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Write or update the CLI `--help` text and README section for a new or changed rezolus feature (a subcommand, flag, mode, or behavior change), then prove the help is usable by dispatching a fresh subagent that has never seen the code. Use this whenever you add or change how rezolus is invoked — new `rezolus <subcommand>`, new flags/args on an existing mode, renamed options, or changed defaults — and any time the user says the help/README is thin, stale, confusing, or "an agent couldn't figure this out." Treats help text as an interface under test, not prose.

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Generates complete video ad scripts for 15s, 30s, and 60s formats with shot-by-shot breakdowns, voiceover scripts, on-screen text, B-roll suggestions, music direction, storyboard outlines, and three proven video frameworks

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Full Ads Funnel Architect. Builds a complete advertising funnel across TOFU, MOFU, BOFU, and Retargeting stages with platform-specific templates, audience targeting, ad formats, copy angles, landing page requirements, KPIs, and budget allocation for Meta, Google, and LinkedIn.

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Fix silent video/media processing failures caused by URL extraction code that filters on file extensions (.mp4, .webm, .webp). Use when: (1) Media moderation, transcoding, or analysis silently skips files from Blossom or content-addressed storage servers, (2) URL extraction from Nostr event tags (imeta, r tags) drops URLs without recognized extensions, (3) CDN fallback URLs append .mp4 but the actual server uses extensionless content-addressed paths like /{sha256}. Common in Nostr video events (kind 34236) where different clients use different URL formats.

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Collect, normalize, and structure execution traces from instrumented programs (strace, ltrace) into JSON format for downstream analysis. Use when working with system call traces, library call traces, or execution logs that need to be analyzed for debugging, test case reproduction, or verification. Supports parsing strace/ltrace output, filtering noise, extracting debug information, and preparing traces for bug analysis or reproduction workflows.

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Establish explicit traceability between formal specifications (preconditions, postconditions, invariants) and verified code components with their correctness proofs. Produce structured Markdown mapping reports showing verification coverage and proof evidence. Use when auditing formal verification, documenting verified systems, establishing traceability for certification, or when the user asks to map specifications to code, generate verification reports, or analyze verification coverage in Coq, Dafny, Isabelle, or other proof assistants.

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Automatically updates a codebase to a new language version, framework version, or library update while ensuring all tests still pass. Use this skill when migrating Python 2→3, upgrading framework versions (React, Django, Angular), updating dependencies, or performing any version migration where tests define correct behavior. The skill analyzes failing tests caused by migration, categorizes errors (import errors, API changes, type errors, behavior changes), proposes systematic fixes, and verifies that test-observable behavior remains unchanged. Triggers when users ask to migrate code versions, upgrade dependencies while keeping tests passing, update framework versions, or perform test-guided version migrations.

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Generate implementation code and tests from written specifications. Use when the user provides specifications (natural language descriptions, formal specs, requirements documents, API specs) and asks Claude to implement the described functionality. Supports data structures, algorithms, classes, functions, and includes automatic test generation to validate implementation against specification.

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Generate formal specifications including preconditions, postconditions, invariants, and contracts from code or requirements. Use this skill when documenting APIs, creating formal verification annotations, defining function contracts, specifying class invariants, writing design-by-contract code, or preparing code for formal verification. Supports multiple specification languages including JML, ACSL, Dafny, Eiffel contracts, and documentation annotations.

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Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property testing).

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Detect and analyze areas with high maintenance cost, poor design, or accumulated technical debt. Use this skill when reviewing codebases for quality issues, planning refactoring efforts, conducting code audits, assessing project health, identifying maintenance hotspots, or prioritizing technical improvements. Analyzes code smells, architectural issues, dependency problems, test quality, documentation gaps, and provides actionable recommendations with priority rankings.

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Analyzes test suites and suggests optimal test execution order based on code change impact and project requirements. Use when optimizing CI/CD pipelines, deciding which tests to run before committing, or selecting critical tests under time constraints. Prioritizes tests by analyzing recently modified files, test dependencies, and coverage overlap. Outputs detailed rankings with priority scores and reasoning to help teams run the most impactful tests first.

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Creates visual representations of system structure including architecture diagrams, data flow diagrams, deployment diagrams, and sequence diagrams. Use when Claude needs to visualize system components, infrastructure, data flows, or interactions. Supports Mermaid (recommended for Markdown/GitHub), PlantUML (for detailed UML), and Graphviz/DOT (for complex networks). Trigger when users request diagrams, visualizations, or ask to "show", "diagram", "visualize", or "map out" system architecture, infrastructure, data flows, or component interactions.

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Analyzes test suites to identify redundant and duplicate test cases using coverage analysis, semantic similarity, and execution results. Use this skill when you need to reduce test suite size, identify redundant tests, optimize test execution time, analyze test coverage overlap, find tests with identical behavior, or improve test suite maintainability. Triggers when users ask to deduplicate tests, find redundant test cases, reduce test suite size, identify duplicate tests, or optimize test coverage.

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Automatically migrate Spring MVC applications to Spring Boot. Use when you need to modernize a Spring MVC project to Spring Boot while preserving functionality. The skill analyzes the codebase, updates build configuration (Maven/Gradle), migrates annotations, converts XML configuration to Java/properties, updates controllers and tests, and creates the Spring Boot main application class. Creates git commits for each migration phase and generates a comprehensive summary. Supports both Maven and Gradle projects.

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Identifies bug-introducing commits using SZZ-style analysis based on bug-fixing commits, commit history, and code blame information. Use this skill when you need to trace bugs back to their origin, identify which commits introduced bugs, analyze bug-fix commits to find root causes, perform software repository mining for bug analysis, or conduct empirical studies on software defects. Triggers when users ask to find bug-introducing commits, identify when a bug was introduced, trace bug origins, perform SZZ analysis, or analyze bug-fixing commits.

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Analyze formal verification artifacts (Isabelle, Coq, Dafny, etc.) and produce structured reports identifying the precise boundary between verified, assumed, and unverified components. Use when assessing verification coverage, understanding trust boundaries, auditing formal proofs, or documenting verification scope. Reports explicitly list verified code, assumptions, axioms, trusted computing base, and unverified components. Conservative and explicit about verification status without attempting to repair or mask gaps.

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Automatically generate TLA+ specifications from source code (C/C++, Python) for formal verification of distributed systems. Use when users need to: (1) Generate TLA+ specs from program implementations, (2) Model distributed systems, consensus protocols, or concurrent algorithms, (3) Extract state variables, actions, and invariants from code, (4) Create formal specifications for model checking with TLC, (5) Verify safety and liveness properties of distributed systems. Particularly effective for message-passing systems, replication protocols, consensus algorithms, and distributed transactions.

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Generate comprehensive test case documentation from test code, test framework output, existing test docs, and source code context. Use when documenting test suites, creating test specifications, generating test coverage reports, onboarding developers to testing practices, or preparing QA documentation. Analyzes test functions (pytest, unittest) to extract test names, docstrings, assertions, and organization, then produces structured markdown with both overview-level summaries and detailed test case specifications including purpose, preconditions, steps, expected results, and test data. Triggers when users ask to document tests, generate test specifications, create test reports, summarize test coverage, or explain what tests do.

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Automatically simplify and minimize TLA+ specifications by reducing redundant state variables, merging equivalent actions, and minimizing invariants while preserving specified properties. Use when working with TLA+ specifications that need optimization, simplification, or reduction. Triggers when users ask to minimize, reduce, simplify, or optimize TLA+ specs, or when they want to remove redundancy from formal specifications while maintaining semantic equivalence.

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Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.

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Extract language-agnostic pseudocode from formally verified programs (Isabelle/HOL, Coq) while preserving verified control flow, data dependencies, and algorithmic logic. Use when: (1) Users have verified code and need readable pseudocode, (2) Documenting verified algorithms for broader audiences, (3) Translating verified implementations to other languages, (4) Creating algorithm specifications from verified code, (5) Preserving verification guarantees in pseudocode form, or (6) Abstracting proof-heavy code to essential logic. Maintains semantic faithfulness to verified implementation.

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Generate implementation code that passes existing unit tests. Use when the user provides test files (Python pytest/unittest, Java JUnit/TestNG) and asks Claude to implement the code to make those tests pass. Supports full TDD workflow - analyzing tests, generating implementation, running tests, debugging failures, and iterating until all tests pass.

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Generate boilerplate code and project templates/skeletons automatically. Use when: (1) Creating new projects from scratch (React app, FastAPI backend, Express API), (2) Generating repetitive code patterns (CRUD endpoints, models, controllers), (3) Scaffolding components, services, or modules, (4) Creating test boilerplate, (5) Setting up monorepo structures. Provides project templates and code generation patterns for common development tasks.

日本語の概要は準備中です。原文の説明を表示しています。