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)

```bash
# 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`.

---

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)