friction-detector

# Friction-to-Learning Pipeline ## Overview Detect friction signals during agent execution, track them across sessions, and graduate recurring patterns into permanent guidance. Bridges the gap between ephemeral session friction and durable CLAUDE.md rules. **Research backing**: Claude Coach (hook-based friction detection with SQLite storage), alirezarezvani's self-improving-agent (three-tier MEMORY to CLAUDE.md graduation), and the ACE framework (arXiv: evolving playbooks from execution feedback, +10.6% on agent tasks). **Current gap**: LEARNINGS.md exists but requires manual aggregation via `/abstract:aggregate-logs`. This skill adds automatic friction detection and a structured promotion path. ## Friction Signal Types | Signal | Detection Method | Weight | |--------|-----------------|--------| | Repeated corrections | User overrides same tool call 2+ times in session | High | | Command failures | Exit code != 0 patterns (same command type fails repeatedly) | Medium | | Permission denials | User denies tool call, indicating unexpected behavior | High | | Re-reads | Same file read 3+ times in session (lost context) | Low | | Retry loops | Same action attempted 3+ times with variations | Medium | | User frustration | Explicit negative feedback or correction language | High | Weight scoring: High = 3, Medium = 2, Low = 1 points per occurrence. Weighted score determines graduation velocity. ## Three-Tier Storage Graduation ``` Tier 1: Friction Log (ephemeral, per-sessio...