assistant-claw/skills/claw-boss-distiller/demo

claw-boss-distiller — Demo Run

A synthetic distillation, end-to-end, on a fictional 中国 SME 老板 "王总". Use this to:

1. Show clients what boss_skill.md actually looks like before they commit
2. Validate the pipeline shape end-to-end before real client data arrives
3. Smoke-test changes to claw-boss-distiller (regression baseline)

Scenario

• Boss: 王总, founder + CEO of 一家约 80 人的企业服务公司 (toB SaaS), based in 上海
• Span: ~25 active projects, ~12 active customers, internal team of ~15 PMs/engineers/sales
• Email volume: ~40 incoming/day, ~20 outgoing/day
• Demo input: 10 boss-outgoing emails over a 14-day window (compressed sample; real distillation reads 6 months ≈ 2400+ emails)
• Languages: mostly 中文, occasional English term

Pipeline (matches SKILL.md and ADAPTER.md)

INPUT/wang-emails-*.txt   (10 mock outgoing emails)
        ↓
[Bucket sort]   classify each email into B1–B6
        ↓
[Triple verify]   patterns must clear cross-domain + generative + exclusive
        ↓
[Synthesize 5 layers]   Expression DNA / Mental Models / Decision Heuristics / Anti-Patterns / Honest Boundaries
        ↓
OUTPUT/boss_skill.demo.md  (status: draft; awaits boss audit)
OUTPUT/run-summary.md      (counts + verification trace)

Files

demo/
├── README.md                           # this file
├── INPUT/
│   ├── 01-催办-zhangsan-PRJ001.txt
│   ├── 02-决策-supplier-pick.txt
│   ├── 03-表扬-wang-onboarding.txt
│   ├── 04-否决-feature-request.txt
│   ├── 05-转交-li-customer-D.txt
│   ├── 06-mid-thread-intervention.txt
│   ├── 07-pressure-customer-A.txt
│   ├── 08-closing-PRJ022.txt
│   ├── 09-short-reply-FYI.txt
│   └── 10-long-reasoning-Q3-strategy.txt
└── OUTPUT/
    ├── boss_skill.demo.md              # the produced 5-layer document
    └── run-summary.md                  # the distillation log

How to read the demo

1. Read 2–3 INPUT emails to get a feel for 王总's voice
2. Read OUTPUT/boss_skill.demo.md and notice how each rule cites which input emails
3. Read OUTPUT/run-summary.md to see which candidate patterns were promoted vs filtered out by triple verification

Caveats

• 10 emails is far below the real W1 corpus size. Real runs produce richer Expression DNA and more decision heuristics.
• 王总 is fictional; any resemblance to real bosses is coincidental.
• The demo deliberately includes one ambiguous case (email #4) to show how the pipeline marks low_confidence.