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QinghongLinQinghongLin/data2story-skill/skills/data2story-pro/ideation/SKILL.md

ideation

Skill155 starsChanged 4 months agoMITView on GitHub

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.

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The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 2 are distinctive, and 5 are very specific to this project.

  • 0

    Common

    Most of the 10 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run the brainstorming dialogue by following Skill sparring-partner with the mission in references/sparring_brief.md: drive the reader from …

    line 42
  2. Distinctive:

    Bounded acquisition loop (≤ 2 attempts): If find-data returns BLOCKED / no adequate dataset:

    line 77
  3. Distinctive:

    Never invent a dataset, a source URL, or a license to "succeed."

    line 82
  4. Very specific:

    After 2 failed attempts with no path forward → return IDEATION_ABORTED: no real dataset supports this idea (closest gap: <gate>).

    line 84
  5. Very specific:

    Only AFTER find-data's audit has run (so it never lands inside the data-file glob), write the approved brief to …

    line 93
  6. Very specific:

    Real data only: No synthesis, no simulated rows, no fabricated source URLs or licenses — that would break the whole verifiability premise.

    line 108
  7. Very specific:

    Portability: No hardcoded machine paths — derive everything from $2 and the resolved skill dir.

    line 112

“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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:

  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

  • references/schema.json — the story_brief contract (annotated example).
  • references/sparring_brief.md — the specialized mission handed to sparring-partner, with the feasibility pressure-test and a worked vague-idea → brief example.

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