Automatically generates executable test cases from model checking counterexample traces. Translates abstract counterexample states and transitions into concrete test inputs, execution steps, and assertions that reproduce property violations. Use when working with model checker outputs (SPIN, CBMC, NuSMV, TLA+, Java PathFinder, etc.) and needing to create regression tests, validate bug fixes, or reproduce verification failures in executable test suites.
npx skills add https://github.com/ArabelaTso/Skills-4-SE --skill counterexample-to-test-generator
This skill transforms counterexample traces from model checkers into executable test cases that reliably reproduce property violations. It bridges formal verification and testing by mapping abstract states to concrete values and generating runnable tests with clear traceability from counterexample steps to test logic.
Gather and understand the counterexample trace and program:
Translate the counterexample's abstract representation into concrete test inputs:
Create the test case framework in the target language:
Write the executable test code:
// Initialize variables to counterexample initial state
// Set up test fixtures or mocks
// Configure environment (if needed)
// Step 1 (CE line X): [description]
// Execute operation with concrete values
// Step 2 (CE line Y): [description]
// Execute next operation
// Verify property violation (CE line Z)
// Assert expected failure condition
// Check final state matches counterexample
Produce the complete test case with documentation:
Input: SPIN counterexample showing a deadlock in a concurrent system
Output:
// test_deadlock.c - Reproduces deadlock from SPIN counterexample trail
#include <pthread.h>
#include <assert.h>
// Counterexample mapping:
// CE Step 1-2: Thread 1 acquires lock A
// CE Step 3-4: Thread 2 acquires lock B
// CE Step 5: Thread 1 waits for lock B (blocks)
// CE Step 6: Thread 2 waits for lock A (deadlock)
void* thread1_func(void* arg) {
pthread_mutex_lock(&lock_a); // CE Step 1
sleep(1); // CE Step 2 (timing)
pthread_mutex_lock(&lock_b); // CE Step 5 (blocks)
// ... rest of test
}
void test_deadlock_scenario() {
// Setup: Initialize locks (CE initial state)
pthread_mutex_init(&lock_a, NULL);
pthread_mutex_init(&lock_b, NULL);
// Execute: Create threads in counterexample order
pthread_create(&t1, NULL, thread1_func, NULL);
pthread_create(&t2, NULL, thread2_func, NULL);
// This test will hang, demonstrating the deadlock
pthread_join(t1, NULL); // Will timeout
}
Challenge: Counterexample uses symbolic values without concrete bounds
Solution: Use representative values from the domain, document the choice
Challenge: Trace involves complex timing or scheduling
Solution: Use synchronization primitives or explicit delays to enforce ordering
Challenge: Program state is partially specified in counterexample
Solution: Initialize unspecified variables to default/neutral values
Challenge: Counterexample is very long
Solution: Identify the minimal prefix that still triggers the violation
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take arabelatso/counterexample-to-test-generator from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.