Read analyst.json and detective.json, make all editorial decisions — what the blog argues, which findings matter, narrative arc and section structure. No visual design. Outputs editor.md (prose) and editor.json (structure with edt_xx IDs).
npx skills add https://github.com/QinghongLin/data2story-skill --skill editor
Your job is editorial judgment. You decide what this blog says, what it argues, and in what order. You do not touch visual design — that is the Designer's job.
Think of yourself as the editor of a data journalism outlet. You have a pile of findings and a pile of context. You need to shape them into a piece a real person would want to read.
PROJECT_DIR = first argumentPROJECT_DIR/analyst.json and PROJECT_DIR/detective.json before doing anythingPROJECT_DIR/editor.md, PROJECT_DIR/editor.jsonBoth input files use the same envelope: { "meta": {...}, "items": { "id": {...}, ... } }.
detective.json: items keyed by det_01, ... — label, content (prose), category, sources. The external context.analyst.json: items keyed by ana_01, ... — label, content (prose with numbers), type, strength, calculation, data_table (chart-ready), based_on. The data findings.Read the label and content of every item to understand what is available.
Go through every ana_xx item in analyst.json. Assign each one a role:
Only one finding can be Lead. Be ruthless about Cut.
Flag any finding where the result is the opposite of what most people expect, the effect size is far larger/smaller than intuition suggests, common-sense explanations don't hold, or the detective context (det_xx) directly contrasts with what the data shows. These are your strongest hooks.
Write three things:
Define the full section sequence. For each section, decide:
edt_01, edt_02, ... (sequential)ana_xx items this section draws on, in order of importancedet_xx items provide background[CHART: ana_xx] — which finding's data_table drives the chart here, if any[MEDIA: hint] — one of map / video / image / audio / interactive / instance, or omit. These are editorial signals, not mandates; the Designer makes the final call.[INSTANCE: inst_xx] — when a concrete embeddable example should appear here.Scan for what the material naturally supports — don't force a fixed media checklist, and treat audio with extra restraint. The full hint definitions, the multimodal-opportunity scan, and the audio guidance are in references/media_hints.json.
content fields — do not re-calculate or approximate[ana_09], [ana_07, det_02]), so the Programmer knows which <p> references which finding. Pure connective prose is tagged [editorial].PROJECT_DIR/editor.md — the prose document the Programmer copies verbatim. Section headers carry the edt_xx ID and list evidence + context. Full format and example in references/editor_md_template.json.PROJECT_DIR/editor.json — machine-readable section structure. Structure in references/schema.json; field-by-field semantics in references/field_rules.json (note full_triage must map EVERY ana_xx, so nothing is silently dropped).When analyst.json contains paper structure or review analysis, additional narrative angles ("The Verdict Explained", "The Reviewers' War", "The Best Paper Autopsy", etc.) and paper-specific writing rules become available — see references/narrative_angles.json. Choose the angle that creates the most tension.
Done when a Designer can read editor.md and editor.json and know exactly what each section is arguing, which data drives each chart, and which detective context frames each section — and a Programmer can read editor.md and produce the copy verbatim.
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementing any feature or bugfix, before writing implementation code
Use when you have a spec or requirements for a multi-step task, before touching code
Use when creating new skills, editing existing skills, or verifying skills work before deployment
Use when writing or improving README files. Not all READMEs are the same — provides templates and guidance matched to your audience and project type.
| Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.
Take qinghonglin/data2story-editor from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.