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64,955 skills found

office-hours

1

/cs:office-hours <topic> — YC-style 6-question founder interrogation before any advice. Forces clarity on problem, customer, distribution, defensibility, capital, and founder fit.

Design e Frontend#llm#mcpby timdevai

onboard

1

/cs:onboard — Founder interview that populates ~/.claude/company-context.md. The first command to run when starting with c-level-agents.

Design e Frontend#llm#mcpby timdevai

post-mortem

1

/cs:post-mortem <decision> — Honest retrospective on an executed decision, scored against original assumptions and dissent. Closes the strategic sprint loop.

Design e Frontend#llm#mcpby timdevai

vpe-review

1

/cs:vpe-review <plan> — Throughput-first VP of Engineering interrogation of any plan that touches delivery, eng hiring, team structure, or production discipline.

Design e Frontend#llm#mcpby timdevai

chief-ai-officer-advisor

1

Chief AI Officer advisory for startups: model build-vs-buy decisions (API vs fine-tune vs in-house), AI risk classification under EU AI Act + US state patchwork, AI cost economics (API-to-self-hosted breakeven), and AI team org evolution. Use when deciding whether to call an API or fine-tune, classifying AI use cases for regulatory risk, calculating when self-hosting pays off, sequencing AI hires,

Design e Frontend#llm#mcpby timdevai

chief-customer-officer-advisor

1

Chief Customer Officer advisory for startups: retention decomposition (gross retention vs NRR honesty, churn root-cause taxonomy), customer segmentation strategy (differential investment across tiers + ICP fit scoring), CS team coverage model (pooled vs named CSM thresholds + ratio math), and CS team org evolution (CS vs Support vs AM distinctions). Use when designing retention strategy, segmentin

Design e Frontend#llm#mcpby timdevai

chief-data-officer-advisor

1

Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when use

Design e Frontend#llm#mcpby timdevai

board-prep

1

Board meeting preparation for the adversarial scenario, not the friendly one. Forces numbers-cold mastery, anticipates hard questions, builds a narrative that acknowledges weakness without losing the room. Use when preparing for a board meeting, an investor update, fundraising presentation, or any high-stakes adversarial review where every number must live in your head not just on a slide.

Design e Frontend#llm#mcpby timdevai

challenge

1

Pre-mortem plan analysis. Imagine the plan failed 12 months from now and work backwards to find the weaknesses. Surfaces assumptions, dependencies, and execution risks before committing resources. Use when before significant resource commitment, before presenting to a board or investors, when feedback has been one-sidedly positive, or when there is pressure to move fast and figure it out later.

Design e Frontend#llm#mcpby timdevai

executive-mentor

1

Adversarial thinking partner for founders and executives. Stress-tests plans, prepares for brutal board meetings, dissects decisions with no good options, and forces honest post-mortems. Use when you need someone to find the holes before the board does, make a decision you've been avoiding, or understand what actually went wrong.

Desenvolvimento#llm#mcpby timdevai

hard-call

1

/em -hard-call — Framework for Decisions With No Good Options

Design e Frontend#llm#mcpby timdevai

stress-test

1

/em -stress-test — Business Assumption Stress Testing

Desenvolvimento#llm#mcpby timdevai