explosion/spaCy
GitHub💫 Industrial-strength Natural Language Processing (NLP) in Python
- ai
- artificial-intelligence
- cython
- data-science
- deep-learning
- entity-linking
- machine-learning
- named-entity-recognition
- natural-language-processing
- neural-network
- neural-networks
- nlp
- nlp-library
- python
- spacy
- text-classification
- tokenization
- Stars:
- 34k
- Language:
- Python
- Default branch:
- master
- Last scored:
At a glance
- Rank
- #222 of 401
- #42 of 56 Python repos
- Checks passed
- 7 of 17
- Full list below
- Best set up for
- Devin
- Score 70.4
- Needs most work for
- Cursor
- Biggest fix: Cursor rules (.cursor/rules) (+11.5 pts)
What it has
What it's missing
What to fix first
Showing fixes for Claude Code
- 1AGENTS.md / CLAUDE.mdAdd an AGENTS.md covering project goals, layout, setup commands, and conventions. Aim for 800+ chars of real guidance (not boilerplate).+13.2 pts
- 2Type configurationAdd a type config (tsconfig.json for JS/TS, mypy.ini or pyrightconfig.json for Python). Rust/Go/JVM/Scala/Swift/C#/OCaml/Haskell/Zig are typed by default.+7.9 pts
- 3Test 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 explosion/spaCy 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
- None at the root (no AGENTS.md, CLAUDE.md, AGENT.md, .claude/CLAUDE.md or .claude/AGENTS.md)
- Agent-specific files
- None: no GEMINI.md, .cursor/rules, .openhands/setup.sh, .aider.conf.yml or .agents/skills
- Repos that have one
- 29 of 56 Python repos · 221 of 401 repos on the leaderboard · See examples →
Get a starter AGENTS.md
From the files the checks found. Fill in the commands, then commit it at the root.
# AGENTS.md
Guidance for AI coding agents working in explosion/spaCy. 💫 Industrial-strength Natural Language Processing (NLP) in Python. Written mainly in Python.
## Where things are
- Dependencies are declared in `pyproject.toml`.
- Test files are spread through the tree, e.g. `spacy/tests/doc/test_add_entities.py`.
- Lint and format rules are configured in `setup.cfg`.
- CI is defined in `.github/workflows`; read it for the exact install, lint and test steps.
- Commit hooks are set up in `.pre-commit-config.yaml`; don't skip them.
- Common tasks are targets in `Makefile`.
- Follow the conventions in `CONTRIBUTING.md`.
## Commands
<!-- Fill in the exact commands CI runs. -->
- Install: TODO
- Test: TODO
- Lint: TODO
All 17 checks · 7 passed
How explosion/spaCy compares
- vs leaderboard average
- 0.6 pts below
- Average 66.8
- vs Python average
- 4.8 pts below
- Average 71.0
- Stars among Python repos
- #40 of 56
Missing here, and how many repos have it
- Type configuration · passed by 68% of leaderboard repos, 57% of Python repos
- Skills (.agents/skills) · passed by 17% of leaderboard repos, 20% of Python repos
- Cursor rules (.cursor/rules) · passed by 4% of leaderboard repos, 7% of Python repos
Passes checks most repos miss
- Pre-commit / git hooks · only 29% of repos pass it
One fix for the most agents: AGENTS.md / CLAUDE.md would raise the score for all 9 agents, by up to 13.3 points.
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Same topics, any language
Repos tagged with the same GitHub topics, most shared first.
- Python · deep learning, machine learning, natural language processing, nlp
- Python · deep learning, machine learning, natural language processing, nlp
- Python · named entity recognition, nlp, spacy
- C++ · deep learning, machine learning, neural networks
- pytorch/pytorch84.6Python · deep learning, machine learning, neural network
- Python · data science, deep learning, machine learning
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[](https://agentfriendlycode.com/repo/github/explosion/spaCy)Check the score on every pull request
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