langchain-local-dev-loop
Build a fast, deterministic local test loop for LangChain 1.0 / LangGraph\ \ 1.0\n\u2014 FakeListChatModel fixtures, pytest config, VCR cassettes with key\ \ redaction,\nwarning-filter policy. Use when adding tests to a new chain, fixing\ \ a flaky\ntest, or making integration tests reproducible.\nTrigger with \"langchain\ \ pytest\", \"FakeListChatModel\", \"VCR langchain\",\n\"langchain test fixtures\"\ , \"langchain integration test\".\n"
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill langchain-local-dev-loop --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LangChain Local Dev Loop (Python) ## Overview An engineer writes the most natural assertion possible: ```python def test_summarize(): out = chain.invoke({"text": "..."}) assert out.content == "expected summary" ``` It passes locally against Claude at `temperature=0`. It fails in CI on the third run with a one-token delta in the output. That is P05: Anthropic's `temperature=0` is not greedy — it
What does the langchain-local-dev-loop skill do?
Build a fast, deterministic local test loop for LangChain 1.0 / LangGraph\ \ 1.0\n\u2014 FakeListChatModel fixtures, pytest config, VCR cassettes with key\ \ redaction,\nwarning-filter policy. Use when adding tests to a new chain, fixing\ \ a flaky\ntest, or making integration tests reproducible.\nTrigger with \"langchain\ \ pytest\", \"FakeListChatModel\", \"VCR langchain\",\n\"langchain test fixtures\"\ , \"langchain integration test\".\n"
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill langchain-local-dev-loop --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.