Read editor.md, editor.json, analyst.json, and designer.json. Resolve chart data from analyst data_tables. Build the final index.html with data-* traceability attributes. Pure implementation — no editorial or visual decisions, no raw data access.
npx skills add https://github.com/QinghongLin/data2story-skill --skill programmer
Your job is faithful implementation. Build exactly what the Editor wrote and the Designer specified. You do not make editorial decisions. You do not make visual decisions. You make them real.
PROJECT_DIR = first argumentPROJECT_DIR/editor.md — verbatim prose for the blogPROJECT_DIR/editor.json — section structure with edt_xx IDsPROJECT_DIR/analyst.json — data findings with ana_xx IDs and data_tablesPROJECT_DIR/designer.json — visual specs with des_xx IDsPROJECT_DIR/index.htmlYou do NOT have access to raw data files. All chart data comes from analyst.json data_tables.
Before writing any code, check the error knowledge base (relative to the skills directory):
../errors/digest.md exists, read it in full. It is a compact, ranked list of the most frequent errors — one line each. Treat every entry as a rule to follow.../errors/base_css.css exists, you will copy its contents verbatim in Step 2 (see below).editor.md verbatim — do not paraphrase, shorten, or rewritedesigner.json — do not substitute or simplifyeditor.mdFor every des_xx chart in designer.json, read content.data_source (an ana_xx ID or array), look up that item's data_table in analyst.json, and convert its columns + rows into Vega-Lite inline values. Stat callouts read content.value directly; interactives with a data_source resolve like charts. The exact conversion snippet and the missing-data_table fallback are in references/data_resolution.json.
Single self-contained HTML file. No build step, no framework. Allowed CDNs: Vega-Embed, Leaflet.js (plus PDF.js / D3.js where a component needs them).
Base CSS injection — if ../errors/base_css.css exists (checked in Step 0), read its contents and paste them verbatim as the very first block inside <style>. Do not modify, reformat, or omit any rule. This is auto-generated defensive CSS from past error patterns.
Page structure:
edt_01, edt_02, ...). For each: prose verbatim → visual (chart / image / video / interactive) directly below.End the page with a compact References section (data source, studies, benchmarks, tools — pulled from detective.json). See references_section in references/component_implementations.json.
Traceability — tag every element with its source IDs. This is critical for the Inspector: data-edt on sections, paragraph-level data-ana/data-det on each <p>, and data-des on every visual (no image or video without it). The full attribute rules and a worked HTML example are in references/traceability.json.
Layout. Max content width 720px centered; charts/teasers may break to full width; serif body, system-ui UI; responsive, no horizontal scroll. All non-text elements must be wrapped and spaced to avoid overlap — the complete container/CSS rules, per-element rules, and the teaser full-bleed exception are in references/layout_rules.json.
Components. Build recipes for every visual mode — Vega-Lite charts (and their pitfalls), interactive elements, instance embeds, maps, timelines, scrollytelling, before/after sliders, card decks, stat callouts, assets, audio/music (Spotify, generated, sonification, ambient — never autoplay sound), and the science-paper components (PDF preview, citation network, review visualizations, paper+review browser, task demo) — are all in references/component_implementations.json.
Before finishing, walk through every section and confirm:
max-width); multi-view charts and timelines fit the column and read on mobiledata-edt; every chart/visual has data-des; every data-driven element has data-anaPROJECT_DIR/index.html
Done when the file opens in a browser, tells the story as the Editor wrote it, looks as the Designer specified, and every element is tagged with data-* attributes for full traceability.
Expert startup business analyst specializing in market sizing, financial modeling, competitive analysis, and strategic planning for early-stage companies. Use PROACTIVELY when the user asks about market opportunity, TAM/SAM/SOM, financial projections, unit economics, competitive landscape, team planning, startup metrics, or business strategy for pre-seed through Series A startups.
This skill should be used when the user asks to "plan team structure", "determine hiring needs", "design org chart", "calculate compensation", "plan equity allocation", or requests organizational design and headcount planning for a startup.
End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.
Generate project status reports from Jira issues and publish to Confluence. When an agent needs to: (1) Create a status report for a project, (2) Summarize project progress or updates, (3) Generate weekly/daily reports from Jira, (4) Publish status summaries to Confluence, or (5) Analyze project blockers and completion. Queries Jira issues, categorizes by status/priority, and creates formatted reports for delivery managers and executives.
Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
Google Workflow: Today's meetings + open tasks as a standup summary.
Read event data from a Google Sheets spreadsheet and create Google Calendar entries for each row.
Create professional, dark-themed SVG diagrams of any type — architecture diagrams, flowcharts, sequence diagrams, structural diagrams, mind maps, timelines, illustrative/conceptual diagrams, and more. Use this skill whenever the user asks for any kind of technical or conceptual diagram, visualization of a system, process flow, data flow, component relationship, network topology, decision tree, org chart, state machine, or any visual representation of structure/logic/process. Also trigger when the user says "画个图" "画一个架构图" "diagram" "flowchart" "sequence diagram" "draw me a ..." or uploads content and asks to visualize it. Output is always a standalone .svg file.
Take qinghonglin/data2story-programmer 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.