team-ops

---

name: team-ops description: "Audit team performance using the Elon Algorithm (identify A/B/C players) and extract action items from meeting transcripts. Use when asked to "audit team performance", "review my team", "extract meeting actions", "meeting notes to tasks", "team efficiency audit", or "identify underperformers"." description_zh: "团队绩效审计与会议行动项提取，识别 A/B/C 员工" description_en: "Team performance audit and meeting action extraction, identify A/B/C players" version: 1.0.0 homepage: https://github.com/ericosiu/ai-marketing-skills

AI Team Ops

Preamble (runs on skill start)

# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true

# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true

Privacy: This skill logs usage locally to ~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See telemetry/README.md.

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AI-powered team performance analysis and meeting intelligence: ruthless performance audits using the "Elon Algorithm" + automatic extraction of action items, decisions, and follow-ups from meeting transcripts.

When to Use

Use this skill when:

• Evaluating team performance against OKRs/KPIs with a structured framework
• Stack ranking team members to identify A/B/C players
• Finding redundant roles, bottlenecks, and automation opportunities in your org
• Extracting action items and decisions from meeting transcripts
• Processing batch meeting notes into structured follow-up lists
• Pushing meeting action items to CRM (HubSpot) as tasks

Tools

Team Performance

Script	Purpose	Key Command
team_performance_audit.py	Elon Algorithm: 5-step team audit + stack rank + scorecards	python3 team_performance_audit.py --input team_data.json --output report.md

Meeting Intelligence

Script	Purpose	Key Command
meeting_action_extractor.py	Extract decisions, actions, follow-ups from transcripts	python3 meeting_action_extractor.py --transcript meeting.txt --format markdown

Configuration

All scripts use environment variables for LLM API access. Copy .env.example to .env and fill in your values.

Required Environment Variables

• ANTHROPIC_API_KEY — Anthropic API key (Claude for analysis)
• OPENAI_API_KEY — OpenAI API key (alternative LLM provider)

Optional Environment Variables

• HUBSPOT_API_KEY — HubSpot private app token (for pushing meeting action items as tasks)
• LLM_PROVIDER — anthropic (default) or openai
• LLM_MODEL — Model name override (default: claude-sonnet-4-20250514 or gpt-4o)

Data Flow

Role Descriptions + OKRs + Output Data (CSV/JSON)
        │
        ▼
┌──────────────────────────────────┐
│   team_performance_audit.py      │
│   5-Step Elon Algorithm:         │
│   1. Question requirements       │
│   2. Delete redundancies         │
│   3. Simplify workflows          │
│   4. Accelerate bottlenecks      │
│   5. Automate what's possible    │
│                                  │
│   + Score: velocity, quality,    │
│     independence, initiative     │
│   + Stack rank: A/B/C players    │
│   + Actions: promote/coach/exit  │
└──────────────────────────────────┘
        │
        ▼
Executive Summary + Individual Scorecards + Org Recommendations


Meeting Transcripts (text files or stdin)
        │
        ▼
┌──────────────────────────────────┐
│   meeting_action_extractor.py    │
│   Extract:                       │
│   • Decisions (who + context)    │
│   • Action items (owner +        │
│     deadline + priority)         │
│   • Open questions               │
│   • Key insights / quotes        │
│   • Follow-up meetings needed    │
│   • Implicit commitments         │
│   + Confidence scores            │
└──────────────────────────────────┘
        │
        ▼
Structured JSON / Markdown + Optional CRM Push

Dependencies

• Python 3.9+
• anthropic or openai (for LLM-powered analysis)
• requests (for optional HubSpot integration)