skill-adoption-planner

yizhiyanhua-ai/skill-adoption-planner

A planning layer for whether a skill should actually be adopted.

AI workflow libraries fail when everything gets added but nothing gets adopted. Adoption discipline is part of product quality.

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January 25, 2026

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yizhiyanhua-ai

MIT License

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Collecting skills without evaluating fit, payoff, or operational friction.

Prioritize which AI workflows deserve rollout in a real team.

Bring product and operations thinking into skill adoption.

Topics

AI-Native Workbenches Technical Distribution

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Skill Adoption Planner 🇨🇳 中文 Plan Skill rollout strategies within organizations, identify seed users, create phased plans, and prepare for resistance.

Analyze department characteristics and tech acceptance

Prioritize departments for rollout

Identify key roles (seed users, influencers, resistors)

Generate phased adoption plans

Skill Adoption PlannerFeaturesUsageAnalyze departmentsWith company size for headcount estimatesOutput as JSON

github.com/yizhiyanhua-ai/skill-adoption-planner

Skill Adoption Planner

🇨🇳 中文

Plan Skill rollout strategies within organizations, identify seed users, create phased plans, and prepare for resistance.

Features

Analyze department characteristics and tech acceptance
Prioritize departments for rollout
Identify key roles (seed users, influencers, resistors)
Generate phased adoption plans
Predict and address common resistance

Usage

# Analyze departments
python scripts/analyze_departments.py \
  --departments "技术,产品,销售,市场,财务,HR"

# With company size for headcount estimates
python scripts/analyze_departments.py \
  --departments "技术,产品,销售" \
  --total-size 200

# Output as JSON
python scripts/analyze_departments.py --json ...

Supported Departments

Department	Tech Acceptance	Best Strategy
Engineering	High	Seed department, emphasize efficiency
Product	High	Sync with engineering, collaboration focus
Sales	Medium	ROI-driven, ready-to-use Skills
Marketing	Medium	Creative assistance, content generation
Finance	Low	Accuracy focus, start with low-risk scenarios
HR	Low	Simple tools, emphasize assistance not replacement
Customer Service	Medium	High-frequency use cases, quick wins
Legal	Low	Expert involvement, human review required
Operations	Medium	Data-driven, show efficiency gains

License

MIT