assistant-claw/atlas/skills/claw-customer-radar/SKILL.md

name	description	version	author	license	metadata
claw-customer-radar	Compute Customer Health Score, sentiment trend, RFM, decision-chain map, churn signals. Fire Andon alerts when health crosses thresholds.	0.1.0	Moments / Atlas team	MIT	hermes category tags related_skills boundaries atlas customer-success chs voc churn rfm andon atlas claw-email-parser claw-project-tracker claw-report-writer cross_customer_data_isolation

claw-customer-radar

Purpose

Atlas's third lens (after project and people). Tracks each external customer as an entity with health, sentiment trajectory, and early-warning signals.

Inputs

• Email JSON (focus on external sender domains)
• state/projects/*.json (for project-health input to CHS)
• state/customers/*.json (existing customer cards)

Outputs

• state/customers/CUST-*.json
• Andon alert JSON to state/runs/alerts.json when health crosses thresholds
• New state/customers/UNCLASSIFIED-<domain>.json for unknown customer domains

Customer Health Score (CHS)

CHS = 0.4 × project_health
    + 0.3 × sentiment_trend_30d
    + 0.2 × email_frequency_normalcy
    + 0.1 × monetary_scale

Tier:
  ≥ 80 → 🟢 Green
  60 – 79 → 🟡 Yellow
  < 60 → 🔴 Red

project_health

• Per associated project: Active=1.0, Stalled=0.3, Completed (recent)=1.0, Dropped=0
• Weighted average across that customer's projects

sentiment_trend_30d

• VoC: extract customer-side quotes from Email JSON
• Sentiment scoring: per-quote score in [-1, +1] via LLM with calibrated rubric
• Trend = slope of last 30 days; convert to [0, 1] with 0.5 + slope/2

email_frequency_normalcy

• Compare last-30-day frequency to trailing-90-day baseline
• 0.5 ± deviation; ≤ 0.3 means significant drop (silence is a churn signal)

monetary_scale

• Sum of associated project amounts, normalized against this client's customer book
• Big customers tilt the score so a Yellow on a whale beats a Yellow on a small one

Sentiment Trend (separate field)

Stored as a 90-day series for chart rendering:

"情绪趋势_90d": [
  {"date": "2026-02", "score": 0.7, "代表词": "期待 / 配合"},
  {"date": "2026-03", "score": 0.5, "代表词": "等候 / 询问"},
  {"date": "2026-04", "score": 0.2, "代表词": "催 / 失望"}
]

VoC Extraction Rules

Surface up to 5 most-impactful customer quotes per 30-day window:

• Strong negative (再这样我就找别家, 你们到底什么时候能...)
• Strong positive (做得不错, 非常感谢)
• Decision-maker direct intervention (boss-level customer person speaks up)
• Threats / explicit deadlines

Each quote keeps source email ID and speaker identity.

Decision Chain Map

For each customer, identify 2–5 key external people and their role:

Role	Heuristic
决策者	most senior title in signature; or person whose intervention shifts thread
执行接口	most frequent external sender on operational topics
用户	recipient of deliverables, may be silent
阻拦者	person who frequently raises objections

Churn Signals

Maintain a list of signals; populate when triggered:

Signal	Trigger
决策者亲自施压	Decision-maker speaks up after silence
频率骤降	30d frequency < 30% of 90d baseline
合同到期临近	Within 60 days of known contract end (boss-provided)
负面 VoC 累积	≥ 3 strong-negative quotes in 30 days
项目同时 stall	≥ 2 projects with this customer in Stalled simultaneously
关键人离职	Detected via OOO message + replacement signature

Andon Alert Triggers (fire to state/runs/alerts.json)

• Tier transition Green→Yellow or Yellow→Red
• Strong-negative VoC quote
• ≥ 2 active churn signals simultaneously

Each alert includes: customer_id, trigger, source email IDs, recommended boss action, suppression-key (don't re-fire same alert within 24h of acknowledgment).

Sample Output

See state-schemas/customer.md for full example. Key fields produced:

• id, name, lifecycle_stage
• 健康度 { score, 档位, 构成 }
• 情绪趋势_90d
• RFM { Recency, Frequency, Monetary }
• 我方接口人 (cross-ref to people)
• 客户方决策链
• VoC_关键原话_30d
• 流失预警信号
• 老板视角建议