Claude Code Skills · page 89

Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s manifests, GitHub Actions workflows, package.json, filter data.

Scans for project documentation files (AGENTS.md, CLAUDE.md, GEMINI.md, COPILOT.md, CURSOR.md, WARP.md, and 15+ other formats) and synthesizes guidance. Auto-activates when user asks to review, understand, or explore a codebase, when starting work in a new project, when asking about conventions or agents, or when documentation context would help. Can consolidate multiple platform docs into unified AGENTS.md.

Modern file and content search using fd, ripgrep (rg), and fzf. Triggers on: fd, ripgrep, rg, find files, search code, fzf, fuzzy find, search codebase.

Modern find-and-replace using sd (simpler than sed) and batch replacement patterns. Triggers on: sd, find replace, batch replace, sed replacement, string replacement, rename.

Enhanced git operations using lazygit, gh (GitHub CLI), and delta. Triggers on: stage changes, create PR, review PR, check issues, git diff, commit interactively, GitHub operations, rebase, stash, bisect.

Model Context Protocol (MCP) server patterns for building integrations with Claude Code. Triggers on: mcp server, model context protocol, tool handler, mcp resource, mcp tool.

Detects stale project plans and suggests session commands. Triggers on: sync plan, update plan, check status, plan is stale, track progress, project planning.

Python asyncio patterns for concurrent programming. Triggers on: asyncio, async, await, coroutine, gather, semaphore, TaskGroup, event loop, aiohttp, concurrent.

CLI application patterns for Python. Triggers on: cli, command line, typer, click, argparse, terminal, rich, console, terminal ui.

SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.

Fast Python environment management with uv (10-100x faster than pip). Triggers on: uv, venv, pip, pyproject, python environment, install package, dependencies.

FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.

Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.

pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.

Python type hints and type safety patterns. Triggers on: type hints, typing, TypeVar, Generic, Protocol, mypy, pyright, type annotation, overload, TypedDict.

Quick reference for RESTful API design patterns, HTTP semantics, caching, and rate limiting. Triggers on: rest api, http methods, status codes, api design, endpoint design, api versioning, rate limiting, caching headers.

Security patterns and OWASP guidelines. Triggers on: security review, OWASP, XSS, SQL injection, CSRF, authentication, authorization, secrets management, input validation, secure coding.

Quick reference for common SQL patterns, CTEs, window functions, and indexing strategies. Triggers on: sql patterns, cte example, window functions, sql join, index strategy, pagination sql.

Patterns for SQLite databases in Python projects - state management, caching, and async operations. Triggers on: sqlite, sqlite3, aiosqlite, local database, database schema, migration, wal mode.

Search code by AST structure using ast-grep. Find semantic patterns like function calls, imports, class definitions instead of text patterns. Triggers on: find all calls to X, search for pattern, refactor usages, find where function is used, structural search, ast-grep, sg.

Quick reference for Tailwind CSS utility patterns, responsive design, and configuration. Triggers on: tailwind, utility classes, responsive design, tailwind config, dark mode css, tw classes.

Run project commands with just. Check for justfile in project root, list available tasks, execute common operations like test, build, lint. Triggers on: run tests, build project, list tasks, check available commands, run script, project commands.

Cross-language testing strategies and patterns. Triggers on: test pyramid, unit test, integration test, e2e test, TDD, BDD, test coverage, mocking strategy, test doubles, test isolation.

Recommend the right agents and skills for any task. Covers both heavyweight agents (Task tool) and lightweight skills (Skill tool). Triggers on: which agent, which skill, what tool should I use, help me choose, recommend agent, find the right tool.

Create or update numbered task docs using repo context, DOC_TEMPLATE.md, and naming conventions.

Create charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.

Persistent project documentation system that maintains context across sessions. Creates structured Memory Bank files to preserve project knowledge, decisions, and progress.

Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.

Python FastAPI development with uv package manager, modular project structure, SQLAlchemy ORM, and production-ready patterns.

Helps write conventional commit messages, create atomic commits, and follow git best practices. Use when the agent needs to help with git commits, commit message writing, or git workflow guidance.

Guide for creating new Agent Skills. Use this skill when you need to create a new skill.

General principles for exploring, developing, editing, and refactoring code. Use for codebase analysis, implementation tasks, and code quality improvements.

Guidelines for iteratively planning tasks and changes before implementation. Use this when the user requests a non-trivial task or when you need to align on a complex implementation strategy.

Provides a structured workflow for planning and executing code reviews like a senior engineer. Use when asked to review code, PRs, or plan a code review task.

Interactive skill creation assistant that guides users through building new Agent Skills for Claude Code. Use when creating new skills, building custom capabilities, or when the user runs /new-skill command. Helps design skill structure, craft descriptions, create scripts, and organize supporting files.

Display knowledge base status and recent learnings

Disable continuous learning mode

Enable continuous learning mode for automatic insight extraction

Clear the knowledge base and start fresh

Extract and persist insights from the current conversation to the knowledge base

Manually route a query to the optimal Claude model (Haiku/Sonnet/Opus)

Display Claude Router usage statistics and cost savings

Design systems, plan implementations, review architecture decisions - Use when you need to plan a complex feature, design system architecture, or make high-level technical decisions.

Write, edit, and refactor code with full tool access - Use when you need to actually write or modify code, create files, run commands, or implement features.

Research documentation, find examples, explain APIs and libraries - Use when you need to look up official documentation, find code examples, understand library APIs, or research best practices.

Quickly search and navigate the codebase - Use when you need to quickly find files, search for patterns, or locate specific code without deep analysis.

Use this skill when the user wants to find customer leads from short-video comments, generate customer-discovery keywords, review lead intent, query a customer pool, or prepare outreach scripts with PPXC.

Prevent feature creep when building software, apps, and AI-powered products. Use this skill when planning features, reviewing scope, building MVPs, managing backlogs, or when a user says "just one more feature." Helps developers and AI agents stay focused, ship faster, and avoid bloated products.

Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration

Guidelines for building production-ready Convex apps covering function organization, query patterns, validation, TypeScript usage, error handling, and the Zen of Convex design philosophy

How to create, structure, and publish self-contained Convex components with proper isolation, exports, and dependency management

Scheduled function patterns for background tasks including interval scheduling, cron expressions, job monitoring, retry strategies, and best practices for long-running tasks

Complete file handling including upload flows, serving files via URL, storing generated files from actions, deletion, and accessing file metadata from system tables

Writing queries, mutations, actions, and HTTP actions with proper argument validation, error handling, internal functions, and runtime considerations

External API integration and webhook handling including HTTP endpoint routing, request/response handling, authentication, CORS configuration, and webhook signature validation

Schema migration strategies for evolving applications including adding new fields, backfilling data, removing deprecated fields, index migrations, and zero-downtime migration patterns

Patterns for building reactive apps including subscription management, optimistic updates, cache behavior, and paginated queries with cursor-based loading

Defining and validating database schemas with proper typing, index configuration, optional fields, unions, and migration strategies for schema changes

Deep security review patterns for authorization logic, data access boundaries, action isolation, rate limiting, and protecting sensitive operations

Quick security audit checklist covering authentication, function exposure, argument validation, row-level access control, and environment variable handling

Umbrella skill for all Convex development patterns. Routes to specific skills like convex-functions, convex-realtime, convex-agents, etc.

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Analyze Mnemon harness eval reports, classify outcomes, and extract improvement evidence.

Turn stable Mnemon harness eval findings into scoped project, loop, adapter, docs, or eval asset improvements.

Design a scenario-driven Mnemon harness eval with target, hypothesis, HostAgent, loop configuration, evidence, and rubric.

Execute or supervise a planned Mnemon harness eval run in an isolated HostAgent workspace.

Manage project-scoped Mnemon goal state, evidence, verification, completion, blockers, and host goal links.

Read scoped memory from Local Mnemon when GUIDE.md indicates that prior memory may help the current task.

Submit durable memory candidates to Local Mnemon when GUIDE.md indicates that a stable fact, preference, decision, or continuity item should be kept.

Draft or revise high-quality SKILL.md content for approved or proposed Mnemon skill changes.

Start a low-frequency review of skill evidence and canonical skill lifecycle state.

Submit approved skill lifecycle and content changes to Local Mnemon.

Record lightweight skill usage evidence when GUIDE.md indicates that a turn produced reusable workflow or lifecycle signal.

Persistent memory CLI for WorkBuddy. Store facts, recall past knowledge, link related memories, manage lifecycle.

Add persistent graph-based memory to NanoClaw agents using mnemon. Agents recall context before responding and remember insights after. Each group gets isolated memory with optional global shared store.

分析已提取的小红书收藏内容，做 AI 总结、对比、提炼

批量提取小红书帖子，整理为本地 Markdown 知识库

生成小红书封面图（HTML+CSS → Playwright 截图）

提取小红书帖子内容（文字、图片、视频转录），整理为 Markdown 并保存

Manage Linux systems covering systemd services, process management, filesystems, networking, performance tuning, and troubleshooting. Use when deploying applications, optimizing server performance, diagnosing production issues, or managing users and security on Linux servers.

Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.

Design cloud network architectures with VPC patterns, subnet strategies, zero trust principles, and hybrid connectivity. Use when planning VPC topology, implementing multi-cloud networking, or establishing secure network segmentation for cloud workloads.

Design comprehensive security architectures using defense-in-depth, zero trust principles, threat modeling (STRIDE, PASTA), and control frameworks (NIST CSF, CIS Controls, ISO 27001). Use when designing security for new systems, auditing existing architectures, or establishing security governance programs.

Assembles component outputs from AI Design Components skills into unified, production-ready component systems with validated token integration, proper import chains, and framework-specific scaffolding. Use as the capstone skill after running theming, layout, dashboard, data-viz, or feedback skills to wire components into working React/Next.js, Python, or Rust projects.

Builds AI chat interfaces and conversational UI with streaming responses, context management, and multi-modal support. Use when creating ChatGPT-style interfaces, AI assistants, code copilots, or conversational agents. Handles streaming text, token limits, regeneration, feedback loops, tool usage visualization, and AI-specific error patterns. Provides battle-tested components from leading AI products with accessibility and performance built in.

Constructs secure, efficient CI/CD pipelines with supply chain security (SLSA), monorepo optimization, caching strategies, and parallelization patterns for GitHub Actions, GitLab CI, and Argo Workflows. Use when setting up automated testing, building, or deployment workflows.

Build professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.

Builds form components and data collection interfaces including contact forms, registration flows, checkout processes, surveys, and settings pages. Includes 50+ input types, validation strategies, accessibility patterns (WCAG 2.1), multi-step wizards, and UX best practices. Provides decision trees from data type to component selection, validation timing guidance, and error handling patterns. Use when creating forms, collecting user input, building surveys, implementing validation, designing multi-step workflows, or ensuring form accessibility.

Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).

Configure host-based firewalls (iptables, nftables, UFW) and cloud security groups (AWS, GCP, Azure) with practical rules for common scenarios like web servers, databases, and bastion hosts. Use when exposing services, hardening servers, or implementing network segmentation with defense-in-depth strategies.

Configure nginx for static sites, reverse proxying, load balancing, SSL/TLS termination, caching, and performance tuning. When setting up web servers, application proxies, or load balancers, this skill provides production-ready patterns with modern security best practices for TLS 1.3, rate limiting, and security headers.

Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.

Debugging workflows for Python (pdb, debugpy), Go (delve), Rust (lldb), and Node.js, including container debugging (kubectl debug, ephemeral containers) and production-safe debugging techniques with distributed tracing and correlation IDs. Use when setting breakpoints, debugging containers/pods, remote debugging, or production debugging.

Deployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.

Selecting and implementing AWS services and architectural patterns. Use when designing AWS cloud architectures, choosing compute/storage/database services, implementing serverless or container patterns, or applying AWS Well-Architected Framework principles.

Design and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.

Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.

Design APIs that are secure, scalable, and maintainable using RESTful, GraphQL, and event-driven patterns. Use when designing new APIs, evolving existing APIs, or establishing API standards for teams.

When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.