What makes Interview Synthesis notable?

982 downloads. Trust evidence available

How should Interview Synthesis be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Interview Synthesis

Use this skill when a UX researcher or product team wants to synthesize qualitative data — interviews, usability tests, focus groups, or survey open-ends — i... Skill: Interview Synthesis Owner: archlab-space Summary: Use this skill when a UX researcher or product team wants to synthesize qualitative data — interviews, usability tests, focus groups, or survey open-ends — i... Tags: latest:0.2.2 Version history: v0.2.2 | 2026-05-28T09:52:02.765Z | user Rewrote frontmatter description to concise 200–500 character format for improved agent-trigger clarity. v0.2.0 | 2026-05-17T1

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

982

Updated

Oct 11, 2026

Version

0.2.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 982 downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 982 downloadsadoption · observed Oct 11, 2026
Latest release: 0.2.2release · observed May 28, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 0.2.2release
Rewrote frontmatter description to concise 200–500 character format for improved agent-trigger clarity.
Observed May 28, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s176qz6rwtpzj9gk93r7b3jm6984ty2d:interview-synthesis

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-archlab-space-interview-synthesis/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

35,276 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: interview-synthesis
description: >
Use this skill when a UX researcher or product team wants to synthesize
qualitative data — interviews, usability tests, focus groups, or survey
open-ends — into structured themes, insights, and prioritized recommendations.
Produces a stakeholder-ready synthesis report.
---

# Interview Synthesis

You are a UX research analyst. Your job is to transform raw qualitative research data—transcripts, session notes, open-ended responses—into a structured synthesis report with named themes, supporting evidence, and actionable recommendations.

**Tone:** Analytical, precise, and stakeholder-ready. Use plain language. Distinguish observations (what happened) from insights (what it means) from recommendations (what to do).

## Flow

Follow these phases in order. Ask one question at a time and wait for the user's response before continuing.

---

## Phase 1: Setup & Routing

### Step 1: Understand the Research

Open with:

> "I'll help you synthesize your research data into clear themes and actionable insights. What type of research are you synthesizing?"

Offer these options: **User Interviews / Usability Test / Focus Group / Survey Open-Ends / Other**

If Other or ambiguous, ask a follow-up question to clarify the method before continuing. Never silently default to User Interviews.

### Step 2: Gather Context

Ask the following, one at a time:

1. What was the research question or goal? (e.g., "Why do users abandon the checkout flow?")
2. How many participants or responses are included?
3. What product, service, or domain does this research cover?

### Step 3: Collect the Data

Ask the user to paste the transcripts, session notes, or open-ended responses.

If the data is long (more than roughly 1,000 words per participant), offer to go participant by participant or session by session.

If the data contains participant names or personally identifying information (PII), say:

> "I see this data includes participant identifiers. I'll refer to participants as P1, P2, etc. in the output to protect their privacy."

### Step 4: Confirm Analysis Plan

Based on the research method, select the analysis blocks from the routing table below. Before starting Phase 2, present the plan to the user:

> "Since this is a [method], I'll work through these [N] analysis steps: [block list]. Ready to start?"

Wait for confirmation before continuing.

**Routing Table:**

| Method | Analysis Blocks (in order) |
| --- | --- |
| User Interviews | Participant Overview · Observation Extraction · Theme Clustering · Quote Selection · Insight Synthesis · Recommendations |
| Usability Test | Task Inventory · Friction Point Extraction · Severity Rating · Behavioral Patterns · Design Recommendations |
| Focus Group | Group Dynamic Notes · Individual vs. Consensus Views · Theme Clustering · Divergence Mapping · Strategic Implications |
| Survey Open-Ends | Sentiment Bucketing · Category Mapping · Frequency Count · Outlier Identification · K

README.md

# interview-synthesis

Synthesizes qualitative user research data—interviews, usability tests, focus groups, or survey open-ends—into a structured report with named themes, supporting quotes, insights, and prioritized recommendations.

## When to Use

- You have raw interview transcripts or session notes and need to find patterns
- You need a stakeholder-ready synthesis report quickly
- You want to ensure no strong observation is missed in the analysis

## What It Does

1. Routes analysis by research method (user interviews, usability tests, focus groups, surveys)
2. Extracts atomic observations from the raw data
3. Clusters observations into named themes with prevalence counts
4. Selects the best supporting verbatim quote for each theme
5. Writes insights (what it means) and prioritized recommendations (what to do)
6. Produces a stakeholder-ready synthesis report

## Input

- Raw transcripts, session notes, or open-ended survey responses (pasted as text)
- Research question or goal
- Number of participants
- Research method type

## Output

A structured Markdown report with named themes, prevalence counts, verbatim quotes, insights, and recommendations rated by priority (🔴 Critical / 🟡 Important / 🟢 Consider), plus a top-insights summary and suggested next steps.

## Privacy

All participant names and identifiers are anonymized to P1, P2, etc. in the output. Sensitive data is never reproduced verbatim in the final report.

## Compatibility

| Platform | Status |
| --- | --- |
| Claude Code | ✅ |
| Openclaw | ✅ |
| Codex | ✅ |

## Feedback & Contributions

Found a gap or have a suggestion? [Open an issue or PR](https://github.com/archlab-space/Open-Skill-Hub/issues) — improvements are welcome.

_meta.json

{
  "ownerId": "kn798vfcxrgjdt230v34k8eqf584vpwv",
  "slug": "interview-synthesis",
  "version": "0.2.2",
  "publishedAt": 1779961922765
}

CHANGELOG.md

# Changelog

## [0.2.2] - 2026-05-28
Rewrote frontmatter description to concise 200–500 character format for improved agent-trigger clarity.

## [0.2.1] - 2026-05-21

### Added
- Feedback prompt in README.md and conditional feedback section in SKILL.md

## [0.2.0] - 2026-05-17
Plugin version bump to accommodate user-research-synthesis skill addition. No changes to this skill.

## [0.1.0] - 2026-05-16
Initial release. Qualitative research synthesis skill covering user interviews, usability tests, focus groups, and survey open-ends, with method-based routing, theme clustering, verbatim quote selection, and prioritized recommendations.

skill-card.md

## Description:

Use this skill when a UX researcher or product team wants to synthesize qualitative data, including interviews, usability tests, focus groups, or survey open-ends, into structured themes, insights, prioritized recommendations, and a stakeholder-ready report.

This skill is ready for commercial/non-commercial use.

## Publisher:

[archlab-space](https://clawhub.ai/user/archlab-space)

### License/Terms of Use:

MIT-0

## Use Case:

UX researchers, product teams, and research operations staff use this skill to turn raw qualitative research data into themes, supporting evidence, insights, and prioritized product recommendations.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Research inputs may include participant identifiers or sensitive personal disclosures.

Mitigation: Review transcripts before use, remove unnecessary sensitive details where possible, and rely on the skill's participant anonymization and synthesis-focused reporting.

## Reference(s):


## Skill Output:

**Output Type(s):** [Text, Markdown, Guidance]

**Output Format:** [Structured Markdown report]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Uses participant codes, selective verbatim quotes, prevalence counts, prioritized recommendations, top insights, and suggested next steps.]

## Skill Version(s):

0.2.2 (source: changelog, released 2026-05-28)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

Github ReposUpdated 3d agoRank 70

AionUi

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

MCPOPENCLAW

Github ReposUpdated 6mo agoRank 70

activepieces

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

OPENCLAW

Github ReposUpdated 6mo agoRank 70

cherry-studio

AI productivity studio with smart chat, autonomous agents, and 300+ assistants.

MCPOPENCLAW

Github ReposUpdated 7mo agoRank 70

CopilotKit

The Frontend for Agents & Generative UI. React + Angular