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User Research Studiov1.0.0

Turn supplied interviews and feedback into an evidence-linked research room. Compare participant segments, preserve contradictory observations, prioritize authored actions and export a brief, backlog and follow-up interview guide.

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Made with this SkillA traceable research brief and action backlog

Fictional research · actual exports · unique participants and counter-evidence

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What you get

A traceable research brief and action backlog

What you can make

  • Connect themes to supplied interview and feedback evidence.
  • Compare participant segments while keeping contradictory observations visible.
  • Export an authored action backlog, research brief and follow-up interview guide.
Explore delivered files7 groups
  • index.html
  • plan.json + original notes.json
  • analysis.json
  • findings.md
  • actions.csv
  • interview-guide.md
  • delivery-manifest.json

Good fit

Product teams synthesizing reviewed qualitative research into next actions.

ResearcherDesignerFounder

Know the limits

Works from supplied records; repeated notes from one person are not extra participants.

About this skill

Capabilities: source-linked themes, separate reported and observed records, participant-segment views, contradictory evidence, severity/effort judgments, action owners and acceptance criteria. Repeated notes from one person count as one participant.

Deliverables: offline index.html, editable plan.json, original notes.json, analysis.json, findings.md, actions.csv, interview-guide.md and a SHA-256 delivery manifest. The example contains seven fictional participants, fourteen observations and four themes.

Read the full workflow

First run: Python 3.10+ standard library only. Run scripts/build-research.py with --input examples/study.json --notes examples/notes.json --output ./research-room. Keep original evidence bytes and update notesHash when supplying new notes.

Validation: actual generation, unique-participant calculations, source-hash and reference rejection, segment filtering, contrary evidence, keyboard views, inert text and preserved existing deliveries. Agent interpretation and source context still require human review.

Limits: the supplied-study priority score is not statistical confidence, market prevalence or measured product uplift. Themes and judgments are authored by your agent; the helper does not automatically classify transcripts or publish private evidence.

Version 1.0.0: original Skillrove tools and examples under MIT; research methodology adapted from an Apache-2.0 source with its notice and pinned attribution included. $4.99 one-time; active Premium memberships cover this package.

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User research

FROM PACKAGE TO FIRST RESULT

Your first evidence-linked research room.

Build the fictional Northstar onboarding study, then replace its anonymized observations with authorized research. The actual exported example preserves four themes and two contrary references.

View example result
US$4.99 · MIT licensePython 3.10+ · offlineNo API key or paid provider
  1. 01

    Choose your agent

    These steps use a project folder. Open the project in your agent and run terminal commands from its root.

    Codex directory guide

    Package folder .agents/skills/user-research-studio

  2. 02

    Add the package

    Download and unzip the package into the folder above. Keep all its files together, with SKILL.md directly inside that folder.

    Downloads require a verified account and purchase access. Choose a new folder if this Skill is already installed.

  3. 03

    Run the included sample

    Manual sample commands and technical details

    Read SKILL.md, review the generator and check its runtime requirements before running:

    cd .agents/skills/user-research-studio
python3 scripts/build-research.py --input examples/study.json --notes examples/notes.json --output ./research-room
    Your first result

    Switch Findings, Evidence and Action plan with mouse or arrow keys. Filter by participant segment, inspect supporting and contrary notes, and reconcile 3/7 supporting participants against five notes in the first theme. The action backlog always covers the complete study. Edit the plan and regenerate into a fresh folder.

    Python 3.10+ standard library, offline. File hashes and observation references are checked; interpretation, sampling and quote context require review. Percentages describe the supplied study. No transcript service, external publication or research participant is contacted.

    If research-room already exists, change the output to ./research-room-2. Existing files are never overwritten.

    Prefer to ask your agent?

    Use User Research Studio at .agents/skills/user-research-studio. Read SKILL.md and review scripts/build-research.py. Run examples/study.json into a new output folder and show me all generated files. Keep fictional sample content clearly labeled.

Ready to use your own facts?

Copy examples/study.json to a new input file, replace all fictional facts, and generate into a new folder. Review the content before using or publishing it; the generator does not independently verify claims.

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Before you choose this Skill

How do I get User Research Studio?

The listed individual package price is USD 4.99. Premium downloads require a confirmed purchase or active membership. Your agent subscription, model access and external provider credits are separate.

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Which agents can use this skill?

This release lists Codex, Claude Code, Cursor as intended installation hosts. Check the package’s dependencies and permissions before running it. These labels do not certify that every workflow has been tested in every agent.

Codex setupClaude Code setupCursor setup
What files can this workflow generate?

The documented 1.0.0 workflow lists: index.html; plan.json + original notes.json; analysis.json; findings.md; actions.csv; interview-guide.md; delivery-manifest.json. Some files or exports are optional; follow the package’s setup and inspect the delivered result.

Review all deliverablesInspect the working example
What should I check before using the result?

Python 3.10+ standard library, offline. File hashes and observation references are checked; interpretation, sampling and quote context require review. Percentages describe the supplied study. No transcript service, external publication or research participant is contacted.

Read the installation guideReview third-party notices

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