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Plans Convex schema and data migrations with widen-migrate-narrow and @convex-dev/migrations. Use for breaking schema changes, backfills, table reshaping, or zero-downtime rollouts.

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Apply helm chart updates for Bifrost. Detects config.schema.json changes since the last helm release, applies user-requested changes, updates values.yaml / values.schema.json / _helpers.tpl, bumps Chart.yaml version, updates helm README (Latest Version + Upcoming section), creates a docs MDX changelog, and updates docs.json navigation. Ends with a diff review to verify every helm field maps back to a config.schema.json counterpart. Invoked with /helm-update.

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Workaround for write_file failures caused by special characters (apostrophes, backticks, template literals) using a Python heredoc script, with mandatory post-write verification via read_file and cleanup of /tmp helper scripts.

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Reliable fallback technique for writing large or special-character-heavy file content when inline write_file or shell_agent calls fail with unknown errors — uses a Python helper script written to a temp file to avoid escaping and truncation pitfalls.

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Create a dashboard panel component using vanilla TypeScript DOM API, following the worldmonitor Panel architecture. Panels have a header with title/count, scrollable content area, loading/error states, and resize handles. Includes XSS-safe rendering pattern with esc() helper for safely interpolating untrusted external API data into innerHTML.

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Unified guide for authoring dashboard panel components in vanilla TypeScript — covers the authoritative Panel base class (grid-span persistence, dual resize handles, radar loading animation, exponential-backoff error countdown, data/new badge helpers, full destroy() cleanup), localStorage state via loadMap/saveMap, exponential-backoff retry, and CircuitBreaker integration. Shows how to wrap any existing fetchX() service call with CircuitBreaker.execute() as a one-liner and clarifies when each resilience layer is appropriate.

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Enrolls a project in OSS-Fuzz, Google's free continuous fuzzing service for open source, and drives it locally. Covers project.yaml, Dockerfile and build.sh setup, the helper scripts, reproducing OSS-Fuzz crash reports, and the acceptance criteria. Use when setting up continuous fuzzing for an open-source project, reproducing an OSS-Fuzz bug report, or testing an OSS-Fuzz build before submitting it.

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Comprehensive Windows privilege escalation methodology for offensive security engagements. Covers the full attack surface from a standard user shell to NT AUTHORITY\SYSTEM: token impersonation via SeImpersonate and SeAssignPrimaryToken privileges using JuicyPotato, PrintSpoofer, GodPotato, SweetPotato, and RoguePotato; service misconfigurations including unquoted service paths, weak service DACLs, writable service binaries, and insecure service creation permissions; AlwaysInstallElevated MSI exploitation; DLL hijacking through search order abuse, phantom DLL loading, and writable PATH directory injection; UAC bypass techniques via fodhelper.exe, eventvwr.exe, CMSTP, and environment variable manipulation; scheduled task abuse for writable task actions and new task creation; registry autorun exploitation for persistence and escalation; PrintNightmare (CVE-2021-34527) for remote and local privilege escalation; and credential harvesting from SAM database extraction, DPAPI blob decryption, LSA secret dumping, and Credential Manager enumeration. Integrates automated enumeration with WinPEAS, PowerUp, SharpUp, Seatbelt, and BeRoot. Each technique includes detection signatures and defender-side visibility for purple team operations. Maps to MITRE ATT&CK T1548 (Abuse Elevation Control Mechanism) and T1574 (Hijack Execution Flow). Designed for authorized penetration testing, red team engagements, and CTF competitions where you hold a standard user shell and need to escalate to SYSTEM or local Administrator.

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Internal prototype QA helper. Use only after a Product Design prototype, URL-to-code build, or image-to-code build has a source visual target and a rendered implementation to compare before handoff. Do not use for broad UX critique, design critique, product audits, or flow reviews; route those user-facing requests to audit.

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Draws hand-authored SVG spot illustrations in a bold pop-geometric style for design and coding scenes. Identifying traits: a thick navy outline on every shape, solid navy extrusions that always step down-right, saturated flat fills (hot pink, lemon, aqua, violet, mint, lime) textured with navy hatching and dots, and a dense overlapping cluster of chunky props (browser windows, pinwheel tiles, iso cubes, lightning bolts) around one slim cartoon character. Covers palette, line weights, extrusion and union-outline techniques, a JS helper for the repeated marks, characters and hands, composition and a render-and-critique loop. Use when someone asks for bold pop, pop geometric, retro pop, memphis-style, navy-outline, chunky outlined or loud colourful illustrations, or for empty states, onboarding, 404 or error pages, blog headers, feature spots and marketing cards in that look.

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Draws flat vector spot illustrations of tall, long-legged people doing design and coding things, with solid black as a main colour. Traits: no outlines on coloured fills; black as big solid shapes (hair, trousers, shoes, devices) and as thin detail lines; denim blue, leaf green, sun yellow and tomato red on cream skin; dot eyes, round glasses and blush; big shoes on one thin black ground line; two stars and two plus-sparkles as the only decor. Covers palette, line weights, characters, heads, hands, props, composition and a parts helper for limbs, shoes, heads, hands and paper props. Use when someone asks for flat illustration with black, flat people or character illustration, friendly product or SaaS illustration, or art for empty states, onboarding, 404 and error pages, blog headers, feature spots, marketing cards or app screens in this style.

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Write Python in n8n Code nodes (native Python, `language` pythonNative, n8n 2.x). Use when the user explicitly wants Python in a Code node, when migrating old Pyodide/"Python (Beta)" code that used _input/_json/_node/_now, or when a Python Code node fails with NameError, "Security violations detected", "Import of standard library module … is disallowed", "__build_class__ not found", "A 'json' property isn't a dictionary", or "Python runner unavailable". Covers the only two variables (_items/_item), dict-only access, imports blocked by default, the sandbox's denied builtins, accepted return shapes, and how errors interact with onError. JavaScript is the default for Code nodes — native Python has no n8n helpers and, by default, no imports. EXCEPTION — for Python in the AI-agent-callable Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode), use the n8n-code-tool skill instead (input is _query, return must be a string).

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Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool instead.

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Use when admin requests hand-creating or resetting a Worker, starting/stopping a Worker, managing Worker skills, enabling peer mentions, or opening a QwenPaw console. Use agentteams-find-worker only as a helper for Nacos-backed market import or when task assignment needs you to discover a suitable Worker.

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Use as a helper when the admin is assigning work but has not specified an existing Worker, so you need to search Nacos for a suitable Worker and ask whether to import it; also use when the admin explicitly says to import a Worker from the market or gives a direct nacos package URI.

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Convert .NET tests from xUnit.net v2/v3 to MSTest v4 while preserving VSTest or MTP. Use for replacing xunit packages, Fact/Theory/InlineData/MemberData, assertions, IClassFixture/ICollectionFixture, ITestOutputHelper, TestContext cancellation, traits/Owner, skips, timeouts, and xUnit parallelization. Also use when a "convert xUnit to MSTest" request may already be migrated: inspect and report the no-op. Do not use for xUnit v2-to-v3, MSTest upgrades, NUnit/TUnit conversion, or runner-only VSTest-to-MTP migration.

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Page Object Model patterns for Playwright — when to use POM, how to structure page objects, and when fixtures or helpers are a better fit.

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Use this skill when editing or creating CLI output, logging, warnings, error messages, progress indicators, or diagnostic summaries in the APM codebase. Activate whenever code touches console helpers (_rich_success, _rich_warning, _rich_error, _rich_info, _rich_echo), DiagnosticCollector, STATUS_SYMBOLS, CommandLogger, or any user-facing terminal output — even if the user doesn't mention "logging" or "UX" explicitly.

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Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization patterns.

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Review chart choice, labeling, accessibility, and storytelling quality for existing visualizations. Use as an explicit/manual helper for critique and cleanup, not as the main owner for scientific figure production or general chart generation.

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Split datasets into training, validation, and test partitions with the right stratification and temporal rules. Use as a narrow preprocessing helper once the broader ML workflow is already chosen, not as the main route owner for an end-to-end ML task.

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Produce a prioritized performance-optimization roadmap across frontend, backend, and infrastructure. Use as an explicit/manual helper after bottlenecks are known or suspected, not as the owner of regression detection, profiling capture, or test execution.

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Compute well-defined metrics from existing formulas, datasets, or test outputs. Use as an explicit/manual helper when the metric definition is already known, not for choosing the overall analysis owner or dashboard strategy.

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Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

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