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This adds the full Atlas (总助 Claw / 老板视角项目执行雷达) scaffolding as a sibling profile to the existing Vega general-purpose assistant. All Atlas content lives under atlas/ to keep the existing top-level skeleton intact. What's included: - atlas/IDENTITY.md, SOUL.md, USER.md, AGENTS.md, MEMORY.md, BOOTSTRAP.md, HEARTBEAT.md, TOOLS.md (+ zh-CN mirrors) — full OpenClaw 8-piece set matching the zero-cca convention - atlas/skills/ — 6 sub-skills with frontmatter: claw-email-parser / claw-project-tracker / claw-people-observer / claw-customer-radar / claw-boss-distiller / claw-report-writer - atlas/skills/claw-boss-distiller/ — adapter notes for nuwa-skill, 5-layer boss_skill seed template (23 rules across Expression DNA / Mental Models / Decision Heuristics / Anti-Patterns / Honest Boundaries), and a complete synthetic distillation demo (10 input emails -> validated 5-layer output) - atlas/mcp-tools/email-extractor/ — Python implementation of stages 1-3 (fetch + decode + dequote), 7 pytest tests passing, CLI: atlas-extract - atlas/state-schemas/ — formal JSON schemas for project / person / customer cards with the no-employee-rating hard constraint baked in - atlas/client-deck/ — 2-page client-facing pitch document - autopilots/atlas-*.yaml — 5 autopilot configs (daily / weekly / monthly / quarterly + andon event-triggered) for a future Multica-side scheduler Notes: - nuwa-skill (MIT, https://github.com/alchaincyf/nuwa-skill) NOT vendored; fetch at deploy time via instructions in atlas/skills/claw-boss-distiller/upstream/README.md - Vega-side prompts/skills/tools/autopilots/docs scaffold left untouched - Top-level README.md updated with a brief Atlas pointer; rest preserved
64 lines
2.6 KiB
Markdown
64 lines
2.6 KiB
Markdown
# claw-boss-distiller — Demo Run
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A synthetic distillation, end-to-end, on a fictional 中国 SME 老板 "王总". Use this to:
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1. Show clients what `boss_skill.md` actually looks like before they commit
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2. Validate the pipeline shape end-to-end before real client data arrives
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3. Smoke-test changes to `claw-boss-distiller` (regression baseline)
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## Scenario
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- **Boss:** 王总, founder + CEO of 一家约 80 人的企业服务公司 (toB SaaS), based in 上海
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- **Span:** ~25 active projects, ~12 active customers, internal team of ~15 PMs/engineers/sales
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- **Email volume:** ~40 incoming/day, ~20 outgoing/day
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- **Demo input:** 10 boss-outgoing emails over a 14-day window (compressed sample; real distillation reads 6 months ≈ 2400+ emails)
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- **Languages:** mostly 中文, occasional English term
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## Pipeline (matches `SKILL.md` and `ADAPTER.md`)
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```
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INPUT/wang-emails-*.txt (10 mock outgoing emails)
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↓
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[Bucket sort] classify each email into B1–B6
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↓
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[Triple verify] patterns must clear cross-domain + generative + exclusive
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↓
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[Synthesize 5 layers] Expression DNA / Mental Models / Decision Heuristics / Anti-Patterns / Honest Boundaries
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↓
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OUTPUT/boss_skill.demo.md (status: draft; awaits boss audit)
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OUTPUT/run-summary.md (counts + verification trace)
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```
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## Files
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```
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demo/
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├── README.md # this file
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├── INPUT/
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│ ├── 01-催办-zhangsan-PRJ001.txt
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│ ├── 02-决策-supplier-pick.txt
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│ ├── 03-表扬-wang-onboarding.txt
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│ ├── 04-否决-feature-request.txt
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│ ├── 05-转交-li-customer-D.txt
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│ ├── 06-mid-thread-intervention.txt
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│ ├── 07-pressure-customer-A.txt
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│ ├── 08-closing-PRJ022.txt
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│ ├── 09-short-reply-FYI.txt
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│ └── 10-long-reasoning-Q3-strategy.txt
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└── OUTPUT/
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├── boss_skill.demo.md # the produced 5-layer document
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└── run-summary.md # the distillation log
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```
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## How to read the demo
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1. Read 2–3 INPUT emails to get a feel for 王总's voice
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2. Read OUTPUT/boss_skill.demo.md and notice how each rule cites which input emails
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3. Read OUTPUT/run-summary.md to see which candidate patterns were promoted vs filtered out by triple verification
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## Caveats
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- 10 emails is far below the real W1 corpus size. Real runs produce richer Expression DNA and more decision heuristics.
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- 王总 is fictional; any resemblance to real bosses is coincidental.
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- The demo deliberately includes one ambiguous case (email #4) to show how the pipeline marks `low_confidence`.
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