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.
npx agentmods add instructions/chekos/datawrapper/agents-mdgit clone --depth 1 https://github.com/chekos/DatawrapperWhat 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 | $0.00544 | $0.00544 |
| Opus 5 | $0.00272 | $0.00272 |
| Sonnet 5 | $0.00109 | $0.00109 |
| Haiku 4.5 | $0.00054 | $0.00054 |
Grade A, and why
Datawrapper AGENTS.md 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 2d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent guide for Datawrapper
This repository is friendly to AI-assisted edits, but agents should stay inside the same guardrails as human contributors.
Start here
- Read
README.md,CONTRIBUTING.md, and this file before changing code. - Work from the latest
mainbranch unless the maintainer tells you otherwise. - Keep pull requests focused. Do not mix dependency upgrades, broad refactors, generated artifacts, and product changes in one PR.
- Prefer the object-oriented chart API (
BarChart,LineChart,get_chart, etc.) for new work. Treat lower-levelDatawrappermethods as legacy compatibility paths unless a task explicitly targets them.
Canonical local commands
Install the locked dependencies:
uv sync --frozen --all-extras
Run the same deterministic checks expected before a PR:
uv run ruff check ./datawrapper ./tests
uv run ruff format --check ./datawrapper ./tests
uv run mypy ./datawrapper --ignore-missing-imports
uv run pytest
uv build --sdist --wheel
Before committing, run the hook suite against all files:
uv run pre-commit run --all-files
Test strategy
- Put fast, isolated validation in
tests/unit/. - Put mocked multi-component behavior in
tests/integration/ortests/functional/. - Mark tests that require the real Datawrapper API with
@pytest.mark.apiand aDATAWRAPPER_ACCESS_TOKENskip guard. The default local and CI test runs must pass without credentials. - Prefer
responses,pytest-mock, orunittest.mockover live HTTP calls for regression tests. - When fixing a bug, add a regression test that would fail before the fix.
Change boundaries
- Do not commit
.venv/,.ruff_cache/,.mypy_cache/,htmlcov/,coverage.xml,dist/, or other generated outputs. - Do not edit
uv.lockor dependency constraints unless the task is explicitly about dependency maintenance. - Do not add secrets, API tokens, recordings of API responses containing private data, or maintainer-specific local configuration.
- Do not publish releases, merge pull requests, or change repository settings.
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.
- 2d ago First seen · 54 lines · 544 tokens per session scan A 7adf9713e7eb
Datawrapper AGENTS.md is an instructions file published in the GitHub repository chekos/Datawrapper (95 stars, last pushed 15d ago), licensed MIT. It adds 544 tokens to every session, about $0.0027 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.
Other instructions, from other repositories
marimo AGENTS.md
Instructions for marimo-team/marimo, covering marimo development guidelines, your primary responsibility is to the project and its users, quick setup, development commands and python.
marimo copilot-instructions.md
Instructions for marimo-team/marimo: For example, if there is a frontend change to make a border thicker, how does this look in run mode, app-view and edit-view? For a backend change, has the user tested different scenarios?
vizro CLAUDE.md
Claude Code instructions for mckinsey/vizro, covering vizro development guide for ai agents, development setup (across all packages), only dependency: hatch, working directory and common hatch commands across all packages.
vizro copilot-instructions.md
Copilot instructions for mckinsey/vizro, covering github copilot instructions for vizro, pull requests from bots and automated tooling and everything else.
cnsplots AGENTS.md
Instructions for faridrashidi/cnsplots, covering agents.md, working branch rules, implementation rules, validation rules and commit rules.
openstreetmap-statistics AGENTS.md
Instructions for piebro/openstreetmap-statistics, covering creating new statistics, available datasets, dataset structure, changeset data and base columns.