Collect company facts through a structured 12-question founder interview and save them in a shared context file for AI executive advisors. Capture business, financial, team, and strategic information when setting up advisors or updating company circumstances.
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/cs-onboard — Founder interview that populates ~/.claude/company-context.md using the canonical 7-dimension cs-onboard schema. The first command to run when starting with c-level-agents. Use when setting up the virtual C-suite for a new company, or when advisors lack company context — e.g. before a first /c-level-agents:boardroom or after a fundraise changes the numbers.
安装前可以查看交给 AI 代理的指令内容。
Command: /cs-onboard
The first command to run when adopting c-level-agents. A structured founder interview that produces ~/.claude/company-context.md — the file every cs-* advisor reads before responding. Without this, the advisors are guessing.
~/.claude/company-context.md — a single file with the durable facts about the company. Read by:
cs-chief-of-staff (routing decisions)/c-level-agents:brief (assumptions in any new decision)Canonical schema: ~/.claude/company-context.md is owned by the cs-onboard skill and follows its 7-dimension schema (${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/cs-onboard/templates/company-context-template.md): Company Identity, Stage & Scale, Founder Profile, Team & Culture, Market & Competition, Current Challenges, Goals & Ambition. The 12 questions above are a faster structured intake that populates that same file — Identity/Business/Financial → Stage & Scale, Team → Team & Culture, Quarter priorities/risks → Current Challenges + Goals & Ambition. Write [not captured] for dimensions the quick intake doesn't reach (Founder Profile, Market & Competition); run the full cs-onboard interview to fill them. Never create a second context file or a divergent layout.
The intake summary captured by the 12 questions:
# Company Context
**Generated:** YYYY-MM-DD
**Last updated:** YYYY-MM-DD
## Identity
- **Company:** <name>
- **Pitch:** <one sentence>
- **Stage:** <stage>
- **HQ + remote:** <distribution>
## Business
- **Model:** <type>
- **ICP:** <description + named customer>
- **ACV:** $<median> (range $<low> - $<high>)
- **Deal count (LTM):** N
- **ARR growth (YoY):** X%
## Financial
- **Cash on hand:** $<amount>
- **Net burn (monthly):** $<amount>
- **Runway base:** N months
- **Runway bear:** N months
- **Last raise:** $<amount> at $<post> in <month YYYY>, led by <investor>
## Team
- **Total headcount:** N
- **Eng:** N | Product: N | GTM: N | Ops: N | G&A: N
## Quarter
- **Top priorities (Q<X> YYYY):**
1. <priority>
2. <priority>
3. <priority>
- **Top risks:**
1. <risk>
2. <risk>
3. <risk>
## Routing Hints
[Optional: any role the founder wants to use sparingly or rely on heavily]
~/.claude/company-context.mdln -sf ~/company-vault/00-meta/company-context.md ~/.claude/company-context.md
/c-level-agents:boardroom for a high-stakes decisionBy default, ~/.claude/company-context.md is local to the founder's machine. To make it persistent across machines / shareable:
../../references/llm-wiki-bridge.md~/.claude/cs-onboard — the underlying interview protocolcontext-engine — reads this file../../references/llm-wiki-bridge.mdVersion: 1.0.0
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Audit web applications for accessibility issues against WCAG 2.2 Level A and AA, the web accessibility standards. Get framework-specific code fixes, contrast checks, verification steps, and reports, with options for automated checks in development pipelines.
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Plan A/B tests that compare page variations, from headlines to pricing layouts. Define a hypothesis, choose success metrics, calculate sample size and duration, and prepare launch checks and results reports to support decisions.
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Create paid ad headlines and body copy tailored to each platform's character limits. Use existing performance data to develop variations, explore new messaging angles, and check copy for length, quality, and potential rejection risks.
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Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
中文简介正在准备中。当前显示英语简介。
Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.
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Create signed records for consequential agent actions such as deployments, deletions, and payments. Decide which actions need evidence, record the agent, action, target, and policy, then verify the resulting certificate offline to detect later changes.
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