bd0be97630
This repo IS Atlas (总助 Claw / 老板视角项目执行雷达). The earlier
two-profile framing (Atlas + Vega placeholder) was a misread — Vega is
the agent persona answering Multica issues, not the product. Vega has
no relationship to assistant-claw the product.
Changes:
- Move atlas/* to top-level (git mv preserves history)
- Remove empty Vega placeholders prompts/.gitkeep, tools/.gitkeep
- Delete atlas/ wrapper directory (now empty)
- Update path references in INTEGRATION-hermes.md, scripts/mirror-...sh,
docs/decisions/0001-mirror-nuwa-skill.md
- Rewrite README.md as Atlas-only, remove dual-profile language
After this commit:
- Top-level OpenClaw 8 files (IDENTITY/SOUL/USER/AGENTS/TOOLS/MEMORY/
BOOTSTRAP/HEARTBEAT + CLAUDE symlink + zh-CN mirrors)
- skills/{6 sub-skills + DESCRIPTION + README}
- mcp-tools/{spec + Python implementation}
- state-schemas/{project, person, customer + README}
- autopilots/{5 atlas-*.yaml}
- client-deck/, docs/decisions/, scripts/
The ~/.hermes/skills/atlas/ destination convention preserved (atlas as
a skill namespace on the operator's machine, distinct from source path).
132 lines
4.3 KiB
Markdown
132 lines
4.3 KiB
Markdown
---
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name: claw-customer-radar
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description: Compute Customer Health Score, sentiment trend, RFM, decision-chain map, churn signals. Fire Andon alerts when health crosses thresholds.
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version: 0.1.0
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author: Moments / Atlas team
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license: MIT
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metadata:
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hermes:
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category: atlas
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tags: [customer-success, chs, voc, churn, rfm, andon, atlas]
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related_skills: [claw-email-parser, claw-project-tracker, claw-report-writer]
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boundaries:
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- cross_customer_data_isolation
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---
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# claw-customer-radar
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## Purpose
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Atlas's third lens (after project and people). Tracks each external customer as an entity with health, sentiment trajectory, and early-warning signals.
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## Inputs
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- Email JSON (focus on external sender domains)
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- `state/projects/*.json` (for project-health input to CHS)
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- `state/customers/*.json` (existing customer cards)
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## Outputs
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- `state/customers/CUST-*.json`
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- Andon alert JSON to `state/runs/alerts.json` when health crosses thresholds
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- New `state/customers/UNCLASSIFIED-<domain>.json` for unknown customer domains
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## Customer Health Score (CHS)
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```
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CHS = 0.4 × project_health
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+ 0.3 × sentiment_trend_30d
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+ 0.2 × email_frequency_normalcy
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+ 0.1 × monetary_scale
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Tier:
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≥ 80 → 🟢 Green
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60 – 79 → 🟡 Yellow
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< 60 → 🔴 Red
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```
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### project_health
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- Per associated project: Active=1.0, Stalled=0.3, Completed (recent)=1.0, Dropped=0
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- Weighted average across that customer's projects
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### sentiment_trend_30d
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- VoC: extract customer-side quotes from Email JSON
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- Sentiment scoring: per-quote score in [-1, +1] via LLM with calibrated rubric
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- Trend = slope of last 30 days; convert to [0, 1] with `0.5 + slope/2`
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### email_frequency_normalcy
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- Compare last-30-day frequency to trailing-90-day baseline
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- 0.5 ± deviation; ≤ 0.3 means significant drop (silence is a churn signal)
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### monetary_scale
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- Sum of associated project amounts, normalized against this client's customer book
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- Big customers tilt the score so a Yellow on a whale beats a Yellow on a small one
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## Sentiment Trend (separate field)
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Stored as a 90-day series for chart rendering:
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```json
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"情绪趋势_90d": [
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{"date": "2026-02", "score": 0.7, "代表词": "期待 / 配合"},
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{"date": "2026-03", "score": 0.5, "代表词": "等候 / 询问"},
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{"date": "2026-04", "score": 0.2, "代表词": "催 / 失望"}
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]
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```
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## VoC Extraction Rules
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Surface up to 5 most-impactful customer quotes per 30-day window:
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- Strong negative (`再这样我就找别家`, `你们到底什么时候能...`)
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- Strong positive (`做得不错`, `非常感谢`)
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- Decision-maker direct intervention (boss-level customer person speaks up)
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- Threats / explicit deadlines
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Each quote keeps source email ID and speaker identity.
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## Decision Chain Map
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For each customer, identify 2–5 key external people and their role:
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| Role | Heuristic |
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|------|-----------|
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| `决策者` | most senior title in signature; or person whose intervention shifts thread |
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| `执行接口` | most frequent external sender on operational topics |
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| `用户` | recipient of deliverables, may be silent |
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| `阻拦者` | person who frequently raises objections |
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## Churn Signals
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Maintain a list of signals; populate when triggered:
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| Signal | Trigger |
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|--------|---------|
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| `决策者亲自施压` | Decision-maker speaks up after silence |
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| `频率骤降` | 30d frequency < 30% of 90d baseline |
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| `合同到期临近` | Within 60 days of known contract end (boss-provided) |
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| `负面 VoC 累积` | ≥ 3 strong-negative quotes in 30 days |
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| `项目同时 stall` | ≥ 2 projects with this customer in Stalled simultaneously |
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| `关键人离职` | Detected via OOO message + replacement signature |
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## Andon Alert Triggers (fire to `state/runs/alerts.json`)
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- Tier transition Green→Yellow or Yellow→Red
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- Strong-negative VoC quote
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- ≥ 2 active churn signals simultaneously
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Each alert includes: customer_id, trigger, source email IDs, recommended boss action, suppression-key (don't re-fire same alert within 24h of acknowledgment).
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## Sample Output
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See `state-schemas/customer.md` for full example. Key fields produced:
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- `id`, `name`, `lifecycle_stage`
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- `健康度` { score, 档位, 构成 }
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- `情绪趋势_90d`
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- `RFM` { Recency, Frequency, Monetary }
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- `我方接口人` (cross-ref to people)
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- `客户方决策链`
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- `VoC_关键原话_30d`
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- `流失预警信号`
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- `老板视角建议`
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