TLA+ Expert Skill

Purpose

Provides expert-level TLA+ formal specification assistance for designing, verifying, and reasoning about concurrent and distributed systems within amplihack.

When This Skill Activates

• User asks to write or review a TLA+ specification
• User needs help with model checking (TLC) configuration or output interpretation
• User wants to formally verify a protocol or workflow design
• User asks about invariants, liveness properties, or safety properties
• User wants to apply formal methods to amplihack components
• User mentions PlusCal or wants to translate between PlusCal and TLA+
• User references Lamport, Demirbas, or formal methods best practices

How It Works

This skill delegates to the tla-plus-expert agent which has deep knowledge of:

1. TLA+ language and idioms — writing specs, operators, temporal formulas
2. TLC model checker — configuration, trace interpretation, state space management
3. Seven mental models (Demirbas) — abstraction, global shared memory, local guards, invariants, stepwise refinement, atomicity refinement, communication
4. Industry case studies — 8 production uses from AWS, MongoDB, Microsoft Azure
5. AI + TLA+ limitations — SysMoBench findings on LLM capabilities and guardrails
6. amplihack experiment infrastructure — manifest-driven experiments, heuristic scoring, TLC validation integration

Integration with Existing Infrastructure

The amplihack repo includes a TLA+ experiment runner at src/amplihack/eval/tla_prompt_experiment.py with:

• Manifest-driven experiment matrix (models x prompt variants x repeats)
• 6 heuristic scoring dimensions
• Real TLC validation support
• Replay and live execution modes

TLA+ specs live in experiments/hive_mind/tla_prompt_language/specs/.

Usage Examples

# Write a spec for a new protocol
/tla-plus-expert Write a TLA+ spec for our consensus voting workflow

# Review an existing spec
/tla-plus-expert Review specs/SmartOrchestrator.tla for correctness

# Help with TLC output
/tla-plus-expert TLC found a counterexample in my spec, help me understand it

# Decide if TLA+ is appropriate
/tla-plus-expert Should I formally specify this retry cascade logic?

# Generate invariants
/tla-plus-expert What invariants should I check for a parallel workstream manager?

Key Resources

• TLA+ specs: experiments/hive_mind/tla_prompt_language/specs/
• Experiment runner: src/amplihack/eval/tla_prompt_experiment.py
• TLC binary: /usr/local/bin/tlc (if installed)
• Issue #3939: TLA+ integration roadmap