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production-python-ai

Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested code.

skillmds/skillmd

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SKILL.md（原文）

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Production Python & AI Engineering Standards

Code quality baseline

Type hints on every function signature. Pydantic v2 models at all I/O boundaries (API requests/responses, LLM outputs, file parsing results).
Code must pass ruff and mypy. No print() — use the logging module with structured context.
Config via pydantic-settings and environment variables. Never hardcode API keys, model names, or URLs. Magic numbers (chunk sizes, top_k, thresholds) live in a config object, not inline.

FastAPI

async def for I/O-bound routes; never call blocking/sync I/O inside them.
Shared clients (DB, HTTP, LLM SDKs) created once at startup and injected via Depends — never instantiated per request.
response_model on every route. Correct status codes. Routes stay thin: validation in, service call, response out — business logic lives in service functions.
Raise HTTPException with clear detail; map internal exceptions to safe client messages (never leak stack traces or keys).

LLM calls (any provider)

Every call gets: explicit timeout, retry with exponential backoff on transient errors, and a max-retry cap.
NEVER trust raw model output. Parse into a Pydantic schema; on validation failure, retry with the error fed back or fall through to an explicit failure path — never .get() blindly on un-validated JSON.
Pin model versions in config. Prompts are versioned constants/templates in the repo, not inline f-strings scattered through code.
Log per call: model, latency, input/output tokens, and a request/trace ID. Propagate the trace ID through every pipeline stage.

LangChain / LangGraph / RAG

Prefer explicit LangGraph state (TypedDict/Pydantic) over implicit chains; every node validates what it reads from state.
Handle tool errors inside the graph — a failed tool returns a structured error message to the model, it does not crash the run.
RAG: chunking params, embedding model, and top_k come from config; embedding model version is pinned (changing it invalidates the index — say so in code comments).
Use asyncio.gather for independent parallel LLM/tool calls; cap concurrency with a semaphore.

Errors & logging

Catch specific exceptions only — no bare except. Either handle meaningfully or let it propagate; never swallow silently.
Fail loud and early in pipelines: validate inputs at the start, not three stages deep.
Logs are structured (key=value or JSON): event, trace_id, duration_ms, and outcome.

Testing

pytest. Unit tests NEVER hit live LLM APIs — mock the client and test the parsing/validation/retry logic hard, including malformed model output.
Bug fix = first a failing test that reproduces it, then the fix.
For prompts: keep golden input/output examples and assert the parser handles them.

Before declaring done

Run the code or tests — never claim it works without verification.
Check: secrets out of code, types clean, errors handled, LLM outputs validated, logs in place.s

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academic-pptx

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Use this skill whenever the user wants to create or improve a presentation for an academic context — conference papers, seminar talks, thesis defenses, grant briefings, lab meetings, invited lectures, or any presentation where the audience will evaluate reasoning and evidence. Triggers include: 'conference talk', 'seminar slides', 'thesis defense', 'research presentation', 'academic deck', 'academic presentation'. Also triggers when the user asks to 'make slides' in combination with academic content (e.g., 'make slides for my paper on X', 'create a presentation for my dissertation defense', 'build a deck for my grant proposal'). This skill governs CONTENT and STRUCTURE decisions. For the technical work of creating or editing the .pptx file itself, also read the pptx SKILL.md.

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