Browse
Search and filter by type across the catalog
1,359 results in Hooks, Skills, Infrastructure, Identity · page 52 of 57
task-execution-engine
Execute implementation tasks from design documents using markdown checkboxes. Use when (1) implementing features from feature-design-assistant output, (2) resuming interrupted work, (3) batch executing tasks. Triggers on 'start implementation', 'run tasks', 'resume'.
tdd-gate
Test-Driven Development enforcement hook. Blocks editing production code files (.cs, .py, .ts, .go, .rs, .rb, .php, .java, .kt, .swift, .dart) unless a corresponding test file exists. Forces the TDD workflow: write tests first, then implement. Automatically skips config files, migrations, DTOs, test files themselves, and infrastructure files. Looks for test files with common naming patterns (MyClassTest.ext, my-class.test.ext, my_class_test.ext, test_my_class.ext). Inspired by pm-workspace's Spec-Driven Development methodology.
tech-resume-optimizer
Optimize resumes for software engineering, product management, and technical roles. Use when the user mentions software engineer, developer, PM, data scientist, ML, DevOps, or other technical role resumes.
telegram-bot-builder
This skill should be used when the user asks to "create a Telegram bot", "build a Telegram chatbot", "set up a Telegram webhook", "add inline keyboards to a bot", "handle Telegram callback queries", "implement Telegram payments", "send media via Telegram bot", "configure Telegram bot commands", "deploy a Telegram bot", or mentions the Telegram Bot API, telegram bot tokens, getUpdates, setWebhook, or bot frameworks like node-telegram-bot-api, grammy, python-telegram-bot, or aiogram. Provides comprehensive guidance for building production-ready Telegram bots with Node.js and Python.
telegram-bot-builder-2
Expert in building Telegram bots that solve real problems - from simple automation to complex AI-powered bots. Covers bot architecture, the Telegram Bot API, user experience, monetization strategies, and scaling bots to thousands of users. Use when: telegram bot, bot api, telegram automation, chat bot telegram, tg bot.
telegram-detailed-notifications
Send detailed Telegram notifications with session information when Claude Code finishes. Includes working directory, session duration, and system info. Requires TELEGRAM_BOT_TOKEN and TELEGRAM_CHAT_ID environment variables.
telegram-error-notifications
Send Telegram notifications when Claude Code encounters long-running operations or when tools take significant time. Helps monitor productivity and catch potential issues. Requires TELEGRAM_BOT_TOKEN and TELEGRAM_CHAT_ID environment variables.
telegram-mini-app
Expert in building Telegram Mini Apps (TWA) - web apps that run inside Telegram with native-like experience. Covers the TON ecosystem, Telegram Web App API, payments, user authentication, and building viral mini apps that monetize. Use when: telegram mini app, TWA, telegram web app, TON app, mini app.
telegram-notifications
Send Telegram notifications when Claude Code finishes working. Requires TELEGRAM_BOT_TOKEN and TELEGRAM_CHAT_ID environment variables. Get bot token from @BotFather, get chat ID by messaging the bot and visiting https://api.telegram.org/bot<TOKEN>/getUpdates
telegram-pr-webhook
Send Telegram notification when a new PR is created via gh pr create. Includes PR URL and Vercel preview URL. Requires TELEGRAM_BOT_TOKEN and TELEGRAM_CHAT_ID environment variables. Optionally set VERCEL_PROJECT_NAME and VERCEL_TEAM_SLUG to construct the Vercel preview URL automatically.
telegram-pr-webhook-2
Telegram PR Webhook Hook Sends a Telegram notification when a new PR is created via `gh pr create`. Includes the PR URL and the Vercel preview URL. Required environment variables: TELEGRAM_BOT_TOKEN - Bot token from @BotFather TELEGRAM_CHAT_ID - Chat ID for notifications Optional environment variables: VERCEL_PROJECT_NAME - Vercel project name (for preview URL) VERCEL_TEAM_SLUG - Vercel team slug (for preview URL)
tensorrt-llm
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.