github/mkdocs-translations
Generate a language translation for a mkdocs documentation stack.
Generate a language translation for a mkdocs documentation stack.
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/mkdocs-translationsGenerate a language translation for a mkdocs documentation stack.
This repo contains 20 individual skills — each has its own dedicated page.
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
Instrument a webapp to send useful telemetry data to Azure App Insights
Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.
Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.
Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.
Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.
Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.
Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.
Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.
A curated collection of software engineering skills for Claude Code
Use for: (1) Blave market alpha data — 籌碼集中度 Holder Concentration, 多空力道 Taker Intensity, 巨鯨警報 Whale Hunter, 擠壓動能 Squeeze Momentum, 市場方向 Market Direction, 資金稀缺 Capital Shortage, 板塊輪動 Sector Rotation, Blave頂尖交易員 Top Trader Exposure, kline, alpha table, 市場情緒 Market Sentiment, screener saved conditions, Hyperliquid top trader tracking (leaderboard, positions, history, performance, bucket stats), Taiwan stock daily OHLCV, forward-adjusted prices, institutional investor buy/sell, margin trading data, shareholding distribution, quarterly fundamental statements — income statement, balance sheet, cash flow, and broker/dealer daily buy/sell by branch (台股日K/向後調整/三大法人/融資融券/股權持股分級表/綜合損益表/資產負債表/現金流量表/分點買賣超); (2) CME / ICE futures OHLCV — WTI crude oil (CL), gold (GC), Brent crude (BRN); daily/hourly/minute candles from 2010; (3) Taiwan Futures OHLCV — TXF (台指期近月連續); daily/intraday candles (1d/1m/5m/15m/30m/60m), 1d from 2013-12-30 and intraday from 2014-01-02; (4) BitMart futures/contract trading — opening/closing position
Analyze and resolve Sentry comments on GitHub Pull Requests. Use this when asked to review or fix issues identified by Sentry in PR comments. Can review specific PRs by number or automatically find recent PRs with Sentry feedback.
Implements unit, widget, and integration tests for a Flutter app. Use when ensuring code quality and preventing regressions through automated testing.
Daily curation agent: discovers new Claude Code skills and AI/agent tools, scores them, and sends a Telegram shortlist for approval.
Personal Agent OS for Claude Code — 42 hooks, 3,432 skills, 93 agents. Blocks rm -rf, prompt injection, pipe-to-shell at runtime. Apache 2.0.