Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Cowork-style cleanup of a messy folder or shared drive -- classifies every file, proposes a target structure, renames to a consistent convention, deduplicates, and writes an index. Use on Downloads, Desktop, client folders, or team shared drives.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Turn a chaotic folder into an organized, navigable workspace the way Claude Cowork works through a directory: inventory everything first, propose before moving, then execute with a full undo trail. Never destroy information -- every move is logged and reversible.
Take a directory path. If none is provided, ask for one. Confirm scope before touching anything: subdirectories included or not, and whether any paths are off-limits.
~$ locks, empty files).final, final2, v3, (1)). Identify the canonical copy (newest complete version) and mark the rest.YYYY-MM-DD-descriptive-name.ext for dated documents, client-project-doctype for client work), the duplicate resolution list, and the junk list. Wait for approval before moving anything._review-before-deleting/ -- never delete outright. Log every operation as old-path -> new-path in _organizer-log.md.INDEX.md at the root: the new tree, what lives where, and a table of the 20 most recently active files so the owner can re-orient instantly._review-before-deleting/ and let the human pull the trigger.When run as a recurring Cowork scheduled task (e.g. weekly on Downloads), skip the approval step only for previously approved rules: apply the established convention to new files, quarantine new junk, and post a summary of what moved. Anything that does not match an existing rule goes to _needs-decision/ and is listed in the summary.
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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