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Found 5,143 Skills
Optimizer that refines and professionalizes AI agent skills through real usage — saves tokens, eliminates redundancy, and tightens instructions so skills cost less to run. Learns from mistakes, reviews quality, and improves over time. Observes skill execution in the current conversation, analyzes up to four sources (conversation friction, file diffs, user feedback, static diagnostic) plus accumulated lessons, and proposes concrete improvements to the target skill's SKILL.md. Works with Claude Code and compatible SKILL.md-based agent frameworks. Use after executing any skill: `/skill-optimizer [name]` or `/skill-optimizer` to auto-detect. `--review` processes accumulated lessons.
This skill helps users extract full article contents from WeChat using the BrowserAct API. The Agent should proactively apply this skill when users express needs like finding full WeChat articles for specific keywords, tracking WeChat public accounts for industry trends, extracting WeChat article contents for media research, monitoring public relations on WeChat platforms, collecting competitor updates from WeChat, getting full article body from WeChat links, monitoring brand exposure on WeChat articles, retrieving structured WeChat data for sentiment analysis, summarizing daily news from WeChat, getting author and publication date for WeChat articles, or automating WeChat content extraction without scraping.
Use this skill whenever calling agent-uml MCP tools (design_create, diagram_upsert, design_feedback, design_export) to render PlantUML diagrams on the collaborative canvas. Covers three tiers — rendering safety (syntax that prevents HTTP 400 blank canvas), conversation mechanics (when to push a version vs ask a question, what to write in the message parameter), and design effectiveness (decomposition thresholds, cross-diagram traceability, export readiness). Trigger even when the task seems simple — a missing `as alias` makes elements un-annotatable, and a skinparam mismatch makes diagrams unreadable on the warm
Use this skill whenever designing, building, or reviewing a command-line tool that AI agents or automation will invoke — covers non-interactive flags, layered --help with examples, stdin/pipeline composition, actionable errors, idempotency, dry-run, destructive-action safety, and predictable command structure. Trigger even if the user doesn't explicitly say "agent-friendly" — apply whenever they are writing `--help` text, adding a new subcommand, designing error messages, or reviewing a CLI's UX.
Agent harness architecture — structure a project's agent context across layers for effective AI-assisted development. Covers CLAUDE.md, skills, design docs, hooks, and all artifacts that shape how an agent understands and operates in a codebase. Use when setting up or improving a project's agent configuration, when agent context feels bloated or disorganized, when onboarding a new project for AI-assisted development, or when the agent keeps losing architectural awareness mid-task. Trigger on phrases like "set up claude", "improve CLAUDE.md", "agent keeps forgetting", "context is too long", "harness setup", "organize agent context", "how should I structure my prompts". Supports arguments: `/harness audit` to evaluate an existing project's context architecture, `/harness init` to set up harness from scratch.
The foundational context engineering skill — start here when exploring the discipline. This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Also activates when the user mentions "context engineering" or "context-engineering" for foundational understanding of AI agent context systems.
Apply Giddens' structuration theory to analyze the duality of structure — how social structures are both the medium and outcome of the practices they organize. Use this skill when the user needs to bridge agency and structure in organizational or social analysis, explain how routines reproduce or transform institutional patterns, analyze the recursive relationship between action and structure, or when they ask 'do people shape institutions or do institutions shape people', 'how are these routines maintained', or 'where does change come from if structures constrain action'.
Measure and optimize customer service performance using CSAT, NPS, CES, First Contact Resolution, and text mining on support tickets. Use this skill when the user needs to evaluate CS team performance, identify top complaint drivers, optimize staffing, or build CS dashboards — even if they say 'is our CS team doing well', 'what are customers complaining about', 'how many agents do we need', or 'build a CS dashboard'.
Guide for creating, improving, benchmarking, and packaging Claude Agent Skills (SKILL.md files). Invoke when users want to create a skill from scratch, improve or test an existing skill, benchmark skill performance with variance analysis, or optimize a skill description for triggering accuracy. Also invoke when users say "turn this into a skill", "make a skill for X", "help me write a SKILL.md", "my skill isn't firing correctly", or want to convert a workflow/conversation into a reusable skill. Invoke proactively when a conversation has produced a repeatable workflow worth capturing. If the user mentions SKILL.md, skill files, skill descriptions, or skill triggering, this skill applies.
Security auditor for Claude Code skills and agent definitions. Scans a skill or agent directory for prompt injection, data exfiltration, privilege escalation, memory poisoning, obfuscation, malicious persistence, and 12 other threat categories (18 total). Returns a graded verdict (OK / WARNING / CRITICAL) with detailed findings. Use this skill whenever you need to audit, review, or validate the safety of a skill, an agent definition, a system prompt, or any set of instruction files before installing or trusting them. Also use it when the user mentions security scanning, threat detection, prompt injection checking, or wants to verify that a skill is safe. Triggers on: /maton, "audit this skill", "is this skill safe", "check for injection", "scan for threats", "review this agent", "security check".
Command-line interface for Ollama - Local LLM inference and model management via Ollama REST API. Designed for AI agents and power users who need to manage models, generate text, chat, and create embeddings without a GUI.
Protects LLM agent systems in real-time with a 5-tier filter (hash cache, rule engine, ML classifier, LLM judge, human approval) and an async learning engine. Synthesizes new rules from every detected attack, adding less than 50ms latency. Trigger on 'add security layer', 'prevent prompt injection', 'adaptive guard', 'runtime protection', or 'agent security'.