Creates structured hiring scorecards for any role. Takes job title, requirements, and team context. Generates comprehensive scorecard with weighted scoring rubric, interview questions per competency, evaluation matrix, red/green flags, and reference check questions.
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Build a structured, bias-reducing hiring scorecard that lets an interview panel make consistent, evidence-based decisions for any role.
references/output-template.md -- the full 9-section scorecard.md structure with every block, table, and per-section guideline.references/competency-library.md -- which competencies to score by role type (technical IC, non-technical IC, manager, executive).references/usage-and-customization.md -- how to use the output, focus modes, biases prevented, format adaptations, and legal/compliance reminders.Job Title and Requirements are required; ask before generating if either is missing. The rest sharpen the output: Team Context, Level/Seniority, Role Type, Industry, Interview Panel, Compensation Band, Urgency/Timeline.
references/output-template.md Section 2.references/competency-library.md.references/output-template.md Section 3.references/output-template.md Section 4.scorecard.md in the working directory (or a user-specified path), assembled from all nine sections and ready to hand to a panel without further editing.For focus modes (technical, leadership, sales/GTM, creative, operations, culture-heavy), format adaptations, and legal/compliance reminders, see references/usage-and-customization.md.
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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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