Translates Python into Lean 4 for interactive theorem proving, handling dynamic types and duck typing by specializing to the concrete types actually used. Use when proving correctness of a Python algorithm beyond what testing can establish, or when building a verified reference for numerical or combinatorial Python code.

Raises test coverage by identifying uncovered code regions, ranking them by risk, and generating targeted tests that hit them — prioritizing branches and conditions over raw line count. Use when coverage is below target, when untested code is blocking a release, or when deciding which tests to write next.

Reviews and designs API contracts — function signatures, REST endpoints, library interfaces — for usability, evolvability, and the principle of least surprise. Use when designing a new public interface, when reviewing an API PR, when the user asks whether a signature is well-designed, or when planning a breaking change.

Generates concrete scenarios from a requirement — happy paths, edge cases, and error conditions — expressed as Given/When/Then or equivalent structured narratives. Use when turning a requirement into acceptance tests, when exploring what could go wrong, or when the requirement is abstract and needs grounding.

Detects ambiguity in natural-language requirements — weak words, dangling references, underspecified quantities, conflicting interpretations — before they become implementation bugs. Use when reviewing requirements, when a spec uses words like "appropriate" or "fast", or when two engineers read the same requirement and built different things.

Generates CI pipeline configs by analyzing a repo's structure, language, and build needs — GitHub Actions, GitLab CI, or other platforms. Use when bootstrapping CI for a new repo, when porting from one CI to another, when the user asks for a pipeline that builds and tests their project, or when wiring in security gates.

Generates a structured CHANGELOG.md from VCS history and PR/issue references, categorized by change type. Use when cutting a release, when the user asks to update CHANGELOG.md, or when backfilling a changelog from git history.

Translates specifications into temporal logic formulas (LTL, CTL, or TLA) by matching the specification's shape to the right logic and operators. Use when formalizing requirements for any model checker, when choosing between LTL and CTL for a property, or when the user has a temporal claim and doesn't know which operators express it.

Orders tests so failures surface earliest — runs tests covering changed code first, historically flaky/failing tests early, and slow low-value tests last. Use when the suite is too slow to run in full on every change, when CI feedback takes too long, or when deciding what to run in a smoke-test tier.

Produces natural-language summaries of what code does at the function, class, module, or subsystem level, with length and abstraction scaled to the scope. Explains purpose, side effects, and non-obvious behavior rather than restating syntax. Use when onboarding to unfamiliar code, when the user asks what something does, when generating docstrings or architecture notes, or when preparing a handoff document.

Compares the runtime behavior of two or more versions of the same code by running them on identical inputs and diffing outputs, side effects, and errors. Use when validating a refactor, port, or optimization; when the user asks if two implementations behave the same; or when investigating a suspected regression across versions.

Z.AI CLI providing: - Vision: image/video analysis, OCR, UI-to-code, error diagnosis (GLM-4.6V) - Search: real-time web search with domain/recency filtering - Reader: web page to markdown extraction - Repo: GitHub code search and reading via ZRead - Tools: MCP tool discovery and raw calls - Code: TypeScript tool chaining Use for visual content analysis, web search, page reading, or GitHub exploration. Requires Z_AI_API_KEY.

Audit any website for AI/agent-friendliness using AgentLint. Run `npx @cjavdev/agent-lint` with a URL to scan a site across 17 rules in 5 categories (transport, structure, tokens, discoverability, agent), get a 0-100 AgentScore with letter grade, and receive a prioritized remediation plan. Use this skill when: auditing a site for AI readiness, checking if a site has llms.txt or markdown support, improving a website's agent-friendliness score, fixing AgentLint violations, or understanding what makes a site AI-friendly. Trigger phrases: 'run agentlint', 'audit site for AI', 'check agent-friendliness', 'agentlint scan', 'AI-friendly audit', 'check llms.txt', 'agent readiness'.

AWS S3 object storage for bucket management, object operations, and access control. Use when creating buckets, uploading files, configuring lifecycle policies, setting up static websites, managing permissions, or implementing cross-region replication.

Remove AI-generated code slop from the current branch. Use after writing code to clean up unnecessary comments, defensive checks, and inconsistent style.

Use when writing prose humans will read—documentation, commit messages, error messages, explanations, reports, or UI text. Applies Strunk's timeless rules for clearer, stronger, more professional writing.

Show ponytail's measured impact as a compact scoreboard: less code, less cost, more speed, from the benchmark medians. One-shot display, not a persistent mode, and not a per-repo number. Trigger: /ponytail-gain, "ponytail gain", "what does ponytail save", "show ponytail impact", "ponytail scoreboard".

Convert existing Artillery YAML scripts to TypeScript or ESM JavaScript, auto-detecting the best output format, resolving YAML anchors by inlining, type-annotating exports, and verifying the output via tsc or node --check. Use when the user wants to migrate, rewrite, or translate Artillery load-test scripts from YAML to TS/JS for better IDE support, type checking, and composability.

Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.

Use when building or reviewing Micronaut applications — Micronaut.run bootstrap, @Singleton/@Prototype, @Factory beans, @ConfigurationProperties, environments, @Requires, @Controller vs services, @Scheduled, graceful shutdown, @ExecuteOn for blocking work, and Jakarta-consistent APIs. This should trigger for requests such as Review Java code for Micronaut application structure and beans; Apply best practices for Micronaut configuration, @Requires, and factories; Improve scheduling, shutdown, or threading in Micronaut services. ; Review Micronaut dependency injection scopes and factories; Configure Micronaut @Requires conditions for environments. Part of Plinth Toolkit