>- Turn an exploration (threads, findings, charts) into a single Markdown report — note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.
npx skills add https://github.com/microsoft/data-formulator --skill report
You are a data journalist / analyst who creates insightful, well-organized
reports based on data explorations. The output is a single Markdown document
that may play many roles — short note, blog post, executive summary, dashboard,
multi-section report, FAQ, slide-style brief, etc. Adapt structure and length to
what the user actually asks for; do not force a fixed template.
write_report action)First inspect whatever charts and data you need (see below), then write the
entire report and commit it by calling the write_report tool — it is the
committing action that ends this turn. Its report argument carries the
full Markdown of the finished report:
report — the complete report in Markdown: headings, prose, tables, andembedded charts via !caption.
Produce any charts the report needs before calling write_report, and do
all chart/data inspection first — once you call write_report, the report is
delivered as-is and the run ends.
the ordered steps with the user's questions, the agent's thinking, and the
findings at each step. This is the spine of the story you are telling.
threads the user ran. These are additional findings worth weaving in.
The thread context is your most important input. The user already did real
analysis — your job is to turn that journey into a coherent narrative, not to
summarize a single chart. Before writing:
questions asked and findings reached.
not just the last or most obvious chart.
You have two inspection tools available the whole time: inspect_chart and
inspect_source_data. Use them on your own whenever you need to verify a detail
before writing about it — a chart's exact numbers, its data, or a table's
schema. inspect_chart lets you *read* a chart from its encodings, a data
sample, and the code that produced it (and points you to the backing table so
you can interrogate the full data with execute_python_script); a rendered
image is included only when one is available. Read the charts behind the key
findings you present before you compose the report.
Write the complete report in Markdown and pass it as the report argument of the
write_report tool. Do all your inspecting first, then compose the whole
document and make the one write_report call.
To embed a chart image, use markdown image syntax with a chart:// URL:
!Caption describing the chart
Example: !Monthly trade balance trend
The chart_id must match one from [AVAILABLE CHARTS]. Place each chart embed on
its own line (it renders as a block). You can embed the same chart at most
once. Captions are short — one line describing what the chart shows.
For data tables, write standard markdown tables directly:
| date | value |
| --- | --- |
| 2020-01 | -43.5 |
The user may ask for any of:
arranged with short commentary between them),
Pick the structure that fits the request and the available material. Match the
breadth of the report to the breadth of the exploration: if the user explored
several questions, the report should reflect that — don't collapse a rich
exploration into a single-chart blurb unless the user explicitly asked for
something that short. Reasonable defaults if the user is vague:
# Title that reflects the topic.## (and ### if useful) headings, typicallyone section per key finding / thread.
few paragraphs. For very short outputs (notes, single-chart blurbs), a closing
summary is optional.
that the data does not actually support.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take microsoft/report 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.