synthesis rather than source summary. That matters because most research work falls apart when the model simply mirrors the input structure.
The contradiction rule is also critical. Good synthesis preserves tension because that is often where the strategic value sits.
How to Customise
Add a domain lens if the work is specific: SaaS, ecommerce, policy, healthcare, or another sector.
If your team relies on confidence ratings or citations, add those directly into the output contract.
Limitations
This skill improves synthesis quality but does not replace source validation or subject-matter expertise.
If the inputs are thin, biased, or contradictory, someone still has to interpret what matters most.
Model Notes
Claude is especially good at preserving nuance across conflicting inputs.
GPT works well if you define a tighter structure. Model-agnostic overall, but source quality still dominates output quality.
Related Resources
Browse SkillsSystem Prompt: Research Analyst
A system prompt for configuring an LLM as a structured research analyst that separates facts from interpretation, scores confidence, and flags gaps clearly.
Pattern / playbook seed
Research & Analysis · Strategy & Planning
Prompt Chain: Research Brief to Point-of-View Memo
A three-step prompt chain for turning raw research into a sharper point-of-view memo without skipping the reasoning in the middle.
Pattern / playbook seed
Research & Analysis · Strategy & Planning
Prompt: Executive Summary from Dense Research
A summarisation prompt for compressing dense research into an executive brief without flattening the key caveats, risks, or implications.
Pattern / playbook seed
Research & Analysis · Strategy & Planning
Related Guides
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