Memory Settings - Configuration Viewer

SkillDocs & knowledge

Display current memory system configuration and settings

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Memory Settings - Configuration Viewer skill

What this skill tells your AI

The instructions your AI receives, as published by hidden-history/ai-memory in .claude/skills/aim-settings/SKILL.md and read by ahel’s review.

Display the current configuration of the AI Memory Module, including collections, types, thresholds, token budgets, and service endpoints.

Activation

# Show all memory settings
/aim-settings

# Show specific section
/aim-settings --section collections
/aim-settings --section types
/aim-settings --section thresholds
/aim-settings --section services
/aim-settings --section agents

Configuration Sections

Collections

Shows the 3 Memory System V2.0 collections:

  • code-patterns - Project-specific implementation patterns
  • conventions - Cross-project shared conventions
  • discussions - Decision context and session summaries

Memory Types (14 total)

Organized by collection:

  • code-patterns: implementation, error_fix, refactor, file_pattern
  • conventions: rule, guideline, port, naming, structure
  • discussions: decision, session, blocker, preference, context

Thresholds

  • similarity_threshold - Minimum relevance score for search results (default: 0.7)
  • dedup_threshold - Similarity threshold for duplicate detection (default: 0.95)
  • max_retrievals - Maximum memories per search (default: 5)
  • token_budget - Maximum tokens for context injection (default: 4000, per BP-039)

Services

Shows connection details for:

  • Qdrant - Vector database (default: localhost:26350)
  • Embedding Service - Jina AI embeddings (default: localhost:28080)
  • Monitoring API - Health checks and metrics (default: localhost:28000)
  • Streamlit Dashboard - Web UI (default: localhost:28501)
  • Grafana - Metrics visualization (default: localhost:23000)
  • Prometheus - Metrics storage (default: localhost:29090)
  • Pushgateway - Metrics push gateway (default: localhost:29091)

Agent Token Budgets

Shows token allocation per BMAD agent:

  • architect: 1500 tokens
  • analyst: 1200 tokens
  • pm: 1200 tokens
  • developer/dev: 1200 tokens
  • solo-dev: 1500 tokens
  • quick-flow-solo-dev: 1500 tokens
  • ux-designer: 1000 tokens
  • qa: 1000 tokens
  • tea: 1000 tokens
  • code-review/code-reviewer: 1200 tokens
  • scrum-master/sm: 800 tokens
  • tech-writer: 800 tokens
  • default: 1000 tokens

Logging

  • log_level - Logging verbosity (default: INFO)
  • log_format - Log output format (json or text, default: json)

Collection Size Limits

  • Warning threshold - 10,000 points
  • Critical threshold - 50,000 points

Activation Examples

# View complete configuration
/aim-settings

# Check current thresholds
/aim-settings --section thresholds

# View service endpoints
/aim-settings --section services

# Check agent token budgets
/aim-settings --section agents

# View all memory types
/aim-settings --section types

Python Configuration Reference

Configuration is managed by src/memory/config.py:

from src.memory.config import get_config, AGENT_TOKEN_BUDGETS, get_agent_token_budget

# Get configuration singleton
config = get_config()

# Access settings
print(f"Qdrant: {config.qdrant_host}:{config.qdrant_port}")
print(f"Similarity threshold: {config.similarity_threshold}")
print(f"Max retrievals: {config.max_retrievals}")

# Get agent-specific token budget
budget = get_agent_token_budget("architect")  # Returns 1500

Environment Variables

Configuration can be customized via environment variables or .env file:

# Core thresholds
SIMILARITY_THRESHOLD=0.7    # Retrieval relevance cutoff
DEDUP_THRESHOLD=0.95        # Duplicate detection sensitivity
MAX_RETRIEVALS=10           # Results per search
TOKEN_BUDGET=4000           # Context injection limit (per BP-039)

# Service endpoints
QDRANT_HOST=localhost
QDRANT_PORT=26350
EMBEDDING_HOST=127.0.0.1
EMBEDDING_PORT=28080
MONITORING_HOST=localhost
MONITORING_PORT=28000

# Logging
LOG_LEVEL=INFO              # DEBUG, INFO, WARNING, ERROR, CRITICAL
LOG_FORMAT=json             # json or text

# Collection size limits
COLLECTION_SIZE_WARNING=10000
COLLECTION_SIZE_CRITICAL=50000

Configuration Precedence

Settings are loaded in this order (highest priority first):

  1. Environment variables
  2. .env file in project root
  3. Default values

Output Format

The skill displays configuration in organized sections with:

  • Current values
  • Default values (if different)
  • Validation ranges (for thresholds)
  • Service URLs with ports
  • Memory type mappings to collections

Technical Details

  • Type Safety: Uses pydantic-settings v2.6+ for validation
  • Immutable: Configuration is frozen (thread-safe)
  • Singleton: Single config instance per process (lru_cache)
  • Validation: All thresholds validated on load

Related Skills

  • /aim-search - Use these settings for memory search
  • /aim-status - Check system health and statistics

Notes

  • Configuration is loaded once at startup
  • Changes to .env require service restart
  • All ports use 2XXXX prefix to avoid conflicts
  • Token budgets optimized per agent role (architects need more context than scrum masters)

Signals

GitHub stars
41
Forks
5
Last commit
Sep 2026
Advanced
Item type
skill
Key
aim-settings
Source
github.com/hidden-history/ai-memory