ds-ai-coding-skills
AGENTS.md86 starsChanged 49 days agoMITView on GitHub
This file is the agent router for GitHub Copilot and Codex. It provides high-level rules and directs agents to the appropriate skill files for detailed instructions. CLAUDE.md carries the same content for Claude Code. Everything from ## Hard Rules onward must match between the two files; scripts/sync_agent_docs.py --check enforces this. This is a Python 3.11 data science / analysis project. Detailed task-specific procedures are in .claude/skills/*/SKILL.md. Project-specific context is in docs/agent/*.
Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 31,700 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository24 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- analysis-reportingSkill
- causal-inference-diagnosticsSkill
- dataframe-polarsSkill
- notebook-workflowSkill
- path-and-ioSkill
- plan-analysisSkill
- predictive-modeling-diagnosticsSkill
- prepare-prSkill
- python-project-opsSkill
- python-styleSkill
- review-sqlSkill
- run-edaSkill
- run-modelingSkill
- safe-data-handlingSkill
- simulation-optimization-diagnosticsSkill
- sql-analysisSkill
- statistical-inference-diagnosticsSkill
- statistical-ml-reviewSkill
- summarize-analysisSkill
- sync-agent-docsSkill
- unsupervised-eda-diagnosticsSkill
- update-agent-docsSkill
- visualizationSkill
The file
Show its rules and text
We split this AGENTS.md into the rules it gives an agent
23 rules. About 2 are common, 10 are distinctive, and 11 are very specific to this project.
≈2
Common · estimate
Many files like this have a rule of this kind.
≈10
Distinctive · estimate
This author's own choices. Worth a look.
11
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- could be automatic: a setting could enforce it
- Common: line 16
Never commit raw data, credentials, API keys, tokens, or customer-level records.
- Distinctive: line 17
Never modify, overwrite, delete, or regenerate raw data directly.
- Distinctive: line 18
Prefer small, reviewable changes.
- Distinctive: line 20
Ask for clarification when data semantics are unclear.
- Very specific: line 21
Use uv exclusively for Python dependency management.
- Very specific: line 22
When applying a statistical, ML, causal, simulation, or optimization method, always apply the matching *-diagnostics skill (図と値をセットで出す).
- Distinctive: line 26
Use uv exclusively for all dependency management.
- Very specific: line 27
Never use pip, pip3, python -m pip, poetry, conda, pipenv, or easy_install.
- Distinctive: line 32
Never modify or delete raw data directly.
- Very specific: line 33
Treat data/raw and data/external as immutable.
- Very specific: line 61
Follow python-style skill: type hints, Google-style docstrings, Japanese comments.
- Very specific: line 62
Follow dataframe-polars skill: prefer polars over pandas.
- Very specific: line 63
Follow path-and-io skill: use pathlib.Path, no absolute paths.
- Very specific: line 67
Do not create, modify, or commit real data files under data/raw/ or data/external/ — these are immutable.
- Very specific: line 69
Only .gitkeep files and documentation (README, markdown) are acceptable to commit.
- Very specific: line 74
Use explicit column names.
- Distinctive: line 75
Use CTEs for readability.
- Distinctive: line 76
Add date filters for large tables.
- Very specific: line 78
Never run DROP, TRUNCATE, DELETE, or UPDATE unless explicitly requested.
- Common: line 83
Keep restartable from a clean kernel.
- Distinctive: line 88
Write documentation in clear Japanese.
- Distinctive: line 89
Keep code snippets, commands, file paths, and config values in English as-is.
- Distinctive: line 90
Do not translate technical terms unnecessarily.
≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AGENTS.md
This file is the agent router for GitHub Copilot and Codex. It provides high-level rules and directs agents to the appropriate skill files for detailed instructions.
CLAUDE.md carries the same content for Claude Code. Everything from ## Hard Rules onward
must match between the two files; scripts/sync_agent_docs.py --check enforces this.
This is a Python 3.11 data science / analysis project.
Detailed task-specific procedures are in .claude/skills/*/SKILL.md.
Project-specific context is in docs/agent/*.
Hard Rules (Always Apply)
- Never commit raw data, credentials, API keys, tokens, or customer-level records.
- Never modify, overwrite, delete, or regenerate raw data directly.
- Prefer small, reviewable changes.
- Explain assumptions before non-trivial analytical decisions.
- Ask for clarification when data semantics are unclear.
- Use
uvexclusively for Python dependency management. Never use pip, conda, poetry, or pipenv. - When applying a statistical, ML, causal, simulation, or optimization method, always apply the matching
*-diagnosticsskill (図と値をセットで出す).
Package Management
- Use uv exclusively for all dependency management.
- Never use pip, pip3,
python -m pip, poetry, conda, pipenv, or easy_install.
Data Safety
- Never commit raw data, credentials, API keys, tokens, or customer-level records.
- Never modify or delete raw data directly.
- Treat
data/rawanddata/externalas immutable.
Common Commands
uv sync
uv run pytest
uv run ruff check .
uv run ruff format . # ローカル整形
uv run ruff format --check . # CI用(差分チェックのみ)
uv run mypy src
uv run python scripts/check_no_raw_data_commit.py
uv run python scripts/check_no_sensitive_patterns.pyKey Conventions
- DataFrame operations: prefer polars over pandas.
- Visualization: use
fig, ax = plt.subplots(...), notplt.figure(...). - File paths: use
pathlib.Path, no absolute local paths. - Docstrings: Google-style.
- Inline comments: Japanese.
- Reports and documentation: Japanese.
File-Specific Guidelines
Python files (**/*.py)
- Follow
python-styleskill: type hints, Google-style docstrings, Japanese comments. - Follow
dataframe-polarsskill: prefer polars over pandas. - Follow
path-and-ioskill: use pathlib.Path, no absolute paths.
Data files (data/**)
- Do not create, modify, or commit real data files under
data/raw/ordata/external/— these are immutable. - Writing derived data to
data/interim/ordata/processed/is allowed in code, but do not commit large data files. - Only
.gitkeepfiles and documentation (README, markdown) are acceptable to commit. - See
safe-data-handlingskill.
SQL files (**/*.sql)
- Use explicit column names; avoid
SELECT *. - Use CTEs for readability.
- Add date filters for large tables.
- Validate join cardinality and row counts.
- Never run
DROP,TRUNCATE,DELETE, orUPDATEunless explicitly requested. - See
sql-analysisskill.
Notebooks (**/*.ipynb, notebooks/**/*.md)
- Keep restartable from a clean kernel; no secrets in outputs.
- See
notebook-workflowandsafe-data-handlingskills.
Documentation (README.md, docs/**/*.md)
- Write documentation in clear Japanese.
- Keep code snippets, commands, file paths, and config values in English as-is.
- Do not translate technical terms unnecessarily.
Skill Routing
| Task | Skill |
| Dependencies, tests, lint, type check, notebook execution | python-project-ops |
| Reading / writing / moving data files | safe-data-handling + path-and-io |
| Writing or reviewing SQL | sql-analysis |
| Writing or reviewing Python code | python-style |
| DataFrame operations | dataframe-polars |
| Charts and visualization | visualization |
| Notebook creation and editing | notebook-workflow |
| Running any statistical / ML / causal / simulation / optimization method | the matching *-diagnostics router (5 families, each routing to references/<method>.md): statistical-inference, predictive-modeling, causal-inference, unsupervised-eda, simulation-optimization |
| Reviewing someone else's statistics or ML work | statistical-ml-review |
| Analysis summaries and reports | analysis-reporting |
| File paths and I/O | path-and-io |
Project Context (docs/agent)
このリポジトリでしか通用しない知識だけを置く。作業手順・規約は skill 側が正本であり、
ここには重複させない(持ち運ぶ単位は .claude/skills/<name>/ ディレクトリ)。
ディレクトリ構成は README.md を参照する。
| Document | Purpose |
| project-overview.md | プロジェクトの目的とスコープ |
| data-catalog.md | データセット一覧と定義 |
| metrics-and-definitions.md | 指標定義 |
| diagnostics-reference-template.md | 診断 skill(*-diagnostics)への手法追加手順と雛形 |
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