| name | failure-memory |
| version | 1.5.0 |
| description | Stop making the same mistakes — turn failures into patterns that prevent recurrence |
| author | Live Neon <contact@liveneon.dev> |
| homepage | https://github.com/live-neon/skills/tree/main/agentic/failure-memory |
| repository | leegitw/failure-memory |
| license | MIT |
| tags | ["agentic","memory","learning","self-improving","error-tracking","observability","patterns","adaptive","feedback"] |
| layer | core |
| status | active |
| alias | fm |
| metadata | {"openclaw":{"requires":{"config":[".openclaw/failure-memory.yaml",".claude/failure-memory.yaml"],"workspace":[".learnings/",".learnings/observations/"]}}} |
failure-memory (記憶)
Unified skill for failure detection, observation recording, memory search, and pattern convergence.
Consolidates 10 granular skills into a single coherent memory system.
Trigger: 失敗発生 (failure occurred)
Source skills: failure-tracker, observation-recorder, memory-search, topic-tagger, failure-detector, evidence-tier, effectiveness-metrics, pattern-convergence-detector, positive-framer, contextual-injection
Installation
openclaw install leegitw/failure-memory
Dependencies: leegitw/context-verifier (for file change detection)
openclaw install leegitw/context-verifier
openclaw install leegitw/failure-memory
Standalone usage: This skill can function independently for basic failure tracking.
For full lifecycle management, install the complete suite (see Neon Agentic Suite).
Data handling: This skill operates within your agent's trust boundary. When triggered,
it uses your agent's configured model for failure detection and pattern recording. No external APIs
or third-party services are called. Results are written to .learnings/ in your workspace.
What This Solves
AI systems often make the same mistakes repeatedly — deleting working code, missing edge cases, forgetting context. This skill turns failures into learning by:
- Detecting failures when they happen (not after)
- Recording observations with R/C/D counters (Recurrence/Confirmations/Disconfirmations)
- Finding patterns within the workspace's
.learnings/ directory
- Promoting to constraints when evidence threshold is met
The insight: Systems learn better from consequences than instructions. A failure that happened teaches more than a rule that might apply.
Scope note: Pattern detection operates within the current workspace only. Observations
are stored in .learnings/ and searched locally. No cross-project data access occurs.
Usage
/fm <sub-command> [arguments]
Sub-Commands
