Loading...
Loading...
Found 2,794 Skills
Core LifeOS skill for research, synthesis, and Notion storage workflows
Use when developing WordPress plugins: architecture and hooks, activation/deactivation/uninstall, admin UI and Settings API, data storage, cron/tasks, security (nonces/capabilities/sanitization/escaping), and release packaging.
Comprehensive equity research analyst skill that orchestrates all Octagon financial analysis skills. Use when conducting full company analysis, writing initiation of coverage reports, performing due diligence, or creating investment recommendations with quantitative support.
Build websites easily with Wix - create, edit, and manage websites using drag-and-drop tools and templates
Analyze debt covenants and credit agreement terms from SEC filings using Octagon MCP. Use when researching financial covenants, leverage ratios, interest coverage requirements, credit facilities, debt maturity schedules, and covenant compliance from 10-K, 10-Q, and 8-K filings.
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.