Generate bash script templates with best practices including error handling, argument parsing, logging, and portability considerations.
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Generate bash script templates with best practices including error handling, argument parsing, logging, and portability considerations.
日本語の概要は準備中です。原文の説明を表示しています。
Generate argument validation logic with type coercion, constraints, custom validators, and helpful error messages for CLI applications.
日本語の概要は準備中です。原文の説明を表示しています。
Generate Zod/JSON Schema configuration validators with defaults and error messages.
日本語の概要は準備中です。原文の説明を表示しています。
Design agents for multi-turn tool use — sequential tool calls, result accumulation, error recovery, and complex task decomposition over multiple turns.
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Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.
日本語の概要は準備中です。原文の説明を表示しています。
Master Rust async programming with Tokio, async traits, error handling, and concurrent patterns. Use when building async Rust applications, implementing concurrent systems, or debugging async code.
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You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based practices. Design SLO frameworks, define SLIs, and build monitoring that balances reliability with delivery velocity.
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Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.
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Coordinate specialized code, security, architecture, and performance reviewers and consolidate their results. Use when a codebase needs parallel review across multiple disciplines.
日本語の概要は準備中です。原文の説明を表示しています。
Expert SRE incident responder specializing in rapid problem resolution, modern observability, and comprehensive incident management. Masters incident command, blameless post-mortems, error budget management, and system reliability patterns. Handles critical outages, communication strategies, and continuous improvement. Use IMMEDIATELY for production incidents or SRE practices.
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose software failures using observability data, ranked hypotheses, and targeted instrumentation. Use when the cause is unclear or an issue requires systematic investigation.
日本語の概要は準備中です。原文の説明を表示しています。
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
日本語の概要は準備中です。原文の説明を表示しています。
Validate application configuration with schemas, environment checks, and runtime tests. Use when preventing configuration errors, checking deployment settings, or detecting configuration drift.
日本語の概要は準備中です。原文の説明を表示しています。
Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.
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Explain any topic, code, concept, or error tailored to a specific audience's level of understanding. Use this skill whenever the user says 'explain like I am', 'ELI5', 'explain this to my', 'break this down for', 'dumb it down', 'simplify this for', or asks you to explain something to a specific person or audience type (e.g., 'explain this to a manager', 'how would I explain this to my mom', 'make this understandable for a 5th grader'). Also trigger when the user mentions wanting to understand something at a particular level, or asks for an explanation targeting a non-technical audience. Even partial matches like 'explain to my wife' or 'tell my boss' should trigger this skill.
日本語の概要は準備中です。原文の説明を表示しています。
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
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Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.
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Structure a Godot 4.7 project with the scene tree and node composition: build reusable scenes, instance PackedScenes at runtime, navigate the tree safely, and register autoload singletons. Use when designing .tscn scenes, deciding how to split nodes, spawning instances with instantiate(), wiring autoloads, or fixing "node not found"/freed-node errors in a Godot project.
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Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
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Use when a test fails and you need to diagnose the root cause. Run the test, read errors, trace through generated and source code, fix, and verify.
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Root-cause a failing or wrong empirical result with a disciplined reproduce → minimise → hypothesise → instrument → fix loop, instead of guessing-and-poking. Use when the user says "why is my regression wrong", "this number changed", "my script errors out", "the result won't reproduce", "debug this", "this estimate looks wrong", or "it worked yesterday". Tuned for research code (R/Stata/Python): type coercion, NA/merge blow-ups, factor levels, clustering/SE choices, weighting, collinearity/convergence, seeds, package-version drift. Use `--no-fix` to localize the root cause without editing shared or load-bearing files.
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Read-only proofreading pass over lecture `.tex` or `.qmd` files. Checks grammar, typos, overflow, terminology consistency, and academic writing quality; produces a report without editing. Use when user says "proofread", "check for typos", "look for grammar issues", "copy-edit this", "any writing errors?", or before a lecture release.
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Run the full R package release gate — regenerate docs, run the test suite, run R CMD check --as-cran, and triage every ERROR / WARNING / NOTE against CRAN policy before a release or submission. Use when the user says "check my R package", "R CMD check", "is this package CRAN-ready", "run devtools::check", "prepare for CRAN submission", or points at a directory containing a DESCRIPTION file. Produces a check report + CRAN-submission checklist in `quality_reports/`.
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Scaffold and run a reproducible Monte Carlo simulation study in R — a declared assumption regime, a parameterized DGP, an estimator grid, a seeded replication loop, and a summary of bias, RMSE, empirical SE, coverage, size/power with Monte Carlo standard errors. Use when the user says "run a Monte Carlo simulation", "simulation study", "check the bias/coverage of an estimator", "compare estimators in simulation", "size and power simulation", "Monte Carlo experiment", or wants to demonstrate an estimator's finite-sample properties. Produces a numbered R script in `scripts/R/` and saves per-replication raw results + a summary table to `output/`.
日本語の概要は準備中です。原文の説明を表示しています。