failure-memory（故障记忆）

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)

# Install with dependencies
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:

1. Detecting failures when they happen (not after)
2. Recording observations with R/C/D counters (Recurrence/Confirmations/Disconfirmations)
3. Finding patterns within the workspace's .learnings/ directory
4. 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

Arguments

/fm detect

/fm record

/fm search

/fm classify

/fm status

/fm refactor

/fm converge

Detection Triggers

These patterns indicate when /fm detect should be invoked (user or orchestrator triggers):

Example: API Failure Detection

[DETECTED] api failure
Pattern: payment-api-timeout
Context: Payment API returned 504 after 30s
Observation: OBS-20260215-002
R: 1 → 3
Status: Eligible for constraint (R≥3)

Example: Deployment Failure Detection

[DETECTED] deployment failure
Pattern: staging-healthcheck-fail
Context: Staging deployment failed health check on /api/health
Observation: OBS-20260215-003
R: 1 → 2
Status: Monitoring (R<3)

Core Logic

R/C/D Counters

Evidence Tiers

Slug Taxonomy

Observations are tagged with slugs: git-*, test-*, workflow-*, security-*, docs-*, quality-*

Metrics

• prevention_rate: Failures prevented / Total potential failures
• false_positive_rate: D / (C + D)

Output

/fm detect output

[DETECTED] test failure
Pattern: lint-before-commit
Observation: OBS-20260215-001
R: 1 → 2
Status: Monitoring (R<3)

/fm status output

=== Failure Memory Status ===

Eligible for constraint (R≥3 ∧ C≥2):
- OBS-20260210-003: lint-before-commit (R=4, C=2, D=0)
- OBS-20260212-007: test-before-push (R=3, C=3, D=1)

Recent (last 30d): 12 observations
Pending review: 3 observations

Configuration

Configuration is loaded from (in order of precedence):

1. .openclaw/failure-memory.yaml (OpenClaw standard)
2. .claude/failure-memory.yaml (Claude Code compatibility)
3. Defaults (built-in)

# .openclaw/failure-memory.yaml
detection:
  auto_detect: true          # Enable automatic failure detection
  patterns:                   # Custom detection patterns
    - "FATAL:"
    - "CRITICAL:"
thresholds:
  eligibility_R: 3           # Recurrence threshold (default: 3)
  eligibility_C: 2           # Confirmation threshold (default: 2)
  false_positive_max: 0.2    # Max D/(C+D) ratio (default: 0.2)

Integration

• Layer: Core
• Depends on: context-verifier (for file change detection)
• Used by: constraint-engine (for eligibility checks), governance (for state queries)

Failure Modes

Next Steps

After invoking this skill:

Workspace Files

This skill reads/writes:

.learnings/
├── ERRORS.md        # [ERR-YYYYMMDD-XXX] command failures
├── LEARNINGS.md     # [LRN-YYYYMMDD-XXX] corrections, best practices
└── observations/    # Individual observation files
    └── OBS-YYYYMMDD-XXX.md

Security Considerations

What this skill accesses:

• Configuration files in .openclaw/failure-memory.yaml and .claude/failure-memory.yaml
• Tool output and user messages in the current session (for failure detection)
• Its own workspace directory .learnings/ (read/write)

What this skill does NOT access:

• Files outside declared workspace paths
• System environment variables
• Other projects or sessions (observations are workspace-local)
• Network resources or external APIs

What this skill does NOT do:

• Send data to external services
• Access "across sessions and projects" beyond the current workspace
• Execute arbitrary code or run external commands

Data scope clarification:

• "Failure detection" scans tool output and user messages within the current agent session
• Observations are stored in .learnings/ within the current workspace only
• No cross-project or cross-session data access occurs
• Pattern matching is local to the configured workspace

Detection trigger clarification: The "Detection Triggers" table describes patterns that indicate when this skill should be invoked. The agent can auto-invoke /fm detect when these patterns are detected, or users can invoke manually. This enables true agentic behavior — failures are captured automatically.

Provenance note: This skill is developed by Live Neon (https://github.com/live-neon/skills) and published to ClawHub under the leegitw account. Both refer to the same maintainer.

Acceptance Criteria

• [ ] /fm detect creates or updates observation with R++
• [ ] /fm record supports R, C, D counter updates
• [ ] /fm search finds observations by pattern, tag, or slug
• [ ] /fm classify returns correct tier based on N count
• [ ] /fm status shows eligible observations
• [ ] /fm refactor merges/splits observations correctly
• [ ] /fm converge detects similar patterns (≥0.8 similarity)
• [ ] Detection triggers work for test failures, user corrections, API errors
• [ ] Workspace files follow self-improving-agent format

*Consolidated from 10 skills as part of agentic skills consolidation (2026-02-15).*