SCHEMA-FACTORY Skill | Agent Skills

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:

Prescriptive schema-schema — what valid schemas must look like
Deterministic layer — evaluate and refine before asking the LLM
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

| File | Purpose | |------|---------| | SCHEMA-SCHEMA.yml | Drives linting and ingestion | | schema_tool.py | CLI for all operations | | examples/schema-example.yml | Compact schema set | | examples/henry-minsky-blocksworld.yml | Classic microworld data |

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: context_generate
Includes: id, action, context, result, reliability, extended_context, 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

Deterministic checks first, LLM second — Python validates before synthesis
Emit events for traceability — Know what happened where
Prefer small, explicit context bundles — Don't dump everything
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/ — Theoretical foundation (Drescher)
../experiment/ — Schemas drive experiment design
../debugging/ — Schema failures are bugs to investigate
../planning/ — Schema composition is planning

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

Agent Skills: SCHEMA-FACTORY

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