memory

Memory System Skill

Unified memory operations across four tiers for AI agents.

---

Quick Start

# Check system health
python3 .claude/skills/memory/scripts/test_memory_health.py

# Search memory (Tier 1)
python3 .claude/skills/memory/scripts/search_memory.py "git hooks"

# Extract episode from session (Tier 2)
python3 .claude/skills/memory/scripts/extract_session_episode.py ".agents/sessions/2026-01-01-session-126.json"

# Update causal graph (Tier 3)
python3 .claude/skills/memory/scripts/update_causal_graph.py

---

When to Use This Skill

Scenario	Use Memory Router?	Alternative
Script needs memory	Yes	-
Agent needs deep context	No	context-retrieval agent
Human at CLI	No	/memory-search command
Cross-project semantic search	No	Forgetful MCP directly

See context-retrieval agent for complete decision tree.

---

Memory-First as Chesterton's Fence

Core Insight: Memory-first architecture implements Chesterton's Fence principle for AI agents.

"Do not remove a fence until you know why it was put up" - G.K. Chesterton

Translation for agents: Do not change code/architecture/protocol until you search memory for why it exists.

Why This Matters

Without memory search (removing fence without investigation):

• Agent encounters complex code, thinks "this is ugly, I'll refactor it"
• Removes validation logic that prevents edge case
• Production incident occurs
• Memory contains past incident that explains why validation existed

With memory search (Chesterton's Fence investigation):

• Agent encounters complex code
• Searches memory: search_memory.py "validation logic edge case"
• Finds past incident explaining why code exists
• Makes informed decision: preserve, modify, or replace with equivalent safety

Investigation Protocol

When you encounter something you want to change:

Change Type	Memory Search Required
Remove ADR constraint	search_memory.py "[constraint name]"
Bypass protocol	search_memory.py "[protocol name] why"
Delete >100 lines	search_memory.py "[component] purpose"
Refactor complex code	search_memory.py "[component] edge case"
Change workflow	search_memory.py "[workflow] rationale"

What Memory Contains (Git Archaeology)

Tier 1 (Semantic): Facts, patterns, constraints

• Why does PowerShell-only constraint exist? (ADR-005)
• Why do skills exist instead of raw CLI? (usage-mandatory)
• What incidents led to BLOCKING gates? (protocol-blocking-gates)

Tier 2 (Episodic): Past session outcomes

• What happened when we tried approach X? (session replay)
• What edge cases did we encounter? (failure episodes)

Tier 3 (Causal): Decision patterns

• What decisions led to success? (causal paths)
• What patterns should we repeat/avoid? (success/failure patterns)

Memory-First Gate (BLOCKING)

Before changing existing systems, you MUST:

1. python3 .claude/skills/memory/scripts/search_memory.py "[topic]"
2. Review results for historical context
3. If insufficient, escalate to Tier 2/3
4. Document findings in decision rationale
5. Only then proceed with change

Why BLOCKING: <50% compliance with "check memory first" guidance. Making it BLOCKING achieves 100% compliance (same pattern as session protocol gates).

Verification: Session logs must show memory search BEFORE decisions, not after.

Connection to Chesterton's Fence Analysis

See .agents/analysis/chestertons-fence.md for:

• 4-phase decision framework (Investigation → Understanding → Evaluation → Action)
• Application to ai-agents project (ADR-037 recursion guard, skills-first violations)
• Decision matrix for when to investigate
• Implementation checklist

Key takeaway: Memory IS your investigation tool. It contains the "why" that Chesterton's Fence requires you to discover.

---

Context Engineering

This skill implements progressive disclosure principles from Anthropic and claude-mem.ai research through three-layer architecture.

Architecture

Layer	Tool	Cost	When to Use
Index	search_memory.py	~100-500 tokens	Always start here
Details	mcp__serena__read_memory	~500-10K tokens	After index confirms relevance
Deep Dive	Follow cross-references	Variable	For complete understanding

Token Cost Visibility

# Count tokens before retrieval (informed ROI decision)
python3 .claude/skills/memory/scripts/count_memory_tokens.py .serena/memories/memory-index.md

# Output: memory-index.md: 2,450 tokens

Caching: SHA-256 hash-based cache in .serena/.token-cache.json provides 10-100x speedup on repeated queries.

See: scripts/README-count-tokens.md

Size Validation

# Pre-commit hook: enforce atomicity thresholds
python3 .claude/skills/memory/scripts/test_memory_size.py .serena/memories --pattern "*.md"

# Exit 0 (pass) or 1 (fail) with decomposition recommendations

Thresholds (from memory-size-001-decomposition-thresholds):

• Max 10,000 chars (~2,500 tokens, atomic memory)
• Max 15 skills (independent concepts per file)
• Max 5 categories (domain focus)

See: scripts/README-test-size.md

Principles

Progressive Disclosure: List names → Read details → Deep dive on cross-references. Prevents loading 9,500 tokens when only 1,200 are relevant (87% waste reduction).

Just-in-Time Retrieval: Serena-first with Forgetful augmentation. High precision through lexical search before expensive semantic operations.

Size Enforcement: Atomic memories prevent token waste. One retrievable concept per file.

For full analysis, see: .agents/analysis/context-engineering.md

---

Triggers

Use this skill when the user says:

• search memory for semantic search across tiers
• check memory health for system status
• extract episode from session for session replay
• update causal graph for pattern tracking
• count memory tokens for budget analysis

---

Quick Reference

Operation	Script	Key Parameters
Search facts/patterns	search_memory.py	query, --lexical-only, --max-results
Extract episode	extract_session_episode.py	session_log_path, --output-path
Update patterns	update_causal_graph.py	--episode-path, --dry-run
Health check	test_memory_health.py	--format (json/table)
Benchmark performance	measure_memory_performance.py	--serena-only, --format
Convert index links	convert_index_table_links.py	--memory-path, --dry-run
Cross-reference	invoke_memory_cross_reference.py	--memory-path, --threshold
Improve graph density	improve_memory_graph_density.py	--memory-path, --dry-run

---

Decision Tree

What do you need?
│
├─► Current facts, patterns, or rules?
│   └─► TIER 1: search_memory.py
│
├─► What happened in a specific session?
│   └─► TIER 2: Episode JSON in .agents/memory/episodes/
│
├─► Need to store new knowledge?
│   ├─ From completed session? → extract_session_episode.py
│   └─ Factual knowledge? → using-forgetful-memory skill
│
├─► Update decision patterns?
│   └─► TIER 3: update_causal_graph.py
│
└─► Not sure which tier?
    └─► Start with TIER 1 (search_memory.py), escalate if insufficient

---

Anti-Patterns

Anti-Pattern	Do This Instead
Skipping memory search	Always search before multi-step reasoning
Tier confusion	Follow decision tree explicitly
Forgetful dependency	Use --lexical-only fallback
Stale causal graph	Run update_causal_graph.py after extractions
Incomplete extraction	Only extract from COMPLETED sessions

---

See Also

Document	Content
quick-start.md	Common workflows
skill-reference.md	Detailed script parameters
tier-selection-guide.md	When to use each tier
memory-router.md	ADR-037 router architecture
reflexion-memory.md	ADR-038 episode/causal schemas
troubleshooting.md	Error recovery
benchmarking.md	Performance targets
agent-integration.md	Multi-agent patterns
zettelkasten-memory-agents.md	Atomic memory principle and auto-linking
codebase-knowledge-graph.md	GitNexus pattern for structural context via MCP

---

Storage Locations

Data	Location
Serena memories	.serena/memories/*.md
Forgetful memories	HTTP MCP (vector DB)
Episodes	.agents/memory/episodes/*.json
Causal graph	.agents/memory/causality/causal-graph.json

---

Verification

Operation	Verification
Search completed	Result count > 0 OR logged "no results"
Episode extracted	JSON file in .agents/memory/episodes/
Graph updated	Stats show nodes/edges added
Health check	All tiers show "available: true"

python3 .claude/skills/memory/scripts/test_memory_health.py --format table

---

Process

Phase 1: Query

Determine the memory tier and run the appropriate script.

Phase 2: Validate

Verify results are non-empty and relevant to the query context.

Phase 3: Report

Return structured results to the caller with source attribution.

---

Scripts

Script	Purpose	Exit Codes
search_memory.py	Tier 1 semantic search across Serena and Forgetful	0=success, 1=error
count_memory_tokens.py	Token counting with tiktoken caching	0=success, 1=error
test_memory_size.py	Memory atomicity validation	0=pass, 1=violations
test_memory_health.py	System health dashboard	0=success
extract_session_episode.py	Episode extraction from session logs	0=success, 1=error
update_causal_graph.py	Causal graph pattern tracking	0=success, 1=error
measure_memory_performance.py	Serena/Forgetful benchmark	0=success, 1=error

---

Related Skills

Skill	When to Use Instead
using-forgetful-memory	Deep Forgetful operations (create, update, link)
curating-memories	Memory maintenance (obsolete, deduplicate)
exploring-knowledge-graph	Multi-hop graph traversal