Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Strategic Forgetting — Not remembering everything is a feature
Hierarchical Organization — Navigate categories, not scan linearly
From PageIndex
Vectorless Retrieval — LLM reasoning instead of embedding similarity
Tree-Structured Index — O(log n) navigation, not O(n) scan
Explainable Results — Every retrieval traces a path through categories
Reasoning-Based Search — "Why relevant?" not "how similar?"
Cloud-First (EvoClaw)
Device is replaceable — Soul lives in cloud (Turso)
Critical sync — Hot + tree sync after every conversation
Disaster recovery — Full restore in <2 minutes
Multi-device — Same agent across phone/desktop/embedded
Memory Tiers
🔴 Hot Memory (5KB max)
Purpose: Core identity and active context, always in agent's context window.
Structure:
{"identity":{"agent_name":"Agent","owner_name":"User","owner_preferred_name":"User","relationship_start":"2026-01-15","trust_level":0.95},"owner_profile":{"personality":"technical, direct communication","family":["Sarah (wife)","Luna (daughter, 3yo)"],"topics_loved":["AI architecture","blockchain","system design"],"topics_avoid":["small talk about weather"],"timezone":"Australia/Sydney","work_hours":"9am-6pm"},"active_context":{"projects":[{"name":"EvoClaw","description":"Self-evolving agent framework","status":"Active - BSC integration for hackathon"}],"events":[{"text":"Hackathon deadline Feb 15","timestamp":1707350400}],"tasks":[{"text":"Deploy to BSC testnet","status":"pending","timestamp":1707350400}]},"critical_lessons":[{"text":"Always test on testnet before mainnet","category":"blockchain","importance":0.9,"timestamp":1707350400}]}
Auto-pruning:
Lessons: Max 20, removes lowest-importance when full
Events: Keeps last 10 only
Tasks: Max 10 pending
Total size: Hard limit at 5KB, progressively prunes if exceeded
Generates:MEMORY.md — auto-rebuilt from structured hot state
🟡 Warm Memory (50KB max, 30-day retention)
Purpose: Recent distilled facts with decay scoring.
Entry format:
{"id":"abc123def456","text":"Decided to use zero go-ethereum deps for EvoClaw to keep binary small","category":"projects/evoclaw/architecture","importance":0.8,"created_at":1707350400,"access_count":3,"score":0.742,"tier":"warm"}
score < 0.05 → Frozen (delete after retention period)
Eviction triggers:
Age > 30 days AND score < 0.3
Total warm size > 50KB (evicts lowest-scored)
Manual consolidation
🟢 Cold Memory (Unlimited, Turso)
Purpose: Long-term archive, queryable but never bulk-loaded.
Schema:
CREATE TABLE cold_memories (
id TEXT PRIMARY KEY,
agent_id TEXT NOT NULL,
text TEXT NOT NULL,
category TEXT NOT NULL,
importance REALDEFAULT0.5,
created_at INTEGERNOT NULL,
access_count INTEGERDEFAULT0
);
CREATE TABLE critical_state (
agent_id TEXT PRIMARY KEY,
data TEXT NOT NULL, -- {hot_state, tree_nodes, timestamp}
updated_at INTEGERNOT NULL
);
Retention: 10 years (configurable)
Cleanup: Monthly consolidation removes frozen entries older than retention period
Tree Index
Purpose: Hierarchical category map for O(log n) retrieval.
Input: Raw conversation text
Output: Structured JSON
{"fact":"User decided to use raw JSON-RPC for BSC to avoid go-ethereum dependency","emotion":"determined","people":["User"],"topics":["blockchain","architecture","dependencies"],"actions":["decided to use raw JSON-RPC","avoid go-ethereum"],"outcome":"positive"}
# Rule-based (default)
distiller.py --text "Had a productive chat about the BSC integration..." --mode rule
# LLM-powered
distiller.py --text "..." --mode llm --llm-endpoint http://localhost:8080/complete
# With core summary
distiller.py --text "..." --mode rule --core-summary
Stage 2→3: Distilled → Core Summary
Purpose: One-line summary for tree index
Example:
Distilled: {
"fact": "User decided raw JSON-RPC for BSC, no go-ethereum",
"outcome": "positive"
}
Core summary: "BSC integration: raw JSON-RPC (no deps)"
Target: <30 bytes
LLM-Powered Tree Search
Purpose: Semantic search through tree structure using LLM reasoning.
How it works:
Build prompt with tree structure + query
LLM reasons about which categories are relevant
Returns category paths with relevance scores
Fetches memories from those categories
Example:
Query: "What did we decide about the hackathon deadline?"
Keyword search returns:
projects/evoclaw (0.8)
technical/deployment (0.4)
LLM search reasons:
projects/evoclaw/bsc (0.95) — "BSC integration for hackathon"
You are a memory retrieval system. Given a memory tree index and a query,
identify which categories are relevant.
Memory Tree Index:
projects/evoclaw — EvoClaw framework (warm:6, cold:45)
projects/evoclaw/bsc — BSC integration (warm:3, cold:12)
...
User Query: What did we decide about the hackathon deadline?
Output (JSON):
[
{"path": "projects/evoclaw/bsc", "relevance": 0.95, "reason": "BSC work for hackathon"},
{"path": "active_context/events", "relevance": 0.85, "reason": "deadline tracking"}
]
{"status":"warning","warnings_count":2,"warnings":["Tool 'Z-Image' mentioned without URL/documentation link","Action 'install' mentioned without command example"],"suggestions":["Add URLs for mentioned tools/services","Include command examples for setup/installation steps","Document next steps after decisions"]}
Extract Metadata (v2.1.0)
memory_cli.py extract-metadata --file PATH
Purpose: Extract structured metadata (URLs, commands, paths) from a file.
memory_cli.py search-url --url FRAGMENT [--limit 5] [--agent-id default]
Purpose: Search facts by URL fragment.
Example:
# Find all facts with comfy.org URLs
memory_cli.py search-url --url "comfy.org"# Find GitHub repos
memory_cli.py search-url --url "github.com" --limit 10
Output:
{"query":"comfy.org","results_count":1,"results":[{"id":"abc123","text":"Z-Image ComfyUI model for photorealistic images","category":"tools/image-generation","metadata":{"urls":["https://docs.comfy.org/tutorials/image/z-image/z-image"],"commands":["huggingface-cli download Tongyi-MAI/Z-Image"],"paths":[]}}]}
# Rule-based distillation
memory_cli.py distill --text "User: Let's deploy to testnet first. Agent: Good idea, safer that way."# LLM distillation
memory_cli.py distill \
--text "Long conversation with nuance..." \
--llm --llm-endpoint http://localhost:8080/complete
Output:
{"distilled":{"fact":"Decided to deploy to testnet before mainnet","emotion":"cautious","people":[],"topics":["deployment","testnet","safety"],"actions":["deploy to testnet"],"outcome":"positive"},"mode":"rule","original_size":87,"distilled_size":156}
Hot Memory
# Update hot state
memory_cli.py hot --update KEY JSON [--agent-id default]
# Rebuild MEMORY.md
memory_cli.py hot --rebuild [--agent-id default]
# Show current hot state
memory_cli.py hot [--agent-id default]
Keys:
identity — Agent/owner identity info
owner_profile — Owner preferences, personality
lesson — Add critical lesson
event — Add event to active context
task — Add task to active context
project — Add/update project
Examples:
# Update owner profile
memory_cli.py hot --update owner_profile '{"timezone": "Australia/Sydney", "work_hours": "9am-6pm"}'# Add lesson
memory_cli.py hot --update lesson '{"text": "Always test on testnet first", "category": "blockchain", "importance": 0.9}'# Add project
memory_cli.py hot --update project '{"name": "EvoClaw", "status": "Active", "description": "Self-evolving agent framework"}'# Rebuild MEMORY.md
memory_cli.py hot --rebuild
Tree
# Show tree
memory_cli.py tree --show [--agent-id default]
# Add node
memory_cli.py tree --add "path/to/category""Description" [--agent-id default]
# Remove node
memory_cli.py tree --remove "path/to/category" [--agent-id default]
# Prune dead nodes
memory_cli.py tree --prune [--agent-id default]
Examples:
# Add category
memory_cli.py tree --add "projects/evoclaw/bsc""BSC blockchain integration"# Remove empty category
memory_cli.py tree --remove "old/unused/path"# Prune dead nodes (60+ days no activity)
memory_cli.py tree --prune
Use cheaper models for frequent operations (distill, search)
Batch distillation — Queue conversations, distill in batch
Cache tree prompts — Tree structure doesn't change often
Skip LLM for simple — Use rule-based for short conversations
Example LLM Endpoint
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route("/complete", methods=["POST"])defcomplete():
data = request.json
prompt = data["prompt"]
# Call your LLM (OpenAI, Anthropic, local model, etc.)
response = llm_client.complete(prompt)
return jsonify({"text": response})
if __name__ == "__main__":
app.run(port=8080)
Performance Characteristics
Context Size:
Hot: ~5KB (always loaded)
Tree: ~2KB (always loaded)
Retrieved: ~1-3KB per query
Total: ~8-15KB (constant, regardless of agent age)
Retrieval Speed:
Keyword: 10-20ms
LLM tree search: 300-600ms
Cold query: 50-100ms
5-Year Scenario:
Hot: Still 5KB (living document)
Warm: Last 30 days (~50KB)
Cold: ~50MB in Turso (compressed distilled facts)
Tree: Still 2KB (different nodes, same size)
Context per session: Same as day 1
Comparison with Alternatives
System
Memory Model
Scaling
Accuracy
Cost
Flat MEMORY.md
Linear text
❌ Months
⚠️ Degrades
❌ Linear
Vector RAG
Embeddings
✅ Years
⚠️ Similarity≠relevance
⚠️ Moderate
EvoClaw Tiered
Tree + tiers
✅ Decades
✅ Reasoning-based
✅ Fixed
Why tree > vectors:
Accuracy: 98%+ vs. 70-80% (PageIndex benchmark)
Explainable: "Projects → EvoClaw → BSC" vs. "cosine 0.73"
Multi-hop: Natural vs. poor
False positives: Low vs. high
Troubleshooting
Tree size exceeding limit
# Prune dead nodes
memory_cli.py tree --prune
# Check which nodes are largest
memory_cli.py tree --show | grep "Memories:"# Manually remove unused categories
memory_cli.py tree --remove "unused/category"
Warm memory filling up
# Run consolidation
memory_cli.py consolidate --mode daily --db-url "$TURSO_URL" --auth-token "$TURSO_TOKEN"# Check stats
memory_cli.py metrics
# Lower eviction threshold (keeps less in warm)# Edit config.json: "eviction_threshold": 0.4
Hot memory exceeding 5KB
# Hot auto-prunes, but check structure
memory_cli.py hot
# Remove old projects/tasks manually
memory_cli.py hot --update project '{"name": "OldProject", "status": "Completed"}'# Rebuild to force pruning
memory_cli.py hot --rebuild
LLM search failing
# Fallback to keyword search (automatic)
memory_cli.py retrieve --query "..." --limit 5
# Test LLM endpoint
curl -X POST http://localhost:8080/complete -d '{"prompt": "test"}'# Generate prompt for external testing
tree_search.py --query "..." --tree-file memory/memory-tree.json --mode llm --llm-prompt-file test.txt
Migration from v1.x
Backward compatible: Existing warm-memory.json and memory-tree.json files work as-is.
New files:
config.json (optional, uses defaults)
hot-memory-state.json (auto-created)
metrics.json (auto-created)
Steps:
Update skill: clawhub update tiered-memory
Run consolidation to rebuild hot state: memory_cli.py consolidate
v2.1.0 — A mind that remembers everything is as useless as one that remembers nothing. The art is knowing what to keep. Now with structured metadata to remember HOW, not just WHAT. 🧠🌲🔗
