「case」の検索結果

Argues the strongest honest case against a decision, plan, belief, hire, pricing move, strategy, or piece of work the user is leaning toward. Steelmans the other side, ranks at most three objections by consequence, runs a short pre-mortem, credits what the user got right, and ends with what would have to be true for their choice to work. Calibrated and collegial, never combative; says plainly when the position is strong. Use whenever the user says "play devil's advocate", "argue the other side", "poke holes in this", "what am I missing", "am I making a mistake", "talk me out of it", "stress-test this idea", "tell me why this is a bad idea", or asks for pushback on something they have mostly decided. Supports gentle, standard, and hard intensity. Not for interrogating requirements before a build (grill-the-brief) or staging a multi-persona debate (debate-simulator).

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。

Guide minimal code via a decision ladder with full safety, edge, and negative-case coverage. Use when adding code, choosing a dependency, or auditing a diff.

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 drafting a medical research manuscript or any IMRAD section. Runs an 8-phase pipeline from outline to submission-ready draft for original articles, AI validation studies, case reports, meta-analyses, technical notes and more. Checking a draft is /self-review.

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 planning how many patients or cases a study needs before data collection (power analysis, IRB justification). Walks a decision tree to the right test and returns reproducible R/Python code and IRB-ready justification text. Analyzing collected data is /analyze-stats.

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。

RCE hunting - template injection, YAML/XML deserialization, dependency confusion, Kubernetes surfaces, CVE-specific exploits (Apache CVE-2021-41773, Spring CVE-2022-22963). OOB-mandatory for blind cases. Wiki-first, FIND schema 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。

Coach the user through actually selling: finding the champion and the buyer, running the first sales conversations, demoing their use case, handling "let me think about it," and closing the first paying customers yourself. Use when developers love it but nobody pays, you've never sold anything and freeze on the conversation, or deals keep stalling on "we'll think about it."

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 PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments. Includes use-case matching matrix, performance comparison, and GDPR alignment assessment for each technology.

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。

Classifies personal vs non-personal data per GDPR Art. 4(1) definition test with decision tree for borderline cases. References Breyer v Germany CJEU C-582/14 dynamic IP ruling and WP29 Opinion 4/2007. Keywords: personal data, GDPR Art 4, data classification, Breyer ruling, identifiability test, PII.

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。

Evaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests.

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。

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

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。

OI-3 spike harness — ADVERSARIAL inline guidance variant. Worst-case anchor: ends with a final-sounding completion statement and gives NO instruction to return to the caller. Use only when a spike consumer skill invokes it.

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。

Produce a standalone test plan by analyzing code for test coverage gaps and edge cases. Use when you need to create, generate, or draft a test plan for a branch, need to analyze test coverage, or need to identify what tests to write for specific files or directories. Does not produce a plain-language plan for a person to run tests by hand — use manual-test-planning for that. Does not write test code — use tdd to implement behavior test-first. Does not refine existing plans — use iterative-plan-review. Does not review code quality, security, or style — use code-review for full code review. Does not evaluate architectural testability or structural coupling — use architectural-analysis for architectural assessment.

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。

Builds a new Claude Code skill from scratch through a relentless, evidence-based interview that walks the skill's design tree decision-by-decision — entity fit, use cases, name, description, workflow steps, tools, and progressive-disclosure layout — then reviews the finished skill against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new skill or slash command. Does not build an agent or subagent — use agent-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.

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。

OI-3 spike harness — heavy consumer, ADVERSARIAL arm. Worst-case early-exit test: the mid-workflow Skill call has no continuation guardrail and the guidance skill ends with a final-sounding anchor. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.

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。

Plan, produce, or diagnose evidence-backed product demo videos and screen-recorded promotional walkthroughs. Use when the user asks to make a product demo, launch video, feature showcase, app walkthrough, demo reel, or polished recording; wants every important capability shown without a slow feature tour; asks to remove dead time or fix narration-to-action pacing; or needs a repeatable script, capture plan, and verified final media. Do not use for fictional commercials with no real product proof, general-purpose video editing, or documentation-only tutorials that do not need a promotional narrative.

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。

Recommend font combinations for different use cases. Provide Google Fonts alternatives to premium fonts with hierarchy examples.

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 complete Claude Code workflow directories with curated skills. Use when user wants to (1) create a new workflow for specific use case (media creator, developer, marketer, etc.), (2) set up a Claude Code project with pre-configured skills, (3) download and organize skills from GitHub repositories, or (4) generate README.md and AGENTS.md documentation for workflows. Triggers on phrases like "create workflow", "new workflow", "set up workflow", "build a xxx-workflow".

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。

Develop art direction for image generation with curated style cards distilled from Open Image Prompts. Use when choosing or comparing visual directions, improving an existing generation prompt, or translating a subject, use case, or supplied image into a compact creative spec. Use img-gen-prompts for real archive examples, exact source prompts, or gallery browsing.

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。

Investment Thesis Generator — builds a complete, structured investment thesis with bull/bear cases, catalyst timeline, entry/exit strategies, position sizing, and asymmetry assessment for any publicly traded stock.

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。

Choose compatibility-aware scope for runtime and API changes in openai-guardrails-python. Use before initial implementation and each review-feedback batch to decide whether to patch, reset the design, preserve compatibility, or reject unsupported cases.

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。

Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.

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。

Performs symbolic execution to detect potential errors by exploring execution paths, solving path constraints, and generating test inputs. Use when you need to analyze code for bugs like null dereferences, division by zero, buffer overflows, or assertion violations. Also use to generate test inputs that exercise different code paths, find edge cases, or explore all reachable program states. Supports Python, Java, and C/C++ through manual symbolic execution techniques and integration with tools like KLEE, angr, Z3, and Symbolic PathFinder.

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。

Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property testing).

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。

Collect, normalize, and structure execution traces from instrumented programs (strace, ltrace) into JSON format for downstream analysis. Use when working with system call traces, library call traces, or execution logs that need to be analyzed for debugging, test case reproduction, or verification. Supports parsing strace/ltrace output, filtering noise, extracting debug information, and preparing traces for bug analysis or reproduction workflows.

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。