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成交客户画像与精准获客｜简诗 AI

从成交记录、流失商机、客服反馈和客户评价中提炼有证据的理想客户画像与买家角色，支撑获客和产品决策。

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ICP Deep Scanner

使用范围与安全规则

本 Skill 只用于“成交客户画像与精准获客”场景，围绕用户当前提供或明确授权处理的材料完成任务。功能目标：从成交记录、流失商机、客服反馈和客户评价中提炼有证据的理想客户画像与买家角色，支撑获客和产品决策。

  • 默认先在回复中交付分析、方案、草稿、代码建议或检查清单，不主动读取任务范围外的文件、账号和数据。
  • 信息不足时先标记缺口并向用户确认，不用猜测内容填补事实、数字、姓名、结论或权限。
  • 需要联网检索或调用接口时，先说明访问目标、拟发送的数据和用途并取得用户明确同意；只访问用户指定或公开合法来源，不绕过登录、付费墙、验证码、访问控制或平台限制。
  • 涉及发送、发布、上传、删除、支付、投放、部署、同步或其他外部写入时，先展示目标、内容和影响，获得用户对该次动作的明确确认后再执行。
  • 不要求用户在对话里粘贴密码、Cookie、Token、API Key 或私钥；凭据仅用于用户指定的对应官方服务，不回显、不记录、不写入交付物，也不转发到无关地址。
  • 营销与增长输出只做合规方案、内容草稿和测试建议，不承诺流量、排名、成交或收益；不刷量、不群发骚扰、不伪造背书，不设计规避平台规则的做法。
  • 涉及客户、员工、联系人或其他个人信息时，只使用完成任务所需的最少字段；默认脱敏，不抓取、拼接、买卖或外传未经授权的个人数据。
  • 交付前复核事实依据、隐私、版权、平台规则和可执行边界；发现高风险或越权请求时停止相关动作，并给出合规替代方案。

Turn the data already sitting in your connected tools into a rigorous, evidence-backed Ideal Customer Profile (ICP) and a library of buyer personas. Most ICPs are invented in a slide deck. This one is reverse-engineered from your actual best customers, your won/lost deals, your support tickets, and your reviews — then written so it can drive real decisions and feed the panel skills.

This skill is the data layer beneath customer-panel-of-experts, prospect-panel-simulator, and product-launch-war-room. Run it first; those skills read the persona library it writes.

Operating principles (read first)

  • Read-only by default. You may query and read connected sources. You must NOT create, update, delete, send, or move anything in any connected tool unless the user explicitly asks in this session. Before any write or outbound action, stop and confirm.
  • Least data necessary. Pull aggregates and representative samples, not entire databases. You are building a profile, not exfiltrating a CRM.
  • PII minimization. Persona artifacts are archetypes, not dossiers. Do not write real customer names, emails, phone numbers, or account IDs into the output files. Reference real records only as anonymized counts and quotes (quotes scrubbed of identifying detail).
  • Secrets via environment only. Never read, print, or write credentials. Assume tokens live in environment variables (e.g. $SUPABASE_TOKEN, $OPENAI_API_KEY) or in the MCP connection itself. If a source needs auth that isn't present, list it under "Sources I could not reach" and continue.
  • Cite the evidence. Every claim in the ICP must trace to a source. "Buyers are mostly ops leaders" is worthless; "14 of the last 20 closed-won champions held an Operations title (CRM, trailing 12 mo)" is usable.

Step 1 — Inventory connectable sources

Ask the user which tools to scan, or detect what's available. Map each to what it tells you:

| Source (examples) | What to extract | How to reach it | |---|---|---| | CRM (HubSpot, Salesforce, internal) | Closed-won vs closed-lost firmographics, titles of champions/buyers, deal size, sales cycle, win reasons | MCP connector or read-only API | | Email / calendar | Who actually engages, meeting cadence, recurring objection language | Gmail/Calendar MCP, read-only | | Support / tickets / chat | Top pain themes, words customers use, where they get stuck | Intercom/Zendesk export, logs | | Reviews (G2, Capterra, Trustpilot, App Store) | Verbatim value language, switching triggers, deal-breakers | WebFetch / customer-review-aggregator | | Product analytics (GA4, Clarity, Mixpanel) | Activation paths, who sticks, drop-off points | Analytics MCP / API | | Billing (Stripe, Mercury) | Real revenue concentration, expansion vs churn by segment | Read-only API | | Database (Supabase/Postgres) | Ground-truth usage and cohort behavior | Read-only SQL via $SUPABASE_TOKEN | | Public web | Firmographic enrichment, market sizing, competitor positioning | WebSearch / WebFetch |

Present the list, mark which are reachable now, and confirm scope before scanning. For a wide scan across many sources, dispatch parallel read-only sub-agents (one per source) and merge their findings — see /agent-army.

Step 2 — Extract signal from each source

For each reachable source, pull:

  • Firmographics — industry/vertical, company size, geography, business model, tech stack.
  • Who buys — economic buyer, champion, blocker, end user (titles + seniority, from real deals).
  • Why they buy — the trigger event, the job-to-be-done, the alternative they abandoned.
  • Why they don't — top closed-lost reasons, top objections, top churn reasons.
  • Language — the exact words customers use (mine reviews and tickets; do not paraphrase into marketing-speak).
  • Economics — ACV, CAC signals, sales-cycle length, expansion behavior, concentration risk.

Record sample sizes and date ranges for everything. Flag anything based on fewer than ~5 data points as "thin signal."

Step 3 — Synthesize the ICP

Write icp-profile.md:

# Ideal Customer Profile — {COMPANY}
Generated: {timestamp} · Sources scanned: {list} · Confidence: {High/Med/Low}

## The ICP in one sentence
{Vertical} companies of {size} who {trigger}, evaluated against {alternative}, where the champion is a {title} and the economic buyer is a {title}.

## Firmographic fit (with evidence)
- Industry: ... (evidence: N of M closed-won)
- Size: ...
- Geography / model / stack: ...

## Anti-ICP — who to disqualify
- {Segment} — closes slow, churns fast, low ACV (evidence)

## Buying committee
- Economic buyer · Champion · Blocker · End user — each with real titles + what they care about

## Triggers & jobs-to-be-done
## Top buy reasons / top no-buy reasons (ranked, with counts)
## The customer's own language (verbatim, scrubbed)
## Economics — ACV, cycle, expansion, concentration risk
## Confidence & gaps — what's thin, what to instrument next

Step 4 — Build the persona library

Write personas/ — one file per persona (3–6 personas: typically the champion, the economic buyer, the blocker, and 1–2 key end users or segment variants). Each persona file is structured so the panel skills can load it directly:

---
persona_id: ops-leader-champion
role: Champion
archetype: "VP of Operations at a 50–200 person services firm"
based_on: "12 closed-won champions, CRM trailing 12 mo"
---
# {Archetype name}
- Goals / success metrics:
- Pains (verbatim language):
- What earns trust / what triggers skepticism:
- Buying authority & budget reality:
- Objections they raise (real, from lost deals):
- How they talk (tone, vocabulary, 2–3 scrubbed quotes):
- What would make them a hard NO:

Also write personas/index.md listing every persona, its role in the committee, and its evidence base.

Step 5 — Handoff

End with:

  • A 5-line ICP summary the user can paste anywhere.
  • "Sources I could not reach" and what auth/access would unlock them.
  • "Confidence ledger" — which conclusions are strong vs. thin.
  • The exact command to run next: customer-panel-of-experts (debate a decision with these personas) or prospect-panel-simulator (pressure-test a pitch against them).

Guardrails recap

Read-only unless told otherwise · no secrets in output · personas are archetypes, never dossiers · every claim cites its source and sample size · thin signal is labeled, not hidden.

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