name: growth-engine description: "Run autonomous marketing experiments with pacing alerts and weekly scorecards. Generates experiment hypotheses, tracks metrics, sends alerts when pacing is off, and produces weekly growth reports. Use when asked to "run growth experiments", "set up pacing alerts", "growth scorecard", "marketing experiment", or "track experiment metrics"." description_zh: "自主营销实验引擎，自动运行、度量与优化增长实验" description_en: "Autonomous marketing experiment engine that runs, measures, and optimizes growth" version: 1.0.0 homepage: https://github.com/ericosiu/ai-marketing-skills

Growth Engine

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.

Autonomous growth experimentation framework based on Karpathy's autoresearch pattern applied to marketing. Creates experiments with hypotheses, logs data points, runs statistical analysis (bootstrap CI + Mann-Whitney U), auto-promotes winners to a living playbook, and suggests next experiments. Supports batch mode (up to 10 variants simultaneously).

Usage

Use this skill when:

• Creating or managing A/B or multivariate experiments for any marketing channel
• Logging experiment data points after content is published or campaigns run
• Scoring experiments to determine statistical winners
• Checking the playbook for proven best practices before creating new content
• Generating weekly scorecards across all channels
• Monitoring campaign pacing and health

Do NOT use for:

• One-off content creation (use the playbook output as input, but don't run the engine)
• Non-experiment analytics or reporting
• Campaign setup in external platforms (this tracks experiments, not campaign config)

Commands

Create an experiment

python3 experiment-engine.py create \
  --agent <agent_name> \
  --hypothesis "What you expect to happen" \
  --variable "<variable_name>" \
  --variants '["variant_a", "variant_b"]' \
  --metric "<primary_metric>" \
  --cycle-hours 24

Add --batch-mode for 3-10 variant tests. Add --min-samples N to override auto-detection.

Log a data point

python3 experiment-engine.py log \
  --agent <agent_name> \
  --experiment-id <EXP-ID> \
  --variant "<variant_name>" \
  --metrics '{"metric_name": value}'

Score an experiment

python3 experiment-engine.py score --agent <agent_name> --experiment-id <EXP-ID>

Statuses: running → trending → keep (winner) or discard (loser)

Winners auto-promote to the playbook. Requires p < 0.05 AND ≥ 15% lift.

List experiments

python3 experiment-engine.py list --agent <agent_name> [--status running|trending|keep|discard]

Check the playbook

python3 experiment-engine.py playbook --agent <agent_name>

Always check the playbook before creating new content to apply proven best practices.

Suggest next experiments

python3 experiment-engine.py suggest --agent <agent_name>

Generate weekly scorecard

python3 autogrowth-weekly-scorecard.py [--weeks N] [--output file.md]

Check campaign pacing

python3 pacing-alert.py [--json]

Exit code 0 = on pace, 1 = alerts present.

Workflow

1. Before creating content: playbook → apply proven rules
2. When publishing: log → record which variant was used and its metrics
3. Periodically: score → check if experiments have reached statistical significance
4. Weekly: autogrowth-weekly-scorecard.py → review all channels
5. After completing experiments: suggest → pick the next variable to test

Configuration

Required Environment Variables

Optional Tuning

Pacing Alert Variables

Dependencies

pip install numpy scipy