imagineer
Fan out MANY candidate interactive concepts from the data + narrative — the ideation pool the Editor curates a hero + supporting set from. Deliberate over-generation: one concept per finding worth making hands-on, each declaring its archetype, purpose, what the reader produces, and an honest feasibility (node-checked against the Analyst's client_model). Builds NOTHING on-page — img_xx concepts are internal and never reach HTML. Outputs imagineer.json after the Analyst, before the Editor.
npx skills add QinghongLin/data2story-skill --skill imagineer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Imagineer Your job is **ideation, not construction**. You read the findings and the narrative and you fan out a wide pool of candidate *interactive concepts* — ways a reader could **produce** a finding (run the model, guess-then-reveal, enter their own value, play the odds) instead of just reading it. You deliberately **over-generate**: propose one concept for every finding worth making hands-on, even the marginal ones. The Editor curates this pool down to a hero + a ranked supporting set; the Interaction Engineer builds only what the Editor keeps. You build **nothing on the page**. Your `img_xx` ids are **internal** — a planning vocabulary the Editor reads. They never reach the HTML, are tagged on no element, and are added to no provenance tuple. Your one job is to make the candidate pool rich, honest about feasibility, and bound to real findings. ## Setup - `PROJECT_DIR` = first argument. - `SKILL_DIR` = the directory containing this `SKILL.md` (`.../skills/data2story-pro/imagineer`). - Read `PROJECT_DIR/analyst.json` — its `items` give you the findings (`ana_xx`: `label`, `content`, `data_table`, and any **`client_model`**). The `client_model`s are what make `explorable_recomp
- Setup
- When to run (and when to stay light)
- Step 1 — Walk the findings, propose a concept per hands-on opportunity
- Step 2 — Bind each concept to a real finding + an honest feasibility
- Step 3 — node-check feasibility (honest, not hopeful)
- Output — imagineer.json
- References
What does the imagineer skill do?
Fan out MANY candidate interactive concepts from the data + narrative — the ideation pool the Editor curates a hero + supporting set from. Deliberate over-generation: one concept per finding worth making hands-on, each declaring its archetype, purpose, what the reader produces, and an honest feasibility (node-checked against the Analyst's client_model). Builds NOTHING on-page — img_xx concepts are internal and never reach HTML. Outputs imagineer.json after the Analyst, before the Editor.
How do I install it?
Run `npx skills add QinghongLin/data2story-skill --skill imagineer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From QinghongLin/data2story-skill, a repository with 149 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.