Frontend-specific technical decision criteria, anti-patterns, debugging techniques, and quality check workflow. Use when making frontend technical decisions or performing quality assurance.
Quantify values with uncertainty bounds. Use when estimating metrics, calculating risk scores, assessing magnitude, or measuring any quantifiable property.
Automated analysis of patent claims for USPTO compliance with 35 USC 112(b) requirements - antecedent basis, definiteness, claim structure
Identify and clean up stale git branches locally and on remotes with safe, reversible steps. Use when asked to prune, list, or delete merged/old branches or audit branch hygiene.
React/TypeScript frontend development rules including type safety, component design, state management, and error handling. Use when implementing React components, TypeScript code, or frontend features.
Guides subagent coordination through implementation workflows. Use when orchestrating multiple agents, managing workflow phases, or determining autonomous execution mode. Defines scale...
Ingest and parse incoming messages, events, or signals into structured form. Use when processing external inputs, handling API responses, parsing webhook payloads, or ingesting sensor data.
Reviews URLSession networking code for iOS/macOS. Covers async/await patterns, request building, error handling, caching, and background sessions.
Language-agnostic testing principles including TDD, test quality, coverage standards, and test design patterns. Use when writing tests, designing test strategies, or reviewing test quality.
Reviews Swift code for concurrency safety, error handling, memory management, and common mistakes. Use when reviewing .swift files for async/await patterns, actor isolation, Sendable conformance,...
Assign labels or categories to items based on characteristics. Use when categorizing entities, tagging content, identifying types, or labeling data according to a taxonomy.
Plan work before coding: do repo research, analyze options/risks, and ask clarifying questions before proposing an implementation plan. Use when the user asks for a plan, design/approach, scope...
Performs metacognitive task analysis and skill selection. Use when determining task complexity, selecting appropriate skills, or estimating work scale. Returns skills with confidence scores and metadata.
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Establish cause-effect relationships between events or states. Use when analyzing root causes, mapping dependencies, tracing effects, or building causal models.
Explanation documentation patterns for understanding-oriented content - conceptual guides that explain why things work the way they do
Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or...
Find failure modes, edge cases, ambiguities, and exploit paths in plans, code, or designs. Use when reviewing proposals, auditing security, stress-testing logic, or validating assumptions.
Guide a safe git rebase of the current branch onto a target branch, including conflict triage and resolution steps. Use when asked to rebase, update a branch, or resolve rebase conflicts.