Testing & Debugging for Claude Code

Hunt WP plugins via REST, exploit CVEs when version known.

Batch WP recon: users, CORS, XMLRPC, leaks across domains.

Scripts and workflows to batch-test popular WordPress plugin CVEs across hundreds of domains. Covers automated plugin detection, version extraction from readme.txt, CVE matching against a curated matrix of high-impact plugin vulnerabilities (ElementsKit, Revslider, WPDM, Gravity Forms, Contact Form 7, Jetpack, WP File Manager, GSpeech), and exploitation PoC generation. Use after initial WordPress detection recon — when you have a target list of WP domains and need to find which specific plugin C

Systematic approach to finding and testing CVEs for identified WordPress plugins. Covers plugin discovery, version extraction from multiple sources (readme.txt, assets, inline JS), CVE database cross-referencing with WPScan/Patchstack/NVD/NVD API, version-based vulnerability matching, exploitation PoC generation, and false-positive elimination. Built from field experience finding exploitable plugin CVEs across 58-company mass recon including ElementsKit (CVE-2023-6851/CVE-2023-6853), Revslider (

Scan WordPress REST API plugin endpoints for unauthenticated state-changing operations — discover write endpoints (POST/PUT/PATCH/DELETE) exposed without auth, enumerate all plugin routes, and test for unauthorized content publishing, settings modification, and data leakage.

Exploit XMLRPC multicall, pingback for brute force and SSRF.

Zimbra SOAP user enum, CVE-2022-37042, SSRF when webmail.

"Ideas are cheap. Execution is everything."

Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D model

Help debug code by analyzing error messages, identifying root causes, and providing fix suggestions.

Automatically generate unit tests based on source code, supporting multiple languages and testing frameworks.

You are a coding agent from the Hello World extension.

161 Apple platform development skills across 23 categories (iOS, macOS, watchOS, visionOS): code generators, product workflow, App Store/ASO, testing and TDD, design, performance, security, and more. Surfaced as 23 category skills that route to the full library.

Rigor Analyze / Rigor Audit read-only skill for deep learning research repositories. Use when the user wants to read and understand a repository, inspect model structure and training or inference entrypoints, review configs and insertion points, or flag suspicious implementation patterns without modifying code or running heavy jobs. Do not use for active command execution, broad refactoring, speculative code adaptation, or automatic bug fixing.

Run Rigor Analyze on this repository using the current compatible slug.

Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted bas

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, co

Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing R

Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

Run Rigor Debug on a research repository failure before patching.

Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.

Remembers approaches you tried and reverted (with reason and estimated token cost) and warns before you retry them — via a prompt-submit card and an ask-before-edit guard.

Stamps a provenance receipt — prompts, estimated spend, tests run with exit codes, and a truthful agent-authored line split — into your PR body on `gh pr create`.

Skills and agents for working with Ark - testing, setup, analysis, and research

Analyze the specified component and provide recommendations

Show a summary of potential naming clashes detected in the codebase with analysis.

Debug context-tools plugin status

Finalize / repair subagent

Fix identified issues in the specified component

Resolve Naming Clashes

Troubleshooting guide for MCP server configuration (auto-configures by default)

Control Firefox for browsing, web testing, and debugging. Fill forms, capture network and console activity, take screenshots, run scripts, and profile performance. Supports Android devices.

Diagnose and fix Firefox DevTools MCP setup issues. Use this if the firefox-devtools-mcp plugin is not working.

Semantic code search using mgrep for efficient codebase exploration. This skill should be used when searching or exploring codebases with more than 30 non-gitignored files and/or nested directory structures. It provides natural language semantic search that complements traditional grep/ripgrep for finding features, understanding intent, and exploring unfamiliar code.

Evaluates code against Apple's App Store Review Guidelines for iOS, macOS, tvOS, watchOS, and visionOS apps

Skills for x64dbg debugger automation — state snapshots, memory analysis, and more

Build and manage iOS/macOS apps using native xcodebuild and xcrun simctl CLI tools. Replaces XcodeBuildMCP with direct CLI commands and XCUITest for UI automation.

Closed-book assessor for the learnbiostats studio. Probes mastery of a module's claims through Socratic, no-notes questioning across Bloom levels, detects misconceptions, bands the result on the Dreyfus-flavored mastery scale, and schedules the next spaced-repetition surfacing. Grounded in the mastery-assessment skill. Use to assess a module the tutor has flagged as ready (`curriculum/modules/pNmM-*.md` + its question bank), to run a spaced-repetition review of due evergreen notes, or to check w