「termin」の検索結果

Fix macOS app hard crashes (EXC_CRASH / SIGKILL) caused by accessing TCC-protected APIs without the matching Info.plist usage description key. Use when: (1) App crashes immediately when touching Photos, Contacts, Calendar, Reminders, Location, Microphone, Camera, Screen Recording, etc., (2) Crash report shows `Exception: EXC_CRASH (SIGKILL)` with `Termination namespace: TCC`, (3) Crash stack includes `TCC.__TCC_CRASHING_DUE_TO_PRIVACY_VIOLATION__`, (4) Termination details contain "This app has crashed because it attempted to access privacy-sensitive data without a usage description", (5) Flutter macOS publish / gallery-save flow crashes on Gal.putVideo, PHPhotoLibrary, CNContactStore, etc. Critical distinction: macOS does NOT return a deny error — it SIGKILLs the process. This skill also covers the NSPhotoLibraryUsageDescription vs NSPhotoLibraryAddUsageDescription distinction that trips many apps.

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。

Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures. Use this skill when you need to reproduce a bug, capture execution traces for debugging, instrument code to record program behavior, generate replay scripts for bug reproduction, diagnose hard-to-reproduce failures, or perform deterministic replay of program execution. Triggers when users ask to instrument code for tracing, capture execution traces, reproduce bugs, generate replay scripts, or enable deterministic debugging.

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。

Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Python, JavaScript, Java, and C/C++ with both custom instrumentation and existing replay tools.

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。

Identifies non-deterministic or unreliable tests through static code analysis and test result analysis. Use when Claude needs to find flaky tests, analyze test reliability, or investigate intermittent test failures. Supports Python (pytest, unittest) and Java (JUnit, TestNG) test frameworks. Trigger when users mention "flaky tests", "intermittent failures", "non-deterministic tests", "unreliable tests", or ask to "find flaky tests", "analyze test stability", or "why tests fail randomly".

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 or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.

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。

Inspect an unfamiliar repository, interpret a broad Markdown user journey at runtime, operate the real product through its supported web, API, CLI, desktop, or mobile surface, and produce an auditable pass, fail, or blocked judgment with screenshots, logs, recordings, and a step timeline when available. Use when asked to run smoke tests, validate an end-to-end user journey, dogfood a product, test a release candidate, or execute a non-deterministic workflow that may include authentication or human checkpoints. Do not use as a substitute for deterministic unit, integration, or scenario 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。

tlive — remote approvals (Telegram/Feishu/web), live web terminal, and session monitoring for Claude Code / Codex. Use for configuring or diagnosing tlive, connecting IM platforms, printing session links, or explaining approval behavior. Triggers "tlive", "IM bridge", "phone approvals", "remote terminal", "Telegram/Feishu notifications".

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。

Thesis report synthesis — turn a thesis's markdown artifacts into its letter-size thesis-report.html, then OPTIMIZE it against real page renders. Four-step loop — pack (deterministic Python bundle of all sources + report/metrics.json), author (the LLM writes report/content.html — narrative, KPI tiles, badges, timeline, tables and citations are LLM judgment), assemble (deterministic validation, template injection, Q50 pins, Q47 overflow gate), render + optimize (Chrome headless per-page PNGs — the LLM reads them and iterates until every page is visually clean). Spec 046 Q46–Q50.

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。

Weekly clinical-trial status diff — the 11-category change report over the stored weekly snapshots (spec 055 FR-B03). Surfaces exactly what changed on the trial registry for your coverage: New / Suspended / Withdrawn / Terminated / Recruiting / Recruitment-Complete / Completed / Ahead / Delayed / Upsized / Downsized, every row carrying previous and new values, with risk flags on Terminated and Downsized and re-timing flags on Ahead and Delayed. The stored-previous-value discipline makes each week's diff reproducible.

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。

A terminal-native marketing system rendered entirely in Berkeley Mono — every word on the page, from the hero headline down to the footer fine print, is monospaced. The page itself reads like a manpage or a static-site README: warm cream canvas (`#fdfcfc`), nearly-black ink (`#201d1d`), 4px-radius rectangles for the few interactive elements, and bracketed `[+]`/`[-]` ASCII markers used as bullets. The brand's only "visual moment" is a single dark hero card that mocks up the OpenCode TUI itself — black background, monospaced terminal output, ASCII pipe characters, and a wordmark rendered as block-pixel ASCII. Every section sits as a hairline-bordered text block on the cream canvas with no shadows, no gradients, no decorative imagery, and no non-monospaced character anywhere in the system.

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。

Goal-driven E2E testing where a browser agent (Playwright MCP / computer-use) reads a natural-language goal and explores the app via the accessibility tree to assert outcomes — no pre-written script. Covers when intent-driven beats scripted, making agent runs deterministic (pinned model, temperature 0, seeded data, bounded steps, explicit success assertion, snapshot-not-pixel), cost/latency control, the accessibility-tree-first interaction model, CI gating, and graduating a stable run into a scripted Playwright test. Use when: "agentic browser test," "goal-driven browser test," "let an agent explore the app," "natural-language E2E," "browser agent smoke test," "Playwright MCP test." Not for: Writing/maintaining deterministic scripted Playwright tests — that is playwright-automation. Testing your product's OWN LLM features — that is ai-system-testing. Related: playwright-automation, ai-system-testing, exploratory-testing, test-reliability, qa-project-context.

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。

Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.

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。

Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-context.

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。

Dify is an open-source platform for building LLM apps such as chatbots, agents, workflows and RAG knowledge bases, and it exposes every published app as a REST API. Use when a user asks to self-host Dify with Docker Compose, configure Dify environment variables, call a Dify app or workflow API, upload documents to a Dify knowledge base, export or import a Dify DSL file, run a Dify app from the terminal with difyctl, or upgrade and back up a Dify instance.

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。

Continue is an open-source AI code assistant for VS Code, JetBrains and the terminal (the cn CLI) that works with any LLM: Claude, GPT, Gemini, Ollama and other local models. Use when a user asks to configure Continue with config.yaml, add chat or tab autocomplete models, write rules or custom prompts, connect MCP servers, run cn headless in scripts, or migrate from config.json. Continue was acquired by Cursor in June 2026: the release from that month is final and the Hub is offline, so the skill covers local, self-configured use.

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 software with Claude Code, the AI-powered CLI coding agent. Use when a user asks to set up Claude Code, configure CLAUDE.md project files, use slash commands, manage permissions, create hooks, or optimize agentic coding 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。

CodeRabbit reviews pull requests with AI on GitHub, GitLab, Azure DevOps and Bitbucket, and reviews local changes from a CLI before they are pushed. Use when a user asks to set up automated PR reviews, write or validate a .coderabbit.yaml, add path-specific review instructions, control a review with @coderabbitai commands, or run coderabbit review from the terminal or a coding agent.

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。

You are an expert in Cline, the autonomous AI coding agent for VS Code that can read files, write code, run terminal commands, and use the browser — with human-in-the-loop approval at each step. You help developers use Cline for complex multi-file refactoring, feature implementation, debugging, and codebase exploration where the AI plans and executes while the developer reviews and approves.

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 CLI tools with Commander.js. Use when creating command-line applications, parsing arguments, implementing subcommands, or building developer tools with flags and options.

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。

Animates 3D objects and characters in Blender with Python (bpy): keyframes, armatures and IK rigs, shape keys, F-Curves, the NLA editor and drivers. Use when the user wants to keyframe properties, create armatures and rigs, set up IK/FK chains, animate shape keys for facial animation, edit F-Curves, blend actions with NLA strips, add drivers, or script any animation workflow in Blender from the terminal.

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。

Tests and debugs APIs with Bruno, the open-source API client that keeps collections as plain files in a Git repository. Use when a user asks to create API requests, organize collections, write test scripts, use environments and variables, run a collection from the terminal or CI with the bru CLI, or collaborate on API workflows stored in Git.

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。

The AWS Command Line Interface (v2) manages Amazon Web Services from the terminal. Use when the user needs to run S3, EC2, Lambda, CloudWatch, IAM or other AWS commands, sign in with SSO or short-term credentials, write AWS automation scripts, or debug an `aws` command and its 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。

Atuin replaces your shell history file with a searchable SQLite database that records directory, exit code, duration and host for every command, and can sync it end-to-end encrypted between machines. Use when a user asks to "search shell history", "replace Ctrl+R", "sync history across machines", "find the command I ran in this directory", "import bash/zsh history", "see my most used commands" or "self-host Atuin".

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。

Azure Command Line Interface for managing Microsoft Azure resources. Use when the user needs to create VMs, manage storage accounts, deploy functions, configure resource groups, and automate Azure operations from the terminal.

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。