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Found 2,810 Skills
Remove repeated boilerplate across sections (methodology disclaimers, generic transitions, repeated summaries) while preserving citations and meaning. **Trigger**: redundancy, repetition, boilerplate removal, 去重复, 去套话, 合并重复段落. **Use when**: the draft feels rigid because the same paragraph shape and disclaimer repeats across many subsections. **Skip if**: you are still drafting major missing sections (finish drafting first). **Network**: none. **Guardrail**: do not add/remove citation keys; do not move citations across subsections; do not delete subsection-specific content.
Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization
This skill should be used when working with Convex actions, HTTP endpoints, validators, schemas, environment variables, scheduling, file storage, and TypeScript patterns. It provides comprehensive guidelines for function definitions, API design, database limits, and advanced Convex features.
Deep Reading Collaborative System: A system leveraging multi-layered AI Agents to help transform articles from "read" to "understood" to "mastered", and convert knowledge into actionable plans. Use this system when you need to deeply understand complex articles/papers, systematically organize reading notes, think critically about content, discover hidden logical issues and assumptions, or turn knowledge into action plans. Trigger keywords: deep reading, critical thinking, reading notes, article analysis, Socratic questioning, action plan
Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
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.
Persistent knowledge storage using basic-memory CLI. Use to save notes, search memories semantically, and build context for topics across sessions.
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
Create and edit rich text message drafts for Gmail, Outlook, and WhatsApp. Writes Markdown fragments and assembles platform-specific HTML via build script. Use when writing emails, drafting emails, composing replies, sending messages, writing WhatsApp messages, sending Gmail messages, replying via email, or when user mentions Gmail, Outlook, WhatsApp, email client, "email to", "reply to", "draft an email", "write an email", "send a message", "message to", "WhatsApp to", or professional correspondence.
Convert EPUB books to high-quality formatted Markdown using pandoc and AI-assisted formatting. Use when the user provides an EPUB file path and wants to convert it to professionally formatted Markdown, similar to the Clean Code Collection formatting. This skill handles the complete workflow from EPUB extraction through AI-driven content formatting, including fixing PDF conversion artifacts, joining split paragraphs, correcting code blocks, standardizing headers, and creating proper Table of Contents.
Plan and (when feasible) implement or execute user acceptance tests (UAT) / end-to-end acceptance scenarios. Converts requirements or user stories into acceptance criteria, test cases, test data, and a sign-off checklist; suggests automation (Playwright/Cypress for web, golden/snapshot tests for CLIs/APIs). Use when validating user-visible behavior for a release, or mapping requirements to acceptance coverage.
Use when advanced Pytest features including markers, custom assertions, hooks, and coverage configuration.