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Research Synthesis

Community
assimovt
research-synthesis

Synthesize user research into actionable insights using atomic research methods. Use when asked to synthesize research, organize user feedback, find patterns in interviews, summarize customer discovery, or turn raw notes into insights and recommendations.

Overview

Publisherassimovt
Repositoryproductskills
Skill nameresearch-synthesis
Stars
71
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by assimovt on GitHub. Read the source before you install it.

Installation

Install the Research Synthesis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/assimovt/productskills/tree/main/skills/research-synthesis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/assimovt/productskills.git /tmp/productskills
mkdir -p .claude/skills
cp -r /tmp/productskills/skills/research-synthesis .claude/skills/research-synthesis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Synthesis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Research Synthesis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Research Synthesis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Turn raw research into atomic insights that drive decisions. Good synthesis surfaces patterns, bad synthesis creates narrative fiction. The goal is structured evidence, not a compelling story that cherry-picks quotes.

Atomic Research Method

Break research into four levels, bottom-up:

1. Nuggets (Raw Evidence)

Individual observations from a single source. Each nugget is:

  • One observation per nugget (not a paragraph)
  • Tagged with source (participant ID, date, method)
  • Direct quotes preferred over your interpretation

Example: "[P3, Jan 12] 'I spend 30 minutes after every customer call just trying to remember what they said.'"

2. Patterns (Recurring Themes)

Group nuggets that point to the same phenomenon. A pattern requires evidence from 3+ independent sources.

Example: "5 of 7 PMs report spending 20-45 minutes on post-call documentation. All describe it as tedious and low-value."

3. Insights (Implications)

What the pattern means for the product. An insight connects a pattern to a product opportunity or risk.

Example: "Post-call documentation is a high-frequency pain point (daily for active PMs) with no satisfying solution. Current workarounds (voice memos, bullet lists) lose context and emotional nuance."

4. Recommendations (Actions)

Specific product actions justified by insights. Each recommendation traces back through the chain: recommendation ← insight ← pattern ← nuggets.

Evidence Strength

Rate every pattern and insight:

StrengthCriteria
Strong5+ sources, consistent behavior observed, corroborated by data
Moderate3-4 sources, mostly consistent, some data support
Emerging2 sources, needs more evidence before acting
WeakSingle source or contradictory evidence

NEVER make product recommendations based on Weak or Emerging evidence. Flag them for further research.

Synthesis Process

  1. Extract all nuggets from raw notes — one per line, tagged with source
  2. Affinity map: group nuggets by theme (not by interview)
  3. Name each group as a pattern with a count ("5/7 participants...")
  4. Derive insights from strong and moderate patterns only
  5. Write recommendations that trace back to specific insights
  6. Flag contradictions explicitly — don't smooth them over

Guidelines

  • CRITICAL: NEVER synthesize from fewer than 3 data points. Two people saying the same thing is a coincidence, not a pattern.
  • ALWAYS cite specific evidence. "Users struggle with X" is worthless. "5 of 7 participants spent 20+ minutes on X, with P2 calling it 'the worst part of my week'" is evidence.
  • ALWAYS weight actions over opinions. What people DO matters more than what they SAY.
  • NEVER cherry-pick quotes to support a narrative. Include contradictory evidence.
  • ALWAYS flag your confidence level. Distinguish "we know" from "we think."
  • NEVER present synthesis without the evidence chain. Anyone reading should be able to trace a recommendation back to raw nuggets.

Built on the Atomic Research method (Daniel Pidcock) and Continuous Discovery Habits (Teresa Torres). Skills from productskills.

Frequently asked questions

What does the Research Synthesis AI skill do?

Synthesize user research into actionable insights using atomic research methods. Use when asked to synthesize research, organize user feedback, find patterns in interviews, summarize customer discovery, or turn raw notes into insights and recommendations.

Why use Research Synthesis on TypingMind?

Because you install it once and use it with any model. Research Synthesis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Research Synthesis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/assimovt/productskills/tree/main/skills/research-synthesis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Research Synthesis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Research Synthesis?

As many as you like. As long as a model supports skills, you can use Research Synthesis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Research Synthesis AI skill free?

Yes. It is published on GitHub by assimovt under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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