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name: chonkie description: "Use Chonkie for text/document chunking, pipelines, embeddings, CLI/API serving, and storage integrations." disable-model-invocation: true metadata: disco-role: operating license: MIT

Chonkie repo skill

Use this skill when a task involves the chonkie Python package: chunking text/documents, building local processing pipelines, choosing optional embedding/model/provider integrations, operating the CLI/API/cloud wrappers, or exporting chunks to files and vector stores.

Start here

1. Confirm Chonkie is installed and importable:

   import chonkie
   print(chonkie.__version__)
   

2. If the environment or optional extras are unclear, run:

   python scripts/check_chonkie_environment.py --help
   python scripts/check_chonkie_environment.py --json
   

3. Route the task to the closest sub-skill. Prefer deterministic local workflows unless the user explicitly asks for model downloads, provider APIs, cloud calls, or datastore writes.

Sub-skill routes

• sub-skills/chunking-and-types/ — local chunker selection and chunk/type contracts: TokenChunker, SentenceChunker, RecursiveChunker, FastChunker, TableChunker, CodeChunker, optional model-dependent chunkers, tokenizers, recursive rules, and Chunk/markdown/code/table objects.
• sub-skills/pipelines-and-processing/ — Pipeline workflows, CHOMP ordering, file fetching, chefs, local document processing, overlap/embedding refineries, and local JSON/dataset export from pipelines.
• sub-skills/embeddings-and-generative/ — embeddings, provider wrappers, EmbeddingsRefinery, semantic/late/neural/slumber chunking, genies, optional dependency/model-cache/API-key decisions, and deterministic fallbacks.
• sub-skills/interfaces-and-deployment/ — chonkie CLI commands, local FastAPI API schemas/server, Chonkie Cloud wrappers, logging/config, API keys, and deployment/serving guidance.
• sub-skills/integrations-and-storage/ — JSONPorter, DatasetsPorter, vector/datastore handshakes, dependency probes, credential/service gates, and safe no-live-write storage planning.

Shared references

• references/package-overview.md — package surface map, install extras, public entry points, and verified support boundaries.
• references/troubleshooting.md — cross-cutting install/import, optional dependency, model download, credentials, logging, CLI/API, and service safety issues.
• references/repo-provenance.md — source snapshot used to produce this skill; read before deciding whether to refresh.
• references/repo-routing-metadata.json — structured router metadata consumed by repo-skill import tooling.

Safe defaults for future agents

• For local chunking, start with RecursiveChunker(tokenizer="character" or "word", chunk_size=...) before model-dependent chunkers.
• For pipelines, use Pipeline().process_with("text").chunk_with("recursive", ...) or a fetch_from("file", ...) pipeline before adding optional refineries, exporters, or storage.
• For CLI examples, specify --chunker recursive, --chunker token, or --chunker sentence unless semantic/model extras are known to be installed.
• For cloud/provider/vector-store tasks, do not make live network calls or writes until the user has explicitly provided credentials, endpoint/service scope, and permission to use them.
• Treat optional extras (semantic, st, neural, openai, api, handshakes, etc.) as capability gates, not as evidence that the user's current environment is ready.

Import policy for this generated candidate

This production run was requested as not import. The runtime skill tree can be verified, but do not import it into the live managed repo-skill library unless a later user explicitly asks for import/export.