Failure Memory

Stop making the same mistakes — turn failures into patterns that prevent recurrence

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

bash

openclaw install leegitw/failure-memory

Dependencies: leegitw/context-verifier (for file change detection)

bash

# 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

text

/fm <sub-command> [arguments]

Sub-Commands

Command	CJK	Logic	Trigger
/fm detect	検出	fail∈{test,user,API}→record	Next Steps (auto)
/fm record	記録	pattern→obs, R++∨C++∨D++	Next Steps (auto)
/fm search	索引	query(pattern∨tag∨slug)→obs[]	Explicit
/fm classify	分類	obs→tier∈{N=1:弱,N=2:中,N≥3:強}	Explicit
/fm status	状態	eligible:R≥3∧C≥2, recent:30d	Explicit
/fm refactor	整理	obs[]→merge∨split∨restructure	Explicit
/fm converge	収束	pattern[]→detect(similarity≥0.8)	Explicit

Arguments

/fm detect

Argument	Required	Description
type	Yes	Failure type: test, user, api, error
context	No	Additional context for the failure

/fm record

Argument	Required	Description
pattern	Yes	Pattern description or observation ID
counter	No	Counter to increment: R (default), C, or D

/fm search

Argument	Required	Description
query	Yes	Search pattern, tag, or slug
status	No	Filter by status: pending, eligible, all (default)

/fm classify

Argument	Required	Description
observation	Yes	Observation ID or pattern

/fm status

Argument	Required	Description
--eligible	No	Show only eligible observations (R≥3 ∧ C≥2)
--recent	No	Show only observations from last 30 days

/fm refactor

Argument	Required	Description
observations	Yes	Comma-separated observation IDs
action	Yes	Action: merge, split, restructure

/fm converge

Argument	Required	Description
--threshold	No	Similarity threshold (default: 0.8)

Detection Triggers

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

Pattern	Source	Action
test.exit_code != 0	Tool output	/fm detect test
"Actually...", "No, that's wrong"	User message	/fm record correction
"I meant...", "Not X, Y"	User message	/fm record correction
API 4xx/5xx response	Tool output	/fm detect api
"error:", "failed", "Exception"	Tool output	/fm detect error
Deployment rollback	CI/CD output	/fm detect deployment
Database migration failed	Tool output	/fm detect migration

Example: API Failure Detection

text

[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

text

[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

Counter	Meaning	Updated By
R (Recurrence)	Auto-detected occurrences	/fm detect, /fm record
C (Confirmations)	Human-verified true positives	Human via /fm record C
D (Disconfirmations)	Human-verified false positives	Human via /fm record D

Evidence Tiers

Tier	Criteria	Meaning
弱 (weak)	N=1	Single occurrence, may be noise
中 (emerging)	N=2	Pattern emerging, monitor
強 (strong)	N≥3	Established pattern, actionable

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

text

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

/fm status output

text

=== 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)

yaml

# .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

Condition	Behavior
Invalid sub-command	List available sub-commands
Missing observation ID	Error with usage example
No matches found	"No observations match query"
Duplicate detection	Increment R counter, don't create new observation

Next Steps

After invoking this skill:

Condition	Action
R incremented	Check eligibility: R≥3 ∧ C≥2 → notify user
R≥3 ∧ C≥2	Suggest /ce generate for constraint
Pattern recurring	Link with See Also, bump priority
Always	Update .learnings/ERRORS.md or .learnings/LEARNINGS.md

Workspace Files

This skill reads/writes:

text

.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

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