apache/airflow
GitHubApache Airflow - A platform to programmatically author, schedule, and monitor workflows
- airflow
- apache
- apache-airflow
- automation
- dag
- data-engineering
- data-integration
- data-orchestrator
- data-pipelines
- data-science
- elt
- etl
- machine-learning
- mlops
- orchestration
- python
- scheduler
- workflow
- workflow-engine
- workflow-orchestration
- Stars:
- 47.4k
- Language:
- Python
- Default branch:
- main
- Last scored:
At a glance
- Rank
- #18 of 401
- Top 4% · #7 of 56 Python repos
- Checks passed
- 11 of 17
- Full list below
- Best set up for
- Kimi Code CLI
- Score 92.1
- Needs most work for
- Aider
- Biggest fix: .aider.conf.yml (+14.1 pts)
What it has
What it's missing
What to fix first
Showing fixes for Claude Code
- 1Manageable sizeIf possible, split into smaller modules or carve out a focused entry path. Document where to start in AGENTS.md.+5.3 pts
- 2Test suiteAdd a tests/ (or test/, __tests__/, spec/) directory with runnable tests. Document how to run them in AGENTS.md.+3.9 pts
Score for each AI agent
Each agent weighs the checks by its own docs. Under each score are its top three. How each agent is scored →
Does apache/airflow have agent instructions?
An AGENTS.md (or CLAUDE.md) at the root tells AI coding agents how to build, test and work in the repo. A few agents also read a file of their own.
- AGENTS.md / CLAUDE.md
AGENTS.md(new tab) · 23,068 characters- Agent-specific files
.agents/skills(new tab) (8 skills)No GEMINI.md, .cursor/rules, .openhands/setup.sh or .aider.conf.yml- Repos that have one
- 29 of 56 Python repos · 221 of 401 repos on the leaderboard · See examples →
All 17 checks · 11 passed
How apache/airflow compares
- vs leaderboard average
- 19.5 pts above
- Average 66.8
- vs Python average
- 15.3 pts above
- Average 71.0
- Stars among Python repos
- #29 of 56
Missing here, and how many repos have it
- Cursor rules (.cursor/rules) · passed by 4% of leaderboard repos, 7% of Python repos
Passes checks most repos miss
- Skills (.agents/skills) · only 17% of repos pass it
- Reproducible dev env · only 22% of repos pass it
- Pre-commit / git hooks · only 29% of repos pass it
One fix for the most agents: Manageable size would raise the score for all 9 agents, by up to 5.7 points.
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Same topics, any language
Repos tagged with the same GitHub topics, most shared first.
- apache/seatunnel64.4Java · apache, data integration, elt
- apache/superset88.7Python · apache, data engineering, data science
- statelyai/xstate67.2TypeScript · orchestration, workflow
- n8n-io/n8n83.6TypeScript · automation, workflow
- qdrant/qdrant82.7Rust · machine learning, mlops
- Python · data science, machine learning
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Paste this at the top of the README. It shows the score, and links back to this page.
[](https://agentfriendlycode.com/repo/github/apache/airflow)Check the score on every pull request
A GitHub Action posts one comment per pull request with how the score changed and why.