Superpowers Overview

Captures learnings, errors, and corrections to enable continuous improvement for the agent.

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking...

Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...

Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.

Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞

Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

处理 Office 文档（Word/Excel/PPT/PDF）的技能。当用户要求读取、创建、编辑 Word 文档（.docx）、Excel 表格（.xlsx/.csv）、PPT（.pptx）或 PDF 时使用。基于 python-docx、openpyxl、python-pptx、pypdf 库。Require...

Enforce a four-phase debugging process—root cause investigation, pattern analysis, hypothesis testing, and evidence-based fix verification—for all bugs and f...

Use when about to claim any work is complete, fixed, passing, or successful - requires running fresh verification commands and reading actual output before m...

Use before any creative work - creating features, building components, adding functionality, or modifying behavior - guides through exploration, questioning,...

Enforces strict RED-GREEN-REFACTOR TDD cycle by writing failing real-code tests before implementation, verifying failures, coding minimally, then refactoring...