team-ops
Skill1 starsChanged 7 months agoMITView on GitHub
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. 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…
- Reads credentials
3 other projects use a version of this.
Adopted by
Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data
Most recently taken up by
- saar2u/team-opstheir own version, first seen Oct 9, 2026
- ericosiu/ai-marketing-skillsword for word, first seen Sep 2, 2026
- varunk130/ai-marketing-claude-skillstheir own version, first seen May 18, 2026
Other versions3 projects use a version of this.
Word for word · 1
- ericosiu/ai-marketing-skills★ 3.8k100% same
Edited · 0
None found.
Wrote their own version · 2
- saar2u/team-ops★ 1same name
- varunk130/ai-marketing-claude-skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
- eval/CLAUDE.mdCLAUDE.md
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The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 21
Use this skill when:
- Very specific: line 45
All scripts use environment variables for LLM API access.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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 || truePrivacy: 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. Seetelemetry/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) oropenaiLLM_MODEL— Model name override (default:claude-sonnet-4-20250514orgpt-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 PushDependencies
- Python 3.9+
anthropicoropenai(for LLM-powered analysis)requests(for optional HubSpot integration)
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
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Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
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