data2story-pro
Use to turn a dataset into a verifiable multimedia blog (a data story / data-driven article / interactive dashboard from a dataset). Orchestrator for the Data Journalist Agent (Data2Story): a 7-team newsroom (14 agents) running detective → scout → analyst → imagineer → editor → copywriter → designer → interaction → hero → cinematographer → programmer → auditor → critic → inspector in sequence. Trigger when the user hands over a dataset (CSV/JSON/folder/path) and wants a published story, blog post, or interactive report built from it. Creates a versioned project folder per run.
npx skills add QinghongLin/data2story-skill --skill data2story-pro --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.
# Data Journalist Agent (Data2Story) Turn **$ARGUMENTS** into a blog. Orchestrates the roles below in sequence. ## Orchestration rituals Five process rules earned from real runs. Each is one rule + why it exists; follow them across every run regardless of topic. - **(D1) Candidate-image review loop.** For any hero/cover image of a named person or specific real object, generate **N candidates**, then run a VLM (or human) review and **select** one — never ship the first render. *Why:* text2image of named people intermittently refuses outright and quality varies shot-to-shot, so a single render is a coin-flip; a small candidate pool plus a review step is the only reliable way to land a usable, correctly-identified image. - **(D2) Audit-before-finalize.** Run the read-only multi-agent audit (provenance / repro / assets / IP lenses) to **find** every defect FIRST, collect them, and only THEN make one editing pass to **fix** them. *Why:* interleaving find-and-fix makes agents edit the same file against a moving target and re-introduce each other's defects; separating the finding phase from the fixing phase prevents that churn. - **(D3) Single owner of one big file.** Parallel agents may
- Orchestration rituals
- The 7 teams
- Setup
- Stage 0 — Input dispatch (data path · URL · idea)
- IDEA MODE
- Run profile — Fast vs Premium (select ONCE, then commit)
- Archival
- Tools available
- Pipeline Overview
- Stage 1 — Detective
- Stage 1.5 — Scout [premium]
- Stage 2 — Analyst
- Stage 2.5 — Imagineer [premium]
- Stage 3 — Editor
mkdir -p PROJECT_DIR/archival cp -r ARCHIVE_DIR/skills PROJECT_DIR/archival/skills python3 SKILL_DIR/inspector/scripts/verify.py PROJECT_DIR python3 SKILL_DIR/inspector/scripts/generate_viewer.py PROJECT_DIR
What does the data2story-pro skill do?
Use to turn a dataset into a verifiable multimedia blog (a data story / data-driven article / interactive dashboard from a dataset). Orchestrator for the Data Journalist Agent (Data2Story): a 7-team newsroom (14 agents) running detective → scout → analyst → imagineer → editor → copywriter → designer → interaction → hero → cinematographer → programmer → auditor → critic → inspector in sequence. Trigger when the user hands over a dataset (CSV/JSON/folder/path) and wants a published story, blog post, or interactive report built from it. Creates a versioned project folder per run.
How do I install it?
Run `npx skills add QinghongLin/data2story-skill --skill data2story-pro --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.