Nw Formal Verification Tlaplus logo

Nw Formal Verification Tlaplus

Organization
nWave-ai
nw-formal-verification-tlaplus

TLA+ and PlusCal for specifying distributed system invariants. Decision heuristics for when formal verification adds value, key patterns, state explosion management, and alternatives comparison.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-formal-verification-tlaplus
Stars
616
Forks
65
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Formal Verification Tlaplus AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-formal-verification-tlaplus
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-formal-verification-tlaplus .claude/skills/nw-formal-verification-tlaplus
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Formal Verification Tlaplus in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nw Formal Verification Tlaplus on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nw Formal Verification Tlaplus is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Formal Verification with TLA+

When to Recommend Formal Verification

Decision Tree

Is the system distributed or concurrent?
|
+-- No --> Complex state machine with high failure cost?
|          +-- No --> NOT cost-effective. Use property-based testing.
|          +-- Yes --> CONSIDER TLA+
|
+-- Yes --> Consensus, coordination, or distributed transactions?
|           +-- Yes --> RECOMMEND TLA+
|           +-- No --> Could concurrency bug cause data loss or safety issues?
|                      +-- Yes --> RECOMMEND TLA+
|                      +-- No --> OFFER as option

Strong Indicators (Recommend)

DomainWhy TLA+ Adds ValueEvidence
Distributed consensus (Paxos, Raft)Subtle interleaving bugs in leader electionRaft TLA+ spec ~400 lines, found implementation bugs
Financial distributed transactionsAtomicity violations cause monetary lossAWS DynamoDB replication verified
Leader election, distributed lockingSplit-brain, deadlock, stale-lockAWS lock manager verified
Eventual consistency / CRDTsConvergence proofs requiredTLA+ CRDT framework verifies SEC
Safety-critical state machinesRegulatory requirementsDO-178C, CENELEC recognize formal methods
Multi-party coordination (sagas, 2PC)Compensation ordering, partial failure2PC is canonical TLA+ example
Data replication protocolsOrdering, consistency under failureElasticsearch, MongoDB, Cosmos DB verified

When NOT to Use

  • Simple CRUD (bugs are in implementation, not design)
  • Single-process without complex state machines
  • Prototypes/MVPs (design will change before verification completes)
  • Performance optimization (TLA+ models correctness, not performance)

Cost-Benefit Reference

  • Learning curve: 2-3 weeks to useful results (AWS engineers, all levels)
  • Typical spec effort: 2-4 weeks part-time for a distributed protocol
  • ROI highest when: bug cost is high, system is long-lived, protocol is novel, concurrency testing is impractical

Core Concepts for Architects

What TLA+ Specifies

TLA+ describes what a system should do (allowed behaviors), not how to implement it. Specifications are mathematical objects checked for correctness before any code exists.

Safety vs. Liveness

Property TypeMeaningExpressionExample
SafetyNothing bad happensInvariant: predicate true in every reachable state"Two processes never hold same lock"
LivenessSomething good eventually happensTemporal: <> (eventually), []<> (infinitely often)"Every request eventually gets response"

Safety violations produce counterexample traces (the debugging artifact). Liveness requires fairness conditions.

PlusCal vs. Raw TLA+

PlusCal compiles to TLA+ with programming-like syntax. Start with PlusCal for first 2-3 specs, then learn raw TLA+ for cases PlusCal cannot express.

Key PlusCal constructs: variables (state) | labels (atomic action boundaries) | either/or (nondeterministic choice) | await (blocking) | process \in 1..N (concurrent processes) | fair process (weak fairness)

Labels define concurrency granularity: everything between two labels is one atomic step. Two processes interleave only at label boundaries.

State Explosion Management

State space grows exponentially: (states per node)^(nodes) x (message permutations).

Containment Strategies

StrategyTechniqueImpact
Bound parametersStart with 2-3 nodes, 2-4 messagesMost bugs appear at small N
Symmetry reductionSYMMETRY Permutations(Nodes)Up to N! reduction
Reduce labelsMerge labels where fine-grained atomicity unnecessaryOrders of magnitude
State constraintsCONSTRAINT Len(log[n]) < MaxLogLengthPrune uninteresting states
AbstractionModel protocol not implementation (TCP -> message set)Dramatic reduction
DecompositionMultiple focused specs, not one monolithEach independently checkable
Progressive refinement2 nodes -> 3 nodes -> add failures -> add livenessIncremental verification
Simulation modejava -jar tla2tools.jar -simulate -depth 100Trades completeness for speed

Memory and Time Budgets

Unique StatesExpected TimeMemoryApproach
< 10KSeconds< 1 GBExhaustive, single thread
10K - 1MMinutes1-4 GBExhaustive, -workers auto
1M - 100MHours4-32 GBExhaustive with constraints
100M - 1BDays32-64 GBLarge instance or simulation
> 1BWeeks60+ GBSimulation, TLAPS, or decompose

Estimation Before Running

  1. Count distinct variable values in model
  2. Multiply domains together for baseline
  3. Start TLC with smallest parameters, observe state count
  4. Extrapolate: doubling a parameter typically squares or cubes the space

Key Specification Patterns

Two-Phase Commit (2PC)

  • Variables: rmState, tmState, tmPrepared, msgs
  • Safety: no RM commits while another aborts (Consistency)
  • State space: 3 RMs ~718 states, 5 RMs ~21,488 states
  • Common mistake: not modeling RM spontaneous abort or unreliable network

Distributed Consensus (Raft)

  • Variables: currentTerm, votedFor, log, state, votesGranted, msgs
  • Safety: at most one leader per term (ElectionSafety)
  • Safety: logs with same index+term are identical (LogMatching)
  • State space: 3 nodes, MaxTerm=2 ~10K-100K states

Saga (Compensating Transactions)

  • Variables: stepState, sagaState, compensateIdx
  • Safety: steps execute in order, compensations in reverse (OrderInvariant)
  • Safety: no completed steps remain after abort (CompensationComplete)
  • Common mistake: not enforcing reverse compensation order

Distributed Lock with Lease

  • Variables: lockHolder, leaseExpiry, clock, nodeState
  • Safety: at most one holder (MutualExclusion)
  • Models crash (node loses awareness) and lease expiry
  • Common mistake: not distinguishing server-side lock state from node belief

CRDT Convergence (G-Counter)

  • Variables: counters (vector per node)
  • Safety: counters monotonically non-decreasing
  • Liveness: all nodes eventually converge after merge
  • Common mistake: merge not commutative, associative, and idempotent

Alternatives Comparison

ToolBest ForLearningDistributed SystemsTemporal Properties
TLA+/PlusCalDistributed protocols, consensus2-3 weeksExcellentNative
AlloyData models, structural properties1-2 weeksAdequateLimited (Alloy 6)
Property-Based TestingImplementation correctnessHours-daysWith stateful testingNone
XState/StatechartsUI workflows, single-processDaysNot applicableNone
Session Types/ScribbleCommunication patternsModerateGood (message patterns)Implicit
TLAPS (proofs)Critical certificationMonthsExcellentFull

Combined Workflow (TLA+ + PBT)

  1. Write TLA+ spec during DESIGN wave; identify invariants
  2. Model-check with TLC to verify design
  3. Implement code during DELIVER wave
  4. Reuse TLA+ invariants as PBT properties
  5. PBT verifies implementation conforms to verified design

Architecture Decision Record Template

markdown
## Decision: Use TLA+ for [Component Name]

### Context
[Component] implements [protocol] with [N] participants
and [concurrency/distribution characteristics].

### Problem
Informal reasoning about [failure/interleaving scenario]
is insufficient because [reason].

### Decision
Formally specify [component] in TLA+/PlusCal and verify:
- Safety: [specific invariants]
- Liveness: [specific temporal properties]

### Model Parameters
- Nodes: [2-3 for initial verification]
- Messages: [bounded to N]
- Failure modes: [crash, partition, message loss]

### Estimated Effort
- Specification: [1-2 weeks]
- Model checking: [hours to days]

### Not Modeling (out of scope)
- [performance, serialization, UI, etc.]

Architect's Checklist

  1. Identify components with concurrency, distribution, or complex state machines
  2. Determine safety properties (what must NEVER happen)
  3. Determine liveness properties (what must EVENTUALLY happen)
  4. Estimate model parameters and state space
  5. Assess cost-effectiveness vs. alternatives (decision tree above)
  6. Document decision in ADR with specific invariants and properties
  7. Scope verification: focused specs per subsystem, not one monolith

Industry Precedent

OrganizationSystems VerifiedOutcome
AWS14 projects across 10 systems (DynamoDB, S3, EBS)Found subtle bugs in every system; management actively encourages adoption
Azure Cosmos DBAll 5 consistency levelsSpecs became authoritative reference, replaced ambiguous docs
MongoDBReplication, reconfiguration, transactionsLogless reconfig deployed since 4.4, no protocol bugs
ElasticsearchCluster coordination, data replication4 TLA+ specs + Isabelle proofs, open-sourced
CockroachDBTransaction layerTLA+ specs in repository under docs/tla-plus/

Frequently asked questions

What does the Nw Formal Verification Tlaplus AI skill do?

TLA+ and PlusCal for specifying distributed system invariants. Decision heuristics for when formal verification adds value, key patterns, state explosion management, and alternatives comparison.

Why use Nw Formal Verification Tlaplus on TypingMind?

Because you install it once and use it with any model. Nw Formal Verification Tlaplus is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nw Formal Verification Tlaplus in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-formal-verification-tlaplus. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Formal Verification Tlaplus?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nw Formal Verification Tlaplus?

As many as you like. As long as a model supports skills, you can use Nw Formal Verification Tlaplus with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nw Formal Verification Tlaplus AI skill free?

Yes. It is published on GitHub by nWave-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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