Agent skill

inland-empire

Unified memory substrate. Store facts, patterns, and context. Query with remember, consult, and stats commands.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/productivity/inland-empire

Metadata

Additional technical details for this skill

version
1.0.0
env vars 
{
    "optional": [
        "LIBSQL_URL",
        "LIBSQL_AUTH_TOKEN",
        "MEM0_API_KEY",
        "POSTGRES_URL",
        "INLAND_EMPIRE_STATE_DIR",
        "INLAND_EMPIRE_EVENT_HOME"
    ],
    "required": []
}
dependencies 
{
    "optional": [
        "openmemory"
    ],
    "required": [
        "memory_libsql",
        "memory_graph"
    ]
}

SKILL.md

Inland Empire

"This is your gut feeling. The raw data of the soul. When logic fails, consult the Empire."

Capabilities

The Inland Empire unifies three memory backends but only exposes sanitized aliases (fact_memory, pattern_memory, context_memory). Internals (MCP, mem0, JSONL) are hidden.

Alias Memory Type Backend Storage Notes
fact_memory fact mcp-memory-libsql Graph entities/relations Defaults to local SQLite fallback
pattern_memory pattern mem0 Hosted API (MEM0_API_KEY) or self-hosted Postgres (POSTGRES_URL)
context_memory context JSONL Local file (session_memory.jsonl) Always available

Backend detection

  • mem0 hosted: MEM0_API_KEY present
  • mem0 self-hosted: POSTGRES_URL present
  • mem0 disabled: neither credential; pattern memory gracefully skipped
  • LIBSQL_URL optional; if missing, a local SQLite file powers fact memory
  • INLAND_EMPIRE_STATE_DIR overrides the storage directory (tests, sandboxes, multi-project)

Commands

remember

Store a memory across configured backends.

bash
python3 inland-empire.py remember "<text>" [--type fact|pattern|context]

Examples:

bash
python3 inland-empire.py remember "User prefers verbose error messages"
python3 inland-empire.py remember "The auth flow has race conditions" --type pattern

consult

Query stored memories with optional depth and type filters. Results contain backend aliases, partial-result indicators, and normalized metadata.

bash
python3 inland-empire.py consult "<query>" [--depth shallow|deep] [--type fact|pattern|context]

Example:

bash
python3 inland-empire.py consult "user preferences" --depth deep --type pattern

Response shape

json
{
  "status": "ok",
  "command": "consult",
  "result": {
    "query": "user preferences",
    "depth": "deep",
    "results": [
      {
        "origin": "pattern",
        "summary": "User prefers verbose error messages",
        "score": 0.812,
        "observed_at": null,
        "backend": "pattern_memory",
        "partial": false,
        "metadata": {
          "id": "mem0_123",
          "user_id": "agent_subconscious",
          "created_at": "2024-01-15T10:30:00Z",
          "updated_at": "2024-01-15T10:30:00Z",
          "mode": "hosted"
        }
      }
    ],
    "metadata": {
      "requested_backends": ["fact_memory", "pattern_memory"],
      "completed_backends": ["pattern_memory"],
      "timed_out_backends": ["fact_memory"],
      "partial": true
    }
  }
}

stats

Display backend health, detection mode, and basic counts (context entries).

bash
python3 inland-empire.py stats

When to Use

  • Store hunches, preferences, and soft context that doesn't belong in files
  • Recall project context after breaks
  • Detect patterns across sessions
  • Build institutional memory for recurring issues

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