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name: core-primitives description: "Work on langchain-core primitives: runnables, messages/content blocks, prompts, tools, documents, embeddings, vector stores, language model interfaces, callbacks, output parsers, serialization, and API/deprecation utilities." disable-model-invocation: true metadata: disco-role: operating license: MIT

Core Primitives

Use this sub-skill when a task touches stable langchain-core abstractions that other LangChain packages build on: LCEL Runnable composition, message and content-block conversion, prompt templates, tool schemas, documents, embeddings, vector stores, chat/LLM interfaces, callbacks/tracing hooks, output parsers, serialization, or deprecation/API utilities.

Route app-level agent construction, middleware, model/tool orchestration, and create_agent behavior to ../agents-and-middleware/SKILL.md when that sibling exists. Route legacy langchain-classic chains, memory, agents, callbacks, and retrievers to ../classic-chains/SKILL.md when that sibling exists.

Start Here

1. Identify the primitive family involved and read references/api-surfaces.md for the public classes, helpers, and compatibility expectations.
2. Follow the matching workflow in references/workflows.md before editing, including package-local commands and skip conditions.
3. For failures involving provider content conversion, runnable mode mismatches, Pydantic schemas, tool docstrings, optional imports, deprecation warnings, or serialization, use references/troubleshooting.md.
4. Run scripts/core_import_smoke.py as a fast, non-mutating import/API smoke check when a Python environment with langchain-core is available.

Scope

• Prefer public imports from langchain_core.* package surfaces; avoid depending on private helpers unless the task is explicitly internal maintenance.
• Preserve stable public signatures. For new public parameters, prefer keyword-only defaults and add tests that fail when the compatibility promise is broken.
• Keep core primitives provider-agnostic. Provider payload details belong in integrations; core should define normalized interfaces and safe translation hooks.
• Use Pydantic v2-native models and helpers while respecting existing v1 compatibility paths where the code already supports them.
• Treat network calls, provider credentials, and heavy integration execution as out of scope for this sub-skill; use fakes, unit tests, and import/schema checks instead.

Local References

• references/api-surfaces.md — distilled map of core public APIs, expected extension points, and evidence-backed validation targets.
• references/workflows.md — concrete edit/test workflows for runnables, messages, tools, prompts, vector stores, model interfaces, parsers, callbacks, serialization, and deprecations.
• references/troubleshooting.md — failure-mode guide for the common core primitive regressions this sub-skill owns.
• scripts/core_import_smoke.py — safe import and minimal behavior smoke check for installed or checkout-backed langchain-core.

Validation Signals

Good changes usually include targeted unit tests around the affected primitive, pass the relevant package-local uv run --group test pytest ... command when uv is available, and keep import smoke output at OK core_import_smoke. If uv or dependencies are unavailable, record the skip clearly and still run static checks such as Python syntax compilation for bundled scripts.