8234 个 · 按相关度排序
NTLM info disclosure is a Medium-severity finding when chained to context — the leak itself is intentional protocol behavior (RFC-compliant NTLMSSP challenge), but on internet-exposed enterprise infrastructure it provides exact reconnaissance for the next stage of an attack. Highest-value targets:
中文简介正在准备中。当前显示原文说明。
Query Google Places API (New) via the goplaces CLI for text search, place details, resolve, and reviews. Use for human-friendly place lookup or JSON output for scripts.
中文简介正在准备中。当前显示原文说明。
Intelligently organizes files and folders by understanding context, finding duplicates, and suggesting better organizational structures. Use when user wants to clean up directories, organize downloads, remove duplicates, or restructure projects.
中文简介正在准备中。当前显示原文说明。
Git-Notes-Based knowledge graph memory system. Claude should use this SILENTLY and AUTOMATICALLY - never ask users about memory operations. Branch-aware persistent memory using git notes. Handles context, decisions, tasks, and learnings across sessions.
中文简介正在准备中。当前显示原文说明。
Neural web search and code context via Exa AI API. Requires EXA_API_KEY. Use for finding documentation, code examples, research papers, or company info.
中文简介正在准备中。当前显示原文说明。
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
中文简介正在准备中。当前显示原文说明。
Deep Research Agent specializes in complex, multi-step research tasks that require planning, decomposition, and long-context reasoning across tools and files by we-crafted.com/agents/deep-research
中文简介正在准备中。当前显示原文说明。
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
中文简介正在准备中。当前显示原文说明。
Manages project knowledge using ByteRover context tree. Provides two operations: query (retrieve knowledge) and curate (store knowledge). Invoke when user requests information lookup, pattern discovery, or knowledge persistence. Developed by ByteRover Inc. (https://byterover.dev/)
中文简介正在准备中。当前显示原文说明。
Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.
中文简介正在准备中。当前显示原文说明。
Query and curate knowledge-base using ByteRover CLI. Use `brv query` for knowledge retrieval, `brv curate` for adding context, and `brv push/pull` for syncing.
中文简介正在准备中。当前显示原文说明。
Manage Canva designs, assets, and folders via the Connect API. WHAT IT CAN DO: - List/search/organize designs and folders - Export finished designs (PNG/PDF/JPG) - Upload images to asset library - Autofill brand templates with data - Create blank designs (doc/presentation/whiteboard/custom) WHAT IT CANNOT DO: - Add content to designs (text, shapes, elements) - Edit existing design content - Upload documents (images only) - AI design generation Best for: asset pipelines, export automation, organization, template autofill. Triggers: /canva, "upload to canva", "export design", "list my designs", "canva folder".
中文简介正在准备中。当前显示原文说明。
A modern text-based browser. Renders web pages in the terminal using headless Firefox.
中文简介正在准备中。当前显示原文说明。
Run the Model Context Protocol (MCP) Atlassian server in Docker, enabling integration with Jira, Confluence, and other Atlassian products. Use when you need to query Jira issues, search Confluence, or interact with Atlassian services programmatically. Requires Docker and valid Jira API credentials.
中文简介正在准备中。当前显示原文说明。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
中文简介正在准备中。当前显示原文说明。
Create user-facing App Store release notes by collecting and summarizing all user-impacting changes since the last git tag (or a specified ref). Use when asked to generate a comprehensive release changelog, App Store "What's New" text, or release notes based on git history or tags.
中文简介正在准备中。当前显示原文说明。
Extract calendar events, deadlines, action items, and follow-ups from emails. Works with any calendar provider (Google, Outlook, Apple, Notion, etc.). No external dependencies — pure agent intelligence. Use when the user forwards an email, asks to check inbox for events, or wants to extract structured scheduling data from any text.
中文简介正在准备中。当前显示原文说明。
Triage, categorize, and draft responses to emails. Sorts by urgency, flags action items, and generates context-aware reply drafts.
中文简介正在准备中。当前显示原文说明。
Complete Claude Code productivity system — project setup, prompting patterns, sub-agent orchestration, context management, debugging, refactoring, TDD, and shipping 10X faster. Zero scripts needed.
中文简介正在准备中。当前显示原文说明。
High-performance temporary storage system using Redis. Supports namespaced keys (mema:*), TTL management, and session context caching. Use for: (1) Saving agent state, (2) Caching API results, (3) Sharing data between sub-agents.
中文简介正在准备中。当前显示原文说明。
Send personalized LinkedIn direct messages to a list of existing 1st-degree connections via browser automation. Use when the user wants to message LinkedIn connections with AI-personalized outreach — e.g. nurturing leads, following up after events, reconnecting with contacts, or announcing something. Takes a data file (CSV/TSV) or plain list with connection names and companies, asks for outreach context/goal, generates a tailored message per person, and sends each one via browser automation. Handles message compose flow, character limits, and incremental status tracking.
中文简介正在准备中。当前显示原文说明。
Manage LinkedIn outreach leads from Google Sheets — search by name, read live conversation threads, update status, and send contextual follow-up messages. Use after linkedin-dm to move leads through the pipeline (Sent → Replied → Call Scheduled → Demo Done → Closed).
中文简介正在准备中。当前显示原文说明。
Mema's personal brain - SQLite metadata index for documents and Redis short-term context buffer. Use for organizing workspace knowledge paths and managing ephemeral session state.
中文简介正在准备中。当前显示原文说明。
Place MML blocks in Doppel worlds. Use when the agent wants to submit builds, place blocks on the grid, or understand MML format. Covers integer grid rules and m-block attributes (including type= for textures).
中文简介正在准备中。当前显示原文说明。