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/andreabozzo/dataprof/agents-mdgit clone --depth 1 https://github.com/AndreaBozzo/dataprofWhat 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 | $0.01066 | $0.01066 |
| Opus 5 | $0.00533 | $0.00533 |
| Sonnet 5 | $0.00213 | $0.00213 |
| Haiku 4.5 | $0.00107 | $0.00107 |
Grade A, and why
dataprof 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Guidance for AI coding agents (Claude Code, Codex, Cursor, ...) working in this repository. Humans should start from README.md and docs/CONTRIBUTING.md; this file is the condensed, agent-oriented version and the single source of truth for agent instructions.
What this project is
dataprof is a local, deterministic data profiling and quality assessment
library: a Rust workspace with a PyO3-based Python package on top. The release
surface is Rust library crates plus Python wheels — there is no CLI binary.
It profiles data and reports on it; it does not transform, clean, or move data.
Keep changes within that identity.
Layout
Cargo.toml(root) — workspace: versions, shared dependencies, feature flags.crates/dataprof/src/— public Rust facade (re-exports internal crates).crates/dataprof-*— core, metrics, csv/json/parquet parsers, engines, partial analysis, db connectors, runtime, python bindings.python/— Python package sources and tests.tests/— Rust integration tests for the facade.docs/— guides, release notes, contributing, security policy.examples/— executable docs; CI runs them, keep them working.
Build, test, lint
Prerequisites: Rust 1.96+, Python 3.10+, uv.
1.96 is the MSRV (rust-version in Cargo.toml). CI lints and tests on a
pinned Rust 1.98; see .github/actions/setup-rust. Clippy gates the build
with -D warnings and every Rust release adds lints, so a local toolchain that
differs from 1.98 can pass here and still fail CI, or the reverse. When clippy
disagrees with CI, check rustc --version first and reproduce with
cargo +1.98 clippy.
uv sync # install the Python dev environment
uv run maturin develop # build + install the local Python extension
# Focused tests (prefer these; match the command to your change)
cargo test -p dataprof-core
cargo test -p dataprof-python
uv run pytest python/tests/test_python_api.py -q
# Gates that CI enforces — run before committing
cargo fmt --all
cargo clippy --all --all-targets -- -D warnings
uv run ruff format python/ .github/scripts/ .claude/skills/dataprof/scripts/
uv run ruff check python/ .github/scripts/ .claude/skills/dataprof/scripts/
uv run ty check python/
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 · 90 lines · 1,066 tokens per session scan A bba207124d4f
dataprof AGENTS.md is an instructions file published in the GitHub repository AndreaBozzo/dataprof (20 stars, last pushed 2d ago), licensed MIT. It adds 1,066 tokens to every session, about $0.0053 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
chart-contract AGENTS.md
Instructions for tmusser/chart-contract, a project described as: Claim-first chart auditing for Python: validate the data, claim, visual choice, provenance, and caveats behind analytical charts before sharing.
zenml CLAUDE.md
Claude Code instructions for zenml-io/zenml, covering zenml claude code guidelines, project structure, use zenml docs via mcp, code style & quality standards and commenting policy — explain why, not what.
chdb AGENTS.md
Instructions for chdb-io/chdb, covering agents.md — chdb, 1. fully lazy execution architecture, 2. natural execution triggering (explicit calls prohibited), 3. unified architecture, simplicity first and 4. testing principles.
dingo AGENTS.md
AGENTS.md instructions for MigoXLab/dingo, covering dingo — agent instructions, project overview, tech stack, directory structure and core concepts.
stata-code CLAUDE.md
Instructions for brycewang-stanford/stata-code, covering claude.md — stata-code project notes, communication, release coordination, pypi / testpypi trusted publishing — what to know and recovery: invalid-publisher failure.
streamlit e2e_playwright.instructions.md
Instructions for streamlit/streamlit, covering streamlit e2e tests, test structure, key fixtures and utilities, external test mode and url handling (no localhost hardcoding).