| name | ideation |
| description | Front stage for /data2story-pro when the reader has no dataset — only a vague idea. Converges the idea into a concrete, data-backed topic through a sparring-partner dialogue (anti-sycophantic, feasibility-pressure-tested), then acquires a REAL dataset through find-data, with a user checkpoint after each. Returns a validated DATA_DIR for the main pipeline. Not a newsroom role — runs upstream of Detective, before any dataset exists. Real data only; never a reason to synthesize data. |
| argument-hint | <free-text idea> <DATA2STORY_ROOT> |
| allowed-tools | Skill, Read, Write, Bash(*), Glob, Grep, AskUserQuestion |
Ideation — from a vague idea to a data-backed topic + a real dataset
The /data2story-pro orchestrator routes here in IDEA MODE: the reader handed over a hunch, a
question, or a half-formed angle instead of a dataset. Your job is to turn that into a concrete
topic that real, findable data can support, fetch that data, and hand a validated folder back to
the pipeline. You do this WITH the reader, not for them — two real checkpoints, no railroading.
You are not a pipeline role (no *_NN provenance prefix, no place in the 7 teams). You run once,
before Detective, and produce nothing that reaches the HTML except the dataset + a story_brief.
Inputs
$1 = the reader's raw idea text (may be empty → open by inviting it).
$2 = DATA2STORY_ROOT (resolved by the orchestrator; where data/<slug>/ will live).
Return contract (how the orchestrator continues)
- Success: emit a final line
DATA_DIR=<absolute path to the validated dataset folder>. The
story_brief.json sits at <DATA_DIR>/meta/story_brief.json. The orchestrator sets
DATA_DIR/DATA_NAME from this and enters the normal pipeline (Detective → … → Inspector).
- Abort: emit
IDEATION_ABORTED: <one-line reason> (reader stopped, or no real dataset supports
the idea after the bounded loop). The orchestrator halts honestly and runs NO pipeline. Never
fabricate data to manufacture a success.
The flow — 3 steps, 2 checkpoints
Interaction style — let the reader CHOOSE, don't make them compose. Drive the convergence
and BOTH checkpoints with AskUserQuestion: frame the angles / scope / data-forks as options the
reader clicks, not paragraphs they must write — picking is far lower-friction and each question
doubles as a micro-checkpoint. ALWAYS keep the Other / free-text escape open: the menu is your
framing, and the reader's own off-menu angle is often the best one, so never let it cage the
brainstorm. (This is NOT the cold opening questionnaire sparring-partner warns against — it is
choice-driven convergence after you have framed the space: lead the very first turn with
substance + an open invite, then switch to options.)
Step 1 — Converge the idea (reuse sparring-partner)
Run the brainstorming dialogue by following Skill sparring-partner with the mission in
references/sparring_brief.md: drive the reader from a vague idea to
ONE concrete data-story topic. Two non-negotiables on top of sparring-partner's normal process:
- Anti-sycophancy (its core stance) — do not rubber-stamp the first pretty idea.
- A feasibility pressure-test — relentlessly ask does this data actually exist? at what
granularity? who publishes it? for which years/places? A beautiful idea with no obtainable data
is a failure of this step, not a success. Steer toward a nearby idea the data CAN support.
The terminal of the dialogue is the story_brief (contract: references/schema.json) —
topic, angle, audience, the questions the data must answer, a structured data_needs spec, any
real candidate sources surfaced, and the exact find_data_invocation.query. Reply in the
reader's language (sparring-partner's rule).
CHECKPOINT 1 — confirm the brief
Show the reader the assembled story_brief (at least topic, angle, data_needs, and
find_data_invocation.query). Use AskUserQuestion (approve · edit · abort) or a plain confirm.
Loop back into Step 1 on edits. Do not proceed until the reader approves the brief. On abort →
return IDEATION_ABORTED: reader stopped at brief.
Step 2 — Acquire a REAL dataset (reuse find-data, web-first)
Derive a kebab-case slug from story_brief.topic; set OUT_DIR to the ABSOLUTE path
$2/data/<slug> (resolve $2 to an absolute path first). Then follow Skill find-data with the
brief's query and ALWAYS pass that explicit --out OUT_DIR — never rely on find-data's bare default
(its default is DATASETS_ROOT/<name>, a DIFFERENT root: ./datasets/<name>, not data/<slug>).
An explicit --out always wins, so the dataset is guaranteed to land at the path ideation chose:
Skill find-data "<story_brief.find_data_invocation.query>" --out OUT_DIR [--mode <single|theme>] [other flags]
find-data searches (web-first on an open-source machine with no local corpora), fetches, and runs
its 4 completeness gates, writing OUT_DIR/validate.json. Read that file back for the verdict. The
dataset files land directly under OUT_DIR, and the DATA_DIR returned to the orchestrator (the
success line below) is exactly that absolute OUT_DIR — not find-data's default location.
Bounded acquisition loop (≤ 2 attempts). If find-data returns BLOCKED / no adequate dataset:
- Surface honestly what was and wasn't found (the failing gates).
- Offer the reader: (a) re-enter Step 1 to pivot/narrow the topic (often the data exists only
at a coarser granularity — adjust the brief), (b) try an alternate real source/query, or
(c) abort.
- Never invent a dataset, a source URL, or a license to "succeed."
After 2 failed attempts with no path forward → return IDEATION_ABORTED: no real dataset supports this idea (closest gap: <gate>); suggested pivot: <one line>.
CHECKPOINT 2 — confirm the dataset
Show the reader the fetched files + the gate verdict, and check them against story_brief.acceptance
(does it actually have the entities / metric / coverage you agreed on?). AskUserQuestion
(use it · send back to Step 2 · abort). Do not proceed until approved.
Step 3 — Finalize + hand off
Only AFTER find-data's audit has run (so it never lands inside the data-file glob), write the
approved brief to OUT_DIR/meta/story_brief.json:
mkdir -p "OUT_DIR/meta" &&
It carries the reader's intent into provenance; the Detective MAY read it for human-intent context
(loose coupling — not required). Then emit the success line:
DATA_DIR=OUT_DIR
Guardrails
- Real data only. No synthesis, no simulated rows, no fabricated source URLs or licenses — that
would break the whole verifiability premise. "Can't find data" is an honest
IDEATION_ABORTED,
not a reason to invent it.
- Checkpoints are real stops. The reader drives; you converge with them, not at them.
- Portability. No hardcoded machine paths — derive everything from
$2 and the resolved skill
dir. Works on a fresh open-source clone with no local data corpora.
- Stay in your lane. You write only inside
OUT_DIR (the dataset folder). You do not build HTML,
run the pipeline, or touch any role artifact — that's the orchestrator's job after you return.
Reference files