Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Generates structured OKR plans (Objectives and Key Results) for teams and companies following Google/Intel methodology. Takes company goals, team function, quarter, and current metrics to produce a comprehensive okr-plan.md with objectives, key results, scoring criteria, alignment mapping, tracking cadence, and retrospective templates.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Generate a comprehensive, actionable okr-plan.md from user inputs, applying the Google/Intel OKR framework.
references/methodology.md -- Core OKR principles (non-negotiable rules to apply).references/inputs.md -- The four required inputs plus optional context.references/output-template.md -- Exact structure of the generated okr-plan.md.references/best-practices.md -- Appendix content (common mistakes, OKR cycle, grading by KR type, reading list).references/inputs.md. Collect the four required inputs: company goals, team function, quarter/period, current metrics/baseline. If any are missing, ask for them. Never fabricate baselines.references/methodology.md and apply every core principle. Objectives are qualitative and inspirational (no numbers); key results are quantitative and measurable (always a number).references/best-practices.md.references/output-template.md as okr-plan.md in the working directory (or a user-specified path). Produce at least 400 substantive lines, no filler.okr-plan-engineering.md, okr-plan-marketing.md).[ADJUSTED], and include rationale per change.还没有评价。欢迎分享你的使用感受。
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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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