Python

Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

Complete guide for building MCP servers with FastMCP 3.0 - tools, resources, authentication, providers, middleware, and deployment. Use when creating Python MCP servers or integrating AI models with external tools and data.

Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Comprehensive guide for Manim Community - Python framework for creating mathematical animations and educational videos with programmatic control

Write Python code in n8n Code nodes. Use when writing Python in n8n, using _input/_json/_node syntax, working with standard library, or need to understand Python limitations in n8n Code nodes.

Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.

Configures Python projects with modern tooling (uv, ruff, ty). Use when creating projects, writing standalone scripts, or migrating from pip/Poetry/mypy/black.

AI-powered browser automation — navigate sites, fill forms, extract structured data, log in with stored credentials, and build reusable multi-step workflows using natural language. Install: pip install skyvern && skyvern setup

Model cap table dilution, SAFE conversion, and exit waterfall across scenarios. Triggered by: "/venture-capital-intelligence:cap-table-waterfall", "model my cap table", "simulate dilution", "SAFE conversion math", "exit waterfall", "how much do I own after Series A", "liquidation waterfall", "cap table scenario", "what happens to equity at exit", "model the waterfall", "how much equity do I have left", "what is my ownership after funding", "run dilution scenarios", "model a new round", "what happens at acquisition", "cap table after SAFE conversion", "pari passu waterfall", "preference stack analysis". Claude Code only. Requires Python 3.x.

Scan a company or sector for deal-sourcing signals across 6 dimensions. Triggered by: "/venture-capital-intelligence:deal-sourcing-signals", "scan signals for X", "what signals is X showing", "deal sourcing scan", "hiring signals for X", "is X raising soon", "monitor this company", "company signal scan", "sourcing brief for X", "what is X up to", "is X growing", "track this company", "deal signal report for X", "is this company fundraising", "what are the momentum signals for X", "find signals on X", "is X worth tracking". Claude Code only. Requires Python 3.x. Uses web search for live signal data.

Run deterministic financial models for startup valuation and SaaS health analysis. Triggered by: "/venture-capital-intelligence:financial-model", "run a financial model on X", "DCF this company", "model the financials", "calculate runway", "what is the valuation", "SaaS metrics model", "LTV CAC analysis", "unit economics", "burn rate analysis", "comparable valuation", "how long is my runway", "what's my burn multiple", "revenue projection for X", "model the ARR growth", "what is the pre-money valuation", "comps analysis", "NRR and churn model", "how healthy are these SaaS metrics". Claude Code only. Requires Python 3.x. Accepts user-supplied numbers or searches for publicly available data.

Compute fund KPIs (TVPI, DPI, IRR, MOIC), model carried interest and management fees, and generate LP quarterly update narratives. Triggered by: "/venture-capital-intelligence:fund-operations", "calculate fund KPIs", "what is my fund TVPI", "IRR calculation", "compute MOIC", "LP report", "quarterly update draft", "carried interest calculation", "management fee calculation", "fund performance report", "write my LP update", "how is my fund performing", "what is my DPI", "fund returns analysis", "model my carry", "how much carry do I earn", "portfolio performance summary", "generate investor update". Claude Code only. Requires Python 3.x.

Deterministic Python-scored startup screening with full audit trail. Use when you need a reproducible, weighted-score verdict on a startup — not just a qualitative opinion. Triggered by: "/venture-capital-intelligence:hard-screening-startup", "hard screen this startup", "run a hard screen on X", "score this startup with Python", "give me an auditable screen", "run a scored evaluation on X", "give me a weighted score for this startup", "screen with numbers", "objective startup score", "reproducible screen", "investment scorecard for X", "score this company out of 100", "run the full screen on X". Claude Code only. Requires Python 3.x. For conversational soft-mode screening, use /venture-capital-intelligence:soft-screening-startup.

Run TAM/SAM/SOM market sizing with top-down and bottom-up methods, competitive landscape, and tech stack analysis. Triggered by: "/venture-capital-intelligence:market-size", "size this market", "what is the TAM for X", "market sizing analysis", "competitive landscape for X", "who are the competitors", "TAM SAM SOM for X", "market opportunity analysis", "how big is this market", "is this market big enough", "what's the addressable market", "total addressable market for X", "how large is the opportunity", "market research for X", "how saturated is this market", "market size estimate", "go-to-market sizing", "what is the serviceable market". Claude Code only. Requires Python 3.x. Uses web search for market data.

Activate for ANY startup evaluation, investment screening, or company assessment. Triggers include: "evaluate this startup", "screen this company", "should I invest in X", "is this a good investment", "what do you think about this company", "review this startup", "score this company", "rate this pitch", "assess this founder", "quick take on X", "is X worth investing in", "pass or decline on X", "what's your verdict on X", "first look at this company", "quick screen on X", "what's your take on this founder", "is this fundable", "would a VC invest in this". Also triggers when a user pastes a company description, funding ask, or founder background and asks for an opinion. Works on claude.ai and Claude Code. For hard-mode deterministic scoring with Python audit trail, use /venture-capital-intelligence:hard-screening-startup.

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

Sets up, develops, tests, and deploys Render Workflows. Covers first-time scaffolding (via CLI or manual), SDK installation (Python or TypeScript), task patterns (retries, subtasks, fan-out), local development, Dashboard deployment, and troubleshooting. Use when a user wants to set up Render Workflows for the first time, scaffold a workflow service, add or modify workflow tasks, test workflows locally, or deploy workflows to Render.

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

Master Python asynchronous programming with asyncio, async/await,

Use when Python data modeling with dataclasses, attrs, and Pydantic. Use when creating data structures and models.

Use when Python's type system including type hints, mypy, Protocol, TypedDict, and Generics. Use when working with Python type annotations.

Adaptive Python development guide with tiered complexity levels (Minimal/Standard/Full). Automatically selects appropriate guidance based on project context - from simple scripts (just clean Python code) to full production systems (complete tooling ecosystem). Covers modern conventions, testing, tooling, security, and best practices. Use when writing Python code, converting scripts, setting up projects, or building production systems. Keywords: PEP-8, Ruff, pytest, mypy, simple scripts, project structure, PyPI, packaging, type hints, clean code

Use when working with TESSERA satellite embeddings — downloading via CLI, sampling via the Python or R library, choosing between point-based and mosaic approaches, or exporting to GeoTIFF/NPY/Zarr.

Alembic database migrations for Python applications - setup, auto-generation, manual migrations, and safe deployment patterns.

Write pytest tests with fixtures, parametrization, mocking, async testing, and modern patterns. Use when creating or updating Python test files. Not for unittest — use standard library patterns instead.

Modern Python 3.12+ development with uv, ruff, and production-ready practices. Routes to specialized skills for frameworks and testing.

Debug Python errors, exceptions, and unexpected behavior. Analyzes tracebacks, reproduces issues, identifies root causes, and provides fixes.

Review Python code with high quality bar for type hints, Pythonic patterns, and maintainability.

Pure-Python knowledge base statistics dashboard. Reads shelf-index and log.md; emits Inventory, Layer distribution, Domain distribution, Recent Activity, and Staleness sections. No agent dispatch. Read-only.

CAD modeling with build123d Python library. Use when creating 3D models, exporting to GLB/STEP/STL, or doing boolean operations (union, difference, intersection). Triggers on: CAD, 3D modeling, sphere, box, cylinder, mesh export, GLB, STEP, STL, solid modeling, parametric design, threads, fasteners, bolts, nuts, screws, gears, pipes, flanges, bearings, bd_warehouse, spur gear, helical gear, bevel gear, planetary gear, ring gear, cycloid gear, rack and pinion, gggears, herringbone, gear mesh, gear train.

Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.

Use this skill when reviewing Python code for common anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.

Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards.

Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing environment-specific behavior.

Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use this skill when designing a new service or component from scratch and choosing how to layer responsibilities, when refactoring a God class or monolithic function that has grown too large, when deciding whether to add a new abstraction or live with duplication, when evaluating a pull request for structural issues like tight coupling or leaking internal types, when choosing between inheritance and composition for a new class hierarchy, or when a codebase is becoming hard to test because of entangled I/O and business logic.

Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.

Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.

Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

Python project organization, module architecture, and public API design. Use when setting up new projects, organizing modules, defining public interfaces with __all__, or planning directory layouts.

Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.

Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

Python type safety with type hints, generics, protocols, and strict type checking. Use when adding type annotations, implementing generic classes, defining structural interfaces, or configuring mypy/pyright.

Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.

Use when you need to run interactive CLI tools (vim, git rebase -i, Python REPL, etc.) that require real-time input/output - provides tmux-based approach for controlling interactive sessions through detached sessions and send-keys

Connect your AI assistant to GoHighLevel CRM via the official API v2. Manage contacts, conversations, calendars, pipelines, invoices, payments, workflows, and 30+ endpoint groups through natural language. Includes interactive setup wizard and 100+ pre-built, safe API commands. Python 3.6+ stdlib only — zero external dependencies.

Teambition project management via Python scripts: projects, tasks, task traces, comments, files, members. Use when: (1) querying/creating/updating tasks or projects, (2) managing task progress and comments, (3) uploading files to tasks, (4) querying team members. NOT for: non-Teambition platforms, direct API calls without scripts, or operations not covered by available scripts.

Complete AI talent discovery and outreach workflow using BrightData MCP or Python scraping. This skill should be used when users need to find PhD students, researchers, or engineers in AI/ML fields, extract their profiles, identify Chinese candidates, classify by type, deduplicate, and generate personalized outreach emails.

Manage Agentmail.to inbox operations with deterministic Python scripts: list/read messages, download and analyze attachments, reply with sender filters, and set read/unread state. Use when handling inbox workflows for any Agentmail.to inbox.

Access Yahoo Finance data including real-time pricing, fundamentals, analyst estimates, options, news, and historical data via the yahooquery Python library.

Query Strava activities, stats, and workout data using Python/stravalib with interactive setup

Generate complete, installable OpenClaw trading skills from natural language strategy descriptions. Use when your human wants to create a new trading strategy, build a bot, generate a skill, automate a trade idea, turn a tweet into a strategy, or asks \"build me a skill that...\". Produces a full skill folder (SKILL.md + Python script + config) ready to install and run.

6 institutional-grade Polymarket trading tools. NegRisk arbitrage (100% win rate), latency arb, BTC scalping, alpha scanner, universe scanner, edge detection. Battle-tested on 8,347 signals. No Python required.