Data Journalist Agent (Data2Story)

SkillDatabases & data

Data Journalist Agent (Data2Story) — orchestrator: turn a dataset into a blog. Runs detective → analyst → editor → designer → programmer → auditor → inspector in sequence. Creates a versioned project folder for each run.

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 Data Journalist Agent (Data2Story) skill

What this skill tells your AI

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

Turn $ARGUMENTS into a blog. Orchestrates the roles below in sequence.

Setup

Resolve paths before doing anything:

  • Never hard-code machine-local paths and never ask the user to export path variables.
  • Resolve SKILL_DIR = the directory containing this SKILL.md (.../skills/data2story)
  • Resolve ARCHIVE_DIR = the ancestor directory that contains skills/ (two levels up from SKILL_DIR, i.e. SKILL_DIR/../..)
  • Resolve DATA2STORY_ROOT = parent of ARCHIVE_DIR
  • Commands below use symbolic placeholders such as ARCHIVE_DIR; replace them with resolved, quoted paths before running Bash.
  • DATA_NAME = the dataset folder name (e.g. pick_a_card)
  • DATA_DIR = if $ARGUMENTS is an existing path, use that path; otherwise use DATA2STORY_ROOT/data/{DATA_NAME}
  • TIMESTAMP = current time formatted as MMDD_HHMM (e.g. 0401_1618): date +%m%d_%H%M (run in bash)
  • PROJECT_DIR = DATA2STORY_ROOT/project/{DATA_NAME}/blog_{MODEL}_{TIMESTAMP}
  • Create PROJECT_DIR/, PROJECT_DIR/assets/, PROJECT_DIR/code/

Archival

Immediately after creating PROJECT_DIR, snapshot the current skills:

mkdir -p PROJECT_DIR/archival
cp -r ARCHIVE_DIR/skills PROJECT_DIR/archival/skills

This preserves the exact skill versions used for this run.

Tools available

All media tools route through OpenRouter. Set OPENROUTER_API_KEY before any generation call.

Media generation is the Designer's job, so the media tools (text2image, text2video, image2video, text2music, embeddings) live under SKILL_DIR/designer/scripts/openrouter-*/. The full list — default models and exact python3 ... invocations — is in designer/references/tools.json; full per-tool docs are each tool's own SKILL.md under SKILL_DIR/designer/scripts/openrouter-*/.

Pipeline Overview

The pipeline is a single linear sequence that produces a traceable HTML blog from raw data:

DATA → Detective → Analyst → Editor → Designer → Programmer → Auditor → Inspector → final index.html + viewer.html

Run each stage in order. Each stage reads the previous artifact(s) before starting. Do not proceed to the next stage until the current artifact is complete.

Stage 1 — Detective

Input: DATA_DIR Output: PROJECT_DIR/detective.json What: Researches external context — background knowledge, domain history, related findings, why this data matters. Each finding gets a det_xx ID.

Stage 2 — Analyst

Input: DATA_DIR, PROJECT_DIR/detective.json Output: PROJECT_DIR/code/*.py, PROJECT_DIR/analyst.json What: Exhaustive quantitative analysis of the data, informed by detective's context. All code saved to code/ as runnable scripts. Each finding gets an ana_xx ID with calculation (file + lines + output) and data_table (chart-ready data).

Stage 3 — Editor

Input: PROJECT_DIR/detective.json, PROJECT_DIR/analyst.json Output: PROJECT_DIR/editor.md, PROJECT_DIR/editor.json What: Editorial decisions — which findings matter, what the narrative arc is, what the blog argues. Each section gets an edt_xx ID with explicit references to ana_xx findings and det_xx context. No visual design.

Stage 4 — Designer

Input: PROJECT_DIR/editor.md, PROJECT_DIR/editor.json, PROJECT_DIR/analyst.json Output: PROJECT_DIR/designer.json, PROJECT_DIR/assets/* What: Data-driven creative visual decisions — how to present each point using charts, images, video, audio, maps, interactives, stat callouts, instances, or text-only treatment when appropriate. The media mix should emerge from the dataset's properties, not from a fixed checklist. The page should be multimedia-rich by default: borrow the visual language from the shared frontend-design skill and use all five channels (chart, image, video, audio, interactive/map) unless a channel's documented fallback would be fabricated or purely decorative. Each visual gets a des_xx ID with data_source pointing to ana_xx data_tables when data-driven. Generates selected assets. No HTML.

Stage 5 — Programmer

Input: PROJECT_DIR/editor.md, PROJECT_DIR/editor.json, PROJECT_DIR/analyst.json, PROJECT_DIR/designer.json Output: PROJECT_DIR/index.html What: Implements the final blog in HTML. Applies the theme/accent recorded in designer.json page_rhythm and borrows component + token recipes from the frontend-design skill. Resolves chart data from analyst.json data_tables (NO raw data access). Tags every element with data-edt, data-ana, data-det, data-des attributes for traceability.

Stage 6 — Auditor

Input: PROJECT_DIR/index.html Output: PROJECT_DIR/index.html (modified), PROJECT_DIR/auditor.json What: Detects and fixes layout issues (overlap, spacing, alignment) without changing content or design intent. Runs automatically after Programmer to ensure visual elements are properly wrapped and spaced.

Call: Skill auditor PROJECT_DIR

Stage 7 — Inspector

Input: PROJECT_DIR/index.html, all JSON files Output: PROJECT_DIR/inspector.json, PROJECT_DIR/viewer.html What: Runs sentence-level traceability verification and generates an interactive viewer. Two steps:

python3 SKILL_DIR/inspector/scripts/verify.py PROJECT_DIR --log-errors
python3 SKILL_DIR/inspector/scripts/generate_viewer.py PROJECT_DIR

Step 1 produces inspector.json (sentence→evidence mapping). Step 2 produces viewer.html (self-contained, works on file:// — no server needed). See inspector/SKILL.md for details.

Traceability: ID flow through the pipeline

det_01 ──┐
det_02 ──┤
         ├──▶ ana_01 (based_on: [det_02]) ──┐
         │    ana_02 (based_on: [])          ├──▶ edt_01 (findings: [ana_01, ana_02], context: [det_01]) ──▶ des_01 (section: edt_01, data_source: ana_01)
         │    ana_03 (based_on: [det_01])    │    edt_02 (findings: [ana_03], context: [det_02])         ──▶ des_02 (section: edt_02, data_source: ana_03)
         └────────────────────────────────────┘

Every value in the final HTML can be traced: HTML data-des="des_01"designer.json des_01.data_source="ana_01"analyst.json ana_01.calculation.code → verifiable.

Handoff rules

  • Each artifact must be complete before the next stage starts.
  • If an artifact is missing required sections, fix it before proceeding.
  • Media-richness gate (after Designer, before Programmer): designer.json should exercise all five channels (chart, image, video, audio, interactive_or_map). For any channel marked used:false, confirm its documented fallback was genuinely tried and a data-grounded reason recorded in meta.media_decisions. If a channel was skipped for convenience rather than because the data can't support it, send it back to the Designer before the Programmer runs.
  • All generated assets go into PROJECT_DIR/assets/ only.
  • Final deliverables: PROJECT_DIR/index.html, PROJECT_DIR/detective.json, PROJECT_DIR/analyst.json, PROJECT_DIR/code/*.py, PROJECT_DIR/editor.md, PROJECT_DIR/editor.json, PROJECT_DIR/designer.json, PROJECT_DIR/inspector.json, PROJECT_DIR/viewer.html.

Signals

GitHub stars
155
Forks
22
Last commit
Jul 2026
Advanced
Catalog kind
skill
Gateway key
data2story
Source
github.com/qinghonglin/data2story-skill