continuous-learning-v2
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
- License 许可
- Apache-2.0
- 最近更新
- 2026-11-08
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- elasticity-lab
- description
- Use to run pricing elasticity experiments with consistent assumptions and guardrails.
02 资源文件 11
- 脚本agents/observer-loop.sh10.5 KB
- 参考文档agents/observer.md7.3 KB
- 脚本agents/session-guardian.sh6.2 KB
- 脚本agents/start-observer.sh7.5 KB
- 参考文档config.json135 B
- 脚本hooks/observe.sh18.4 KB
- 脚本scripts/detect-project.sh10.2 KB
- 脚本scripts/instinct-cli.py71.1 KB
- 脚本scripts/lib/homunculus-dir.sh1009 B
- 脚本scripts/migrate-homunculus.sh1.7 KB
- 脚本scripts/test_parse_instinct.py33.5 KB
03 SKILL.md 正文
Elasticity Lab Skill
When to Use
- Modeling price increases/decreases across segments.
- Evaluating new pricing metrics (consumption, seats, feature gates).
- Stress testing monetization experiments before council reviews.
Framework
- Data Inputs – define baseline metrics (ASP, win rate, churn, attach rate) with data sources.
- Elasticity Curves – map expected response bands (conservative/moderate/aggressive).
- Scenario Builder – plug in price/metric changes and auto-calc ARR, margin, churn impact.
- Risk Flags – highlight cohorts sensitive to change (industry, tenure, product mix).
- Experiment Plan – propose pilot cohorts, measurement windows, and success criteria.
Templates
- Elasticity workbook (inputs, assumptions, scenarios, summary).
- Pilot plan checklist with KPI thresholds.
- Risk matrix by cohort.
Tips
- Keep historical data windows consistent to avoid noisy comparisons.
- Document assumptions + version history for auditability.
- Pair with
pricing-governanceto log council approvals.
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