dust-breaking-changes
CRITICAL guideline - Never introduce breaking changes to the private API without explicit user approval. Always warn and ask for validation first.
CRITICAL guideline - Never introduce breaking changes to the private API without explicit user approval. Always warn and ask for validation first.
MANDATORY guideline - Always keep Swagger documentation in sync when modifying API endpoints or schemas.
Guides Docker, CI/CD pipelines, deployment strategies, infrastructure as code, and observability setup. Use when writing Dockerfiles, configuring GitHub Actions, planning deployments, setting up monitoring, or when asked about containers, pipelines, Terraform, or production infrastructure.
When researching, classifying, drafting, or logging general SuperPlane issues (bug, enhancement, feature, papercut). Use for natural-language issue descriptions, tmp/pm_logger drafts, and optional GitHub MCP logging with labels.
Write and review concise product requirement documents for this repository. Use when the user asks to create, improve, or validate a PRD, scope a feature, or document goals, non-goals, requirements, risks, and metrics.
Provides open source intelligence techniques for CTF challenges. Use when gathering information from public sources, social media, geolocation, DNS records, username enumeration, reverse image search, Google dorking, Wayback Machine, Tor relays, FEC filings, or identifying unknown data like hashes and coordinates.
MUST READ before writing or modifying ADK agent code. ADK API quick reference for Python — agent types, tool definitions, orchestration patterns, callbacks, and state management. Includes an index of all ADK documentation pages. Do NOT use for creating new projects (use adk-scaffold).
Generate code examples and usage patterns for the string-ts library. Use when writing examples, documentation, demos, answering questions about string-ts API, or helping users understand how to use string-ts for type-safe string manipulation, case conversion, object key transformation, environment variables, and payload transformation.
Code quality patterns and guidelines for ToolUniverse tool development. Apply when writing, fixing, or refactoring tool Python code in the ToolUniverse project. Encodes lessons from 80+ debug rounds. Use alongside devtu-fix-tool and devtu-self-evolve. Triggers: implementing tool fixes, writing new tool classes, reviewing tool code quality, checking schema correctness, looking up API-specific bug fixes.
TOP PRIORITY skill — find and immediately fix or remove every piece of wrong, outdated, or redundant information in ToolUniverse docs. Wrong code, broken links, incorrect counts, and overlapping instructions must be fixed or removed — never left in place. Runs five phases: (D) static method scan, (C) live code execution, (A) automated validation, (B) ToolUniverse audit, (E) less-is-more simplification. Core philosophy: each concept appears exactly once; remove don't add; no emojis; single setup entry point. Use when reviewing docs, before releases, after API changes, or when asked to audit, fix, or simplify documentation.
Find and evaluate research datasets for any scientific question. Teaches how to reason about data needs, search across public repositories, evaluate dataset fitness, and identify access requirements. Use whenever users ask to find data, search for datasets, identify cohort studies, or need data for analysis. Also use when users ask about a specific survey or cohort (NHANES, HRS, UK Biobank, TCGA, etc.), when they want to know what data exists for a research question, or when they need to compare available data sources. If the user mentions "where can I get data" or "is there a dataset for X", this is the right skill.
Audit and fix HTML accessibility issues including ARIA labels, keyboard navigation, focus management, color contrast, and form errors. Use when adding interactive controls, forms, dialogs, or reviewing WCAG compliance.
Guide for creating effective skills. Use when creating or updating a skill.
Install Codex skills from curated sources or GitHub paths.
Manage the Grounded Docs MCP Server documentation index. Covers scraping and indexing documentation from URLs or local files, refreshing existing indexes with changed content, and removing libraries from the index. Use when you need to add, update, or delete indexed documentation.
Search and query the Grounded Docs MCP Server documentation index. Covers listing indexed libraries, searching documentation content, and resolving library versions. Use when you need to look up API references, find code examples, or check which documentation is available in the local index.
Write and update the VitePress documentation website for stock indicators. Use when adding a new indicator page, updating an existing indicator page, or making structural changes to the docs site.
Use after implementing features, changing configuration, modifying CLI commands, updating router behavior, altering architecture, or making any user-facing changes. Triggers a documentation review to check if docs-website/ needs updating. Use this skill whenever you finish writing code that changes how users interact with the platform — new flags, config options, API changes, new UI features, behavioral changes, deprecations, or new components. Even small changes like adding an environment variable or a new CLI flag need doc coverage.
Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparing skill versions, or creating ground truth datasets with the Generate-Review-Promote (GRP) pipeline. Triggers include "test skill", "evaluate skill", "skill regression", "ground truth", "GRP pipeline", "skill quality", and "skill metrics".
This skill is used for segmented reading and organization when facing large-scale knowledge bases or web pages. It captures original content segment by segment, summarizes key points in real-time, and continuously deposits them into the knowledge base, ensuring orderly information ingestion, clear structure, and traceability.
Search and recall relevant memories from past sessions. Use when the user's question could benefit from historical context, past decisions, debugging notes, previous conversations, or project knowledge.
Search and recall relevant memories from past sessions. Use when the user's question could benefit from historical context, past decisions, debugging notes, previous conversations, or project knowledge.