Motif

An agentic loop that turns a folder of interview transcripts into a research synthesis where every insight carries cited, verified evidence, an honest confidence level, and the counter-evidence against it.

Built for design and research teams who synthesise qualitative interviews and need output they can trust and trace. Motif is the first tool from ETOT. Built as an R&D project; the case study tells the story.

What it does

transcripts/  →  intake  →  synthesis  →  critic  →  revise  →  report.md
                                            ↑            │
                                            └────────────┘  until the critic passes or 3 rounds

• Intake (one call per transcript) maps topics and notable positions with turn references.
• Synthesis produces 8–14 insights. Each has a claim, cited turns with verbatim receipts, sources, confidence, counter-evidence, and a design opportunity.
• Critic checks every insight against the transcripts using rules you can edit — unsupported claims, missing dissent, overconfidence, merged findings, themes present in the corpus but absent from the report. Some rules run in code (citations exist, quotes match, confidence thresholds); the rest are judged by the model.
• Revise fixes what the critic flagged. It may not delete an insight to make an objection go away.
• Report shows every insight with its evidence expanded, and marks any insight the critic still objected to when the loop stopped. Silence is never treated as agreement.

Sample output: docs/exhibits/best-report-v2/output.md.

Install (about 5 minutes)

You need Python 3.10+ and an Anthropic API key (console.anthropic.com).

pip install etot-motif
export ANTHROPIC_API_KEY=your-key-here      # or put it in a .env file in the working directory

Or from a checkout, if you want to edit the critic rules or run the evals:

git clone https://github.com/sleepycobalt/motif.git
cd motif
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
echo "ANTHROPIC_API_KEY=your-key-here" > .env

Run

Put your transcripts in a folder, one speaker turn per paragraph or line, each starting with the speaker's name and a colon. Label the interviewer Researcher, Interviewer, or Moderator so their turns are never cited as evidence.

Interviewer: Can you tell me about the last time you used the app?
Priya: Sure. I opened it on the train and it logged me out again, which...

Then:

motif ./transcripts --out report.md --question "What frustrates users about onboarding?"

Fifteen transcripts of ~45 minutes each take about 20 minutes and cost about $2.50 in API usage. Every prompt, response, and iteration is saved under runs/ so you can see exactly what the critic objected to and how the synthesis changed.

Try the sample corpus first:

motif data/raw/Dataset-2 --out report.md

Use it from Claude Code or Cursor

Motif is also an MCP server: the same engine, callable from any MCP host.

pip install "etot-motif[mcp]"
claude mcp add motif -e ANTHROPIC_API_KEY=your-key-here -- motif-mcp

Or, with uv and no install step, claude mcp add motif -e ANTHROPIC_API_KEY=your-key-here -- uvx --from "etot-motif[mcp]" motif-mcp. From a checkout: pip install -e ".[mcp]" and point the host at $PWD/.venv/bin/motif-mcp.

Five tools: motif_synthesize, motif_critique (check any synthesis, yours or someone else's, against the transcripts), motif_receipts (verbatim turn text for a citation), motif_board (a run laid out for FigJam, executed by the host through Figma's MCP server), motif_runs_get. Install snippets for Claude Code, Cursor, and Claude Desktop, plus a skill that teaches an agent the verify-before-you-quote workflow: surfaces/mcp/README.md.

Use it from Figma

Motif for Figma (FigJam and Figma Design) is in surfaces/figma/: paste your Anthropic key once, drop transcripts, get the synthesis in the plugin and as Markdown. Development build for now; the Community listing and the board writer follow. Build and import steps: surfaces/figma/README.md.

Tune it

Everything a team might want to change lives in config/synth.yaml:

• which model plays which role
• how many revision rounds
• what "high confidence" requires (default: 4+ participants and no counter-evidence)
• the critic's rules, in plain language — add, remove, or reword them

What the evaluation found

Tested on 15 real research interviews (University of Sheffield, CC-BY-NC) against a human-built ground truth of 16 themes and 12 traps, with blind scoring:

The loop makes fewer errors and, since v3, finds more. Its first version found much less (51%) — the critic only checked what was on the page, and the reviser's cheapest fix was deletion. A recall check against the intake topic maps recovered most of that gap; a second check, which asks whether an already-cited turn contains a second finding nobody used, recovered the rest (3 of 3 runs, on the two themes that were missed in every report of the previous eval). Full results: docs/eval1-results.md, docs/eval2-results.md, docs/eval3-results.md.

Known gaps: the loop never reaches critic_pass — 0 of 10 runs in Eval 3, at three rounds and at five — so it always stops on the iteration cap with objections outstanding; a newly added insight arrives without counter-evidence and the counter-evidence check does not revisit it; and the unsupported-evidence figure above is zero in a fixed sample of four insights per report, not zero outright.

Repo layout

core/       reusable: loop controller, run logger, LLM client, config loader
synth/      this tool: engine (the shared service), agents, prompts, corpus loader, report renderer, board layout, CLI
surfaces/   mcp/ — the MCP server (Claude Code, Cursor, any MCP host)
tests/      offline tests with a stubbed model; the MCP server is exercised over stdio
config/     synth.yaml — models, thresholds, critic rules (symlink to synth/synth.yaml, which ships in the package)
scripts/    ingest.py (transcripts → citable text), eval_pack.py (blind scoring packs)
data/       sample corpus (CC-BY-NC, see LICENSE) and its processed form
docs/       R&D brief, working log, ground truth, eval results, exhibits, case-study notes
eval/       blind scoring packs and completed sheets

core/ is written to be reused by other loops; Motif is the first tool built on it.

Data attribution

Sample transcripts: Hanchard, M. and San Roman Pineda, I. (2023). Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts. University of Sheffield. doi:10.15131/shef.data.23567223.v2. CC-BY-NC 4.0. Non-commercial use only.

License

MIT for the code. See LICENSE.