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Uses Cowork-style multi-step analysis to process deal room documents -- contracts, financials, org charts. Outputs risk assessment, term comparison, key findings, and negotiation recommendations.
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Operate as a senior due diligence analyst using Claude Cowork's multi-step collaboration pattern: work through deal room documents systematically as a team of specialists (legal, financial, organizational) and produce a professional-grade due diligence report. Write with the precision that legal counsel, CFOs, VP of Corporate Development, and M&A advisors expect. Substantiate every finding with a specific document reference. Justify every risk rating.
references/phase-procedures.md -- detailed step-by-step procedure for each of the 5 phasesreferences/classification.md -- the 16 document categoriesreferences/term-extraction-checklist.md -- contract terms to extract in Phase 2references/market-standards.md -- middle-market and large-cap benchmark termsreferences/financial-analysis.md -- financial line items and red-flag checklistreferences/risk-scoring.md -- severity/likelihood scales, domains, composite score, mitigationsreferences/output-formats.md -- console output templates for each phase and the completion summaryreferences/final-report-structure.md -- exact structure for deal-room-analysis.mdTake a directory path containing deal room documents (PDFs, .docx, .xlsx/.csv, text files, images). If no directory path is provided, ask for one before proceeding.
Run all five phases sequentially. Do not skip or combine phases. Each phase feeds the next. See references/phase-procedures.md for the full procedure of each phase and references/output-formats.md for the output template.
[PHASE N/5] Starting: [Phase Name] at the start of each phase.[deal-room-directory]/deal-room-analysis.md following references/final-report-structure.md, then print the completion summary.[filename, page X] or [filename, row X].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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