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Use when verifying a running application end-to-end through its real interface — a web UI, a CLI, or a TUI — by writing and executing agent-run "scenario cards" against a freshly built instance with falsifiable assertions. Trigger on "test it end to end", "prove the UI actually works", "write/run a scenario", or after a change touches a user-facing surface that unit tests can't fully cover. Not for unit tests, pure code review, or API-only checks.

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 only after explicit Codex `$superloopy:superloopy-backend` or Claude Code `/superloopy:superloopy-backend` invocation, a backend task beginning with leading `loopy` or `루피`, or an active Superloopy loop routes backend work here. Relevant work includes backend application boundaries, APIs, persistence, migrations, transactions, background jobs, caches, events, reliability, security, and runtime database-agent tools. Plain API, server, database, SQL, or agent vocabulary does not activate this workflow.

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Analyze startups with Paul Graham mindset (pressure, founder-market fit, brutal validation), Dan Koe discipline (monetization, offer, distribution, sales), and Seth Godin principles (differentiation, narrative, smallest viable audience). Triggers on startup idea, business validation, MVP, first customers, revenue model, GTM, product-market fit, "I have an idea".

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。

PR review checklist for Terraform changes covering structure, state safety, security, naming, modules, variables, providers, and CI/CD. Use when reviewing Terraform PRs or doing pre-merge checks.

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 system bootstrapping and token generation. Takes project context and outputs ready-to-use design tokens, Tailwind config, and CSS variables.

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。

Audit NFR evidence for performance, security, reliability, and maintainability. Use when implementation evidence exists and the user says "audit NFR evidence", "audit NFRs", or "evaluate non-functional requirements"

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。

6-pillar architecture review framework. Adapted from AWS Well-Architected for use by great_cto's architect agent on every non-nano ARCH document. Forces explicit answers across operational excellence, security, reliability, performance, cost, and sustainability — not just feature design.

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。

Audits whether scores, benchmarks, LLM judges, or surveys measure what we think. Use when a benchmark is saturated, a score unreliable, or judge agreement untrustworthy.

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。

Designs and tests distributed-system resilience. Use when adding retries, deadlines, hedging, circuit breakers, overload protection, chaos experiments, or SLO reliability gates.

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。

Computes availability for redundant and series designs, failover, SPOFs, fault trees, FMEA, Weibull, and error budgets. Use when asking what uptime a design gives.

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Designs LLM, agent, responsible-AI, and multimodal evals. Use when measuring quality, fairness, grounding, safety, or judge reliability. Not for test harnesses (qa-agent-testing).

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Chooses DPO, GRPO/RLVR, reward models, and over-optimization controls. Use when adapting a pretrained or SFT model with preference or verifiable reward signals.

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 skill for building CLI applications with Cobra and Viper, authored by spf13 — the original creator of both libraries. Covers command-first architecture, decoupled business logic, configuration management, environment variable binding, context-aware commands, and in-memory CLI testing. Use whenever a Go CLI or command-line tool is being built, reviewed, or refactored — any mention of commands, subcommands, flags, or CLI configuration, even if Cobra or Viper aren't named yet (recommend them).

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 the user wants to turn a static scene, character images, aerial map, drawn path, or route-control image into an immersive first-person FPV AI video prompt, especially Seedance/Kling/Runway/Veo style one-shot videos with numbered stop markers, red-line path control, world-map flythroughs, camera route planning, variable character counts, non-human POVs such as drones, pets, robot vacuums, character references, timed interactions, dialogue, spatial audio, and negative constraints.

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。

Scan all GDDs against the entity registry to detect cross-document inconsistencies: same entity with different stats, same item with different values, same formula with different variables. Grep-first approach — reads registry then targets only conflicting GDD sections rather than full document reads.

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。

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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。

Verifying system resilience via load testing, contract testing, chaos engineering, and mutation testing. Use for limit verification, non-functional testing, or reliability validation.

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。

Refactoring code: variable naming, function extraction, magic number constants, dead code removal. Does not change behavior. Not for bugs/security (Judge), tests (Radar), or features (Builder).

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。

Running autonomous loops for nexus-autoloop. Generates script sets from goals, designs operation contracts, audits live loops, and recovers state — runners that complete reliably.

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Orchestrating multi-specialist task chains and scope-adaptive product delivery: classifies intent, selects and executes the minimum viable chain, aggregates results, and verifies acceptance criteria. For multi-domain tasks, build-first delivery, and product lifecycle execution.

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Author or improve a Skill (an executable, reusable capability with a ``SKILL.md`` plus optional ``scripts/``, ``references/``, and assets). Trigger this skill whenever the user wants to: - Capture a workflow they keep repeating into a fresh Skill - Polish an existing Skill's prompt, description, or examples - Tighten when-to-trigger wording so the model invokes the Skill more reliably - Discuss the Skill format, frontmatter, or how progressive disclosure works Do not wait for the user to say "skill-creator" verbatim — phrases like "turn this into a skill", "let's make a reusable thing for this", "improve my skill", or "tweak the description for this skill" should all reach 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 Nexscope market list capability to filter Amazon niche markets by category dimensions, supporting market size, competition, top concentration, seller structure, new product share, price/rating/margin ranges, and many other criteria for discovering viable markets and evaluating product selection directions. Trigger when the user mentions Amazon market research, niche category research, market opportunity screening, market concentration analysis, new product opportunities, market selection, Nexscope market research, category market research. Even if the user does not explicitly mention "Nexscope", if the need is to screen and evaluate Amazon markets by category dimensions, this skill should also be triggered.

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。

Build students' capacity to interrogate a historical source before reading — asking who authored it, when, why, and what this means for reliability. Use when students read documents without attending to authorship.

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。

Delivers changes incrementally in thin, verifiable slices. Use when implementing any feature or change that touches more than one file, or when picking up the next task from a plan. Use when rolling a change out behind a feature flag, when you're about to write a large amount of code at once, or when a task feels too big to land in one step.

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。