All Skills

把一件用户自己答不出来的事，变成一份交给别人填的问卷（Markdown 文档），可异步发出也可开会一起填。只访谈"发送"本身——发给谁、要拿回什么——问题则对准收件人知道而用户不知道的那段缺口。

从一份或多份现有配置抽取可审阅的 agent-config.yaml，并跨 Windows、Linux、macOS 与 WSL 声明式同步 Codex ~/.codex/config.toml 和 Claude Code ~/.claude/settings.json。用于导入、去重、解决冲突、剔除配置项、初始化、检测、校验、渲染、预览差异、应用、检查状态或诊断；不用于凭据、登录状态、项目级配置、~/.claude.json、skills、agents、hooks 或 MCP 配置。

对一个软件项目或源码仓库进行全面调研，输出一份结构化的中文调研报告，让对项目不熟悉的人能够同时理解"它做什么"和"它怎么实现"。当用户说"调研这个项目 / 分析这个代码库 / 给我一份项目分析报告 / 我要全面理解这个项目 / 帮我看看这个仓库 / onboarding 一下 / 这个项目的整体架构是什么 / 给我梳理下这个源码"等等，或者把一个不熟悉的代码目录交给你并希望你给出全局认识时，必须使用本 skill。即使用户没有显式说"调研报告"，只要意图是"帮我搞懂一个完整的代码库 / 项目"，就应当触发。但本 skill 仅针对"全局理解 / 架构调研 / onboarding"场景：若用户只是要做 code review、定位某个 bug、评审某处具体实现、做性能或安全诊断，不要触发本 skill。

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Use for writing, reviewing, debugging, testing, or diagnosing Python Bayesian models built directly with PyMC, including Model, coords/dims, Data, random variables, potentials, posterior sampling, prior/posterior predictive checks, and InferenceData output. Trigger on model geometry, shape errors, divergences, sampler choice, mutable prediction data, and probabilistic validation. Do not use for Bambi formula models, NumPyro/JAX programs, ArviZ-only analysis of existing draws, deterministic optimization, or general statistics without PyMC code.

Use for writing, reviewing, debugging, or testing Python terminal presentation built with Rich, including Console streams, Text and markup safety, tables, trees, panels, progress, status, Live displays, render protocols, and deterministic output capture. Do not use for CLI argument parsing, structured logging design, browser UI, or machine-readable protocol output that must remain plain.

Use for writing, reviewing, debugging, testing, or optimizing Python CVXPY optimization models. Trigger on Variable, Parameter, Expression, Constraint, Objective, Problem, DCP, DPP, DGP, DQCP, solver selection/status, dual values, mixed-integer, cone, or repeated parametric solves. Do not use for scipy.optimize-only, PyMC inference, symbolic algebra without optimization, or hand-written solver implementations.

Use for writing, reviewing, debugging, or testing Python code that inspects, edits, extracts, validates, preserves, or generates Excel .xlsx or .xlsm workbooks. Trigger on workbook contracts, formulas and cached values, Excel Tables, defined names, OOXML parts, types and precision, macros, charts, hidden sheets, external links, and semantic workbook verification. Do not use for CSV-only work, dataframe computation with no workbook boundary, Excel UI automation, recalculation, connection refresh, or macro execution.

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Use for writing, reviewing, debugging, testing, or analyzing Python SimPy discrete-event simulations. Trigger on Environment, Event, Process, timeout, Resource, PriorityResource, PreemptiveResource, Container, Store, queues, interrupts, simulation clocks, replications, or SimPy monitoring. Do not use for asyncio services, wall-clock schedulers, continuous ODE solvers, or Monte Carlo code without an event-process model.

Use for writing, reviewing, debugging, testing, or optimizing Python SymPy symbolic mathematics. Trigger on Symbol, assumptions, Expr, Eq, solve/solveset, simplify, factor, expand, calculus, matrices, exact arithmetic, lambdify, code generation, or symbolic-to-numeric conversion. Do not use for NumPy-only arrays, mpmath-only arbitrary-precision numerics, CVXPY optimization models, or parsing untrusted mathematical text.

Use for writing, reviewing, debugging, or testing Python command-line interfaces built with Typer, including typed arguments and options, command groups, callbacks, contexts, exit behavior, help, and CliRunner tests. Do not use merely for terminal styling, arbitrary business logic, a Click-only CLI, shell scripts, or invoking an existing command.

Use for writing, reviewing, debugging, testing, or validating Python mpmath arbitrary-precision numerical code. Trigger on mpf, mpc, mp.dps, workdps, interval arithmetic, high-precision quadrature, root finding, special functions, matrices, inverse transforms, or precision/convergence failures. Do not use for ordinary NumPy vectorization, SymPy symbolic manipulation, decimal currency arithmetic, or machine-float code with no precision requirement.

Use for writing, reviewing, debugging, or testing Python JSON serialization and deserialization with orjson, including bytes/text boundaries, datetimes, dataclasses, NumPy, custom default handlers, option flags, strictness, and web/file integration. Do not use for JSON Schema validation, format-preserving JSON edits, streaming I/O frameworks, or choosing a JSON library when orjson is not requested or present.

Write, review, debug, test, or optimize pandas Series, DataFrame, Index, groupby, merge, reshape, dtype, missing-value, and time-series code.

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Design, review, debug, and test Python static types including Protocol, generics, variance, overloads, TypedDict, ParamSpec, TypeGuard, and narrowing.

Write, review, debug, or test Python Xarray workflows for labeled N-dimensional DataArray and Dataset operations, including coordinates, alignment, indexing, groupby, resample, rolling, weighted reduction, Dask-backed execution, and NetCDF/Zarr I/O.

Write, review, debug, and test Python FastAPI applications with path operations, dependencies, Pydantic models, lifespan, middleware, background tasks, exception handling, and ASGI tests.

Create, review, debug, test, or reproduce Python Jupyter notebooks by inspecting format, executing cells top-to-bottom in a clean kernel, and verifying outputs.

Write, debug, and test NumPyro probabilistic programs on JAX with correct shapes, PRNG keys, and inference choice.

Write, review, debug, or test Python code that parses, inspects, transforms, builds, qualifies, optimizes, formats, or transpiles SQL with SQLGlot.

Produces NetworkX code with explicit graph kind, node identity, edge multiplicity, direction, attribute schema, weight semantics, and algorithm preconditions, including testing.

Provides a structured methodology for solving nontrivial, ambiguous problems by framing outcomes, selecting evidence, routing specialists, choosing options, and verifying results.

Write, review, debug, test, or optimize Python code using the rustworkx graph library, with explicit handling of graph kind, index lifecycle, payload semantics, and algorithm result mapping.

Use for writing, reviewing, debugging, or testing Python Streamlit apps, especially rerun behavior, widget identity and callbacks, session_state, cache_data/cache_resource, forms, fragments, containers, multipage navigation, uploads/downloads, and Streamlit-hosted chart/table interaction. Do not use for standalone Plotly/Altair figure design, generic backend services, Dash apps, or deployment configuration without app code.

Use for writing, configuring, integrating, reviewing, debugging, or testing Python structured logging with structlog. Trigger for bound loggers, event dictionaries, processor chains, JSON or console rendering, standard-library logging integration, contextvars, request correlation, exception rendering, and structlog test capture. Do not use for stdlib-logging-only, Loguru-only, metrics-only, tracing-only, or collector configuration tasks that do not use structlog.

Use for writing, reviewing, debugging, testing, or optimizing Python code that inspects, edits, extracts, validates, preserves, or generates Microsoft PowerPoint Open XML presentations, primarily .pptx, using python-pptx, PresentationML/OOXML, Pillow, or supporting Python libraries. Trigger on slides, masters, layouts, placeholders, shapes, text, pictures, tables, charts, notes, themes, hyperlinks, embedded objects, macros, preservation, geometry, rendering verification, and presentation package inspection. Do not use for .ppt binary files, PowerPoint UI automation, VBA execution, slideshow execution, or presentation advice with no Python or file boundary.

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Use for writing, reviewing, debugging, migrating, or testing SQLAlchemy 2.x Core or ORM code involving Engine, Connection, Session, mapped models, select statements, transactions, pooling, results, loading, or AsyncSession. Do not use for raw database SQL with no SQLAlchemy boundary, Alembic migration design, DuckDB relations, or database administration.

Write, review, debug, or interpret Python statistical models using statsmodels, including formulas, regression, GLM, time series, robust covariance, diagnostics, prediction intervals, and inference.

Use for writing, reviewing, debugging, or testing publication-quality display tables in Python with Great Tables, including GT construction, stub and row groups, headers and spanners, labels, numeric/date formatting, targeted styles, footnotes/source notes, HTML/LaTeX/image export, and render verification. Do not use for dataframe computation, interactive data grids, charts, or plain console tables.

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Use for designing, creating, reviewing, or packaging GitHub Copilot and Agent Plugins for Visual Studio Code, including plugin.json, portable skills, MCP components, client-specific agents, commands, and hooks. Do not use for a single standalone skill, ordinary VS Code extensions, or installing an unreviewed plugin.

Use for designing or reviewing resilient asynchronous HTTP call paths that combine HTTPX, Tenacity, and asyncio: client lifetime, time budgets, retry eligibility, backoff, concurrency, cancellation, and idempotency. Do not use for generic HTTPX, retry, or asyncio questions that do not cross these boundaries.

Creates, reviews, debugs, and modernizes Python project structure with deterministic tooling including pyproject.toml, uv, Ruff, Pyright, and pytest.