Ideation — from a vague idea to a data-backed topic + a real dataset

SkillDatabases & data

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Ideation — from a vague idea to a data-backed topic + a real dataset skill

What this skill tells your AI

The instructions your AI receives, as published by qinghonglin/data2story-skill in skills/data2story-pro/ideation/SKILL.md and read by ahel’s review.

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:

  1. Surface honestly what was and wasn't found (the failing gates).
  2. 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.
  3. 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" && # write story_brief.json there (valid JSON matching references/schema.json)

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

Signals

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Jul 2026
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skill
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ideation-qinghonglin
Source
github.com/qinghonglin/data2story-skill