Explorar skills

Updates Python test code to work with new versions of the code being tested. Use when Claude needs to: (1) Update tests after code changes, (2) Fix broken tests due to signature changes, (3) Update assertions to match new behavior, (4) Add test cases for new functionality, (5) Analyze code differences and their test impact, (6) Run tests and fix failures based on error messages. Takes old code, ne

Design and review APIs with suggestions for endpoints, parameters, return types, and best practices. Use when designing new APIs from requirements, reviewing existing API designs, generating API documentation, or getting implementation guidance. Supports REST APIs with focus on endpoint structure, request/response schemas, authentication, pagination, filtering, versioning, and OpenAPI specificatio

Automatically analyzes configuration files to detect inconsistencies, conflicts, missing keys, and divergent values across environments, versions, or modules. Use when managing multi-environment configurations, detecting config drift, validating configuration changes, or ensuring consistency across microservices. Supports JSON, YAML, TOML, INI, XML, .env, and properties files. Identifies security

Extract programming-language-agnostic pseudocode from source code in any language, preserving control flow and logical structure while filtering out implementation details. Use when the user asks to convert code to pseudocode, abstract code logic, understand code structure without syntax, create language-independent documentation, or analyze algorithmic flow without language-specific details.

Automatically derives TLA+ properties (invariants, safety, liveness) from natural-language requirements or structured requirement documents. Resolves ambiguities, asks clarifying questions for underspecified requirements, and outputs TLA+-compatible property definitions with semantic explanations. Use when translating system requirements, specifications, or behavioral constraints into formal TLA+

Identifies non-deterministic or unreliable tests through static code analysis and test result analysis. Use when Claude needs to find flaky tests, analyze test reliability, or investigate intermittent test failures. Supports Python (pytest, unittest) and Java (JUnit, TestNG) test frameworks. Trigger when users mention "flaky tests", "intermittent failures", "non-deterministic tests", "unreliable t

Performs abstract interpretation over source code to infer possible program states, variable ranges, and data properties without executing the program. Reports potential runtime errors including out-of-bounds accesses, null dereferences, type inconsistencies, division by zero, and integer overflows. Use when analyzing code for potential runtime errors, performing static analysis, checking safety p

Identify bug-introducing commits using semantic analysis that extends traditional SZZ algorithm. Distinguishes semantic changes from refactorings or code movements using control-flow and data-flow similarity analysis. Use when analyzing bug-fix commits to trace back to bug-introducing changes, investigating software evolution, conducting empirical studies on defect prediction, or reducing false po

Checks whether a new version of a repository preserves the behavior observed by tests on the old version. Use this skill when comparing two versions of code to detect regressions, verify refactoring safety, validate bug fixes don't break existing functionality, or ensure backward compatibility. Detects differences in function outputs, exceptions, observable states, and performance between versions

Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Py

Generate GitHub Actions deployment workflows for automated deployment to staging and production environments on cloud platforms (AWS, GCP, Azure). Use when setting up continuous deployment pipelines, creating deployment automation, or configuring multi-environment deployment strategies. Includes templates for environment-specific deployments with approval gates, secrets management, and rollback ca

Generate abstract Control Flow Graph (CFG) representations of programs showing loops, branches, and function calls for static analysis or verification. Use when users need to: (1) Visualize program control flow structure, (2) Generate CFGs for static analysis tools, (3) Create control flow abstractions for formal verification, (4) Analyze program paths and reachability, (5) Document program struct