A multi-step workflow that turns a dataset into a blog, with separate roles for investigating, analysing, writing, designing, coding and checking the result. It saves each run in its own versioned project folder.

Use it to turn structured data into a written and designed data story. It helps create the supporting files, charts, code and review records for each project.

It removes the need to coordinate these stages manually and keeps the files from different runs organised. The checks help catch problems before the blog is finished.

data2story — Skill for Claude Code by QinghongLin

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add QinghongLin/data2story-skill --skill data2story

Clone the repo

git clone --depth 1 https://github.com/QinghongLin/data2story-skill

Made for: Claude Code.

Or install data2story, the plugin that ships this one along with the rest of its 2 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/data2story/github.svg)](https://agentmods.dev/skills/qinghonglin/data2story-skill/data2story)

Your own site

<a href="https://agentmods.dev/skills/qinghonglin/data2story-skill/data2story"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/data2story/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/qinghonglin/data2story-skill/data2story"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/data2story.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,886 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium Excessive Agency · line 16
Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00050	$0.01886
Opus 5	$0.00025	$0.00943
Sonnet 5	$0.00010	$0.00377
Haiku 4.5	$0.00005	$0.00189

Measured 10d ago against content hash 0ebc8c0c6e99, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

data2story scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

The scan reads SKILL.md. This mod also ships 12 executable files (designer/scripts/openrouter-embeddings/scripts/embed.py, designer/scripts/openrouter-image2video/scripts/generate_video_from_image.py, designer/scripts/openrouter-text2image/scripts/generate_image.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/data2story/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Journalist Agent (Data2Story)

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

Read the full file on GitHub · 116 lines

Files

What ships with it

59 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

10d ago First seen · 116 lines · 50 tokens per session scan A 0ebc8c0c6e99

Subscribe to this mod's changes

data2story is a skill published in the GitHub repository QinghongLin/data2story-skill (155 stars, last pushed 2mo ago), licensed MIT. It adds 50 tokens to every session and 1,886 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Analytics and metrics Writing and docs agent-skills data-journalists data-science

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