SCHEMA-FACTORY

"Deterministic checks first, LLM second."

Build, lint, ingest, compose, and generate context for Drescher-style schemas.

Why This Exists

Gary Drescher's schema mechanism is strongest when it has:

1. Prescriptive schema-schema — what valid schemas must look like
2. Deterministic layer — evaluate and refine before asking the LLM
3. Context generator — emit only needed patterns and evidence

The goal is hybrid orchestration: Python does deterministic work, Cursor/LLM handles synthesis, MOOLLM stays explicit about what came from where.

Key Files

Quick Use

# Validate schemas
python3 schema_tool.py lint examples/schema-example.yml

# Compose action chain toward goal
python3 schema_tool.py compose --schemas examples/schema-example.yml --goal postgres-running

# Generate LLM context bundle
python3 schema_tool.py context --schemas examples/schema-example.yml --goal pyvision-running

Methods

LINT

Validate schema against schema-schema.

Input: One or more schema files (YAML) Output: Pass/fail + diagnostics Emits: schema_lint Checks: Required fields, type validation, reliability range, non-empty context/result

python3 schema_tool.py lint my-schemas.yml

INGEST

Update schemas from experience logs or observed transitions.

Input: Experience logs Output: Updated schema set + evidence counts Emits: schema_ingest Deterministic: No LLM calls; only schema updates

python3 schema_tool.py ingest experience-log.yml --into my-schemas.yml

COMPOSE

Build action chain toward goal.

Input: Schema set + goal Output: Composed action chain + rationale Emits: schema_compose

python3 schema_tool.py compose --schemas my-schemas.yml --goal target-state

CONTEXT

Generate compact context bundle for LLM synthesis.

Input: Schema set + goal + optional focus items Output: Compact context bundle Emits: contextgenerate Includes: id, action, context, result, reliability, extendedcontext, extended_results

python3 schema_tool.py context --schemas my-schemas.yml --goal target-state --focus item1,item2

Schema Structure

schema:
  id: "unique-identifier"
  action: "what-the-schema-does"
  context:
    - precondition-1
    - precondition-2
  result:
    - postcondition-1
  reliability: 0.85  # 0.0-1.0
  
  # Optional
  extended_context: [...]
  extended_results: [...]
  evidence_count: 47
  marginal_attribution: {...}

Principles

1. Deterministic checks first, LLM second — Python validates before synthesis
2. Emit events for traceability — Know what happened where
3. Prefer small, explicit context bundles — Don't dump everything
4. Schema-schema can evolve — Via the same learning loop it governs

The Schema-Schema

The SCHEMA-SCHEMA.yml defines what valid schemas must look like:

• Required fields and their types
• Reliability range constraints
• Context/result non-empty rules
• Extension field patterns

This is prescriptive — schemas that don't match get lint errors.

Integration with LLM

The factory provides deterministic foundation for LLM reasoning:

# 1. Python validates and composes
schema_factory compose --goal postgres-running

# 2. Output becomes LLM context
"Here are the relevant schemas and a proposed action chain..."

# 3. LLM synthesizes and refines
"Based on these schemas, I recommend..."

# 4. Results feed back into ingest
schema_factory ingest --experience new-observations.yml

Dovetails With

• [../schema-mechanism/](../schema-mechanism/) — Theoretical foundation (Drescher)
• [../experiment/](../experiment/) — Schemas drive experiment design
• [../debugging/](../debugging/) — Schema failures are bugs to investigate
• [../planning/](../planning/) — Schema composition is planning

"Validate structure. Compose plans. Generate context. Let the LLM shine where it should."