「cap」の検索結果

Use when writing or running PowerShell on Windows, especially native programs, quoted paths, escaping, pwsh, Start-Process, file operations, or shell troubleshooting.

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。

Analyze, summarize, and extract insights from DeLive transcription sessions. Use when: user mentions DeLive, transcription, meeting transcripts, live captions, audio transcription, AI correction, corrected transcript, or transcript analysis; user wants to search, retrieve, summarize, correct, or process recorded transcripts; user asks about meeting notes, action items, discussion summaries, or transcript quality from DeLive. Requires DeLive app running locally with its MCP server or REST API.

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Sector Rotation & Analysis — analyzes sector momentum rankings, money flows, economic cycle positioning, relative strength, top stocks per sector, valuations, and rotation signals to identify where institutional capital is moving.

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Turn ONE topic, talking-head video, or photo into a finished Vox-style paper-collage explainer / ad video on the MuAPI platform (api.muapi.ai) + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Three input modalities: a topic (B-roll), a talking-head video (A-roll mode), or a single photo of a person/product anchored into the collage (C-roll mode).

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Use when reviewing what was learned after debugging, workaround discovery, or trial-and-error investigation, and capture it as a dated journal entry under ~/.codex/journal

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Fix psycopg2 "IndexError: tuple index out of range" when using LIKE with parameterized queries. Use when: (1) cursor.execute() fails with IndexError on a query containing LIKE '%pattern%', (2) SQL LIKE wildcards conflict with psycopg2 %s parameter placeholders, (3) Query works in psql but fails in Python. The % character has dual meaning: SQL LIKE wildcard AND psycopg2's parameter substitution marker.

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Fix Monte Carlo project completion simulations that give identical dates for all sub-groups (milestones, priorities, epics). Use when: (1) All sub-group predictions converge to the same date despite different remaining counts, (2) Proportional throughput scaling produces flat/identical results, (3) Applying overall scope rate to individual sub-groups causes simulations to hit max_weeks cap and never converge, (4) Building project forecasting tools that predict completion for sub-groups of a larger backlog. Covers hypergeometric draw model for milestones and cumulative priority model for sequential priorities.

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Fix black camera/microphone preview in Flutter macOS apps launched via `flutter run` from a terminal emulator (cmux, iTerm, Terminal.app, VS Code integrated terminal, Cursor, etc.). Use when: (1) camera plugin reports initialized but preview is black with no frames, (2) logs show "initialization completed via timeout" or similar first-frame timeout fallback, (3) AVCaptureSession.startRunning() returns without error but zero frames arrive, (4) the app's Info.plist has NSCameraUsageDescription and entitlements (com.apple.security.device.camera, audio-input) are correctly set, (5) no permission dialog ever appears, (6) same failure mode for microphone. Root cause is macOS TCC attributing camera/mic requests to the *responsible process* (the parent terminal) rather than the Flutter app itself. TCC refuses to prompt because the terminal is hardened-runtime without a camera entitlement, and silently denies. Not a bug in the camera plugin — would reproduce with ANY camera code.

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Fix "Null check operator used on a null value" errors when an object is set to null during an async await. Use when: (1) Object reference is nullified while awaiting, (2) Code accesses object with ! after await returns, (3) Cancel/dispose operations run concurrently with async operations on same object. Solution: capture local reference before await.

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Use when reviewing what was learned after debugging, workaround discovery, or trial-and-error investigation, and capture it as a dated journal entry under ~/.claude/journal

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Fix LRU-bounded Flutter/bloc state classes that silently lose insertion-order updates. Use when: (1) a state class uses `Equatable` with a `LinkedHashMap<K, V>` field to preserve insertion order (LRU, MRU, recent-items caches), (2) `Cubit.emit` appears to drop emissions when an existing key is re-inserted or moved to most-recent, (3) LRU eviction evicts the "wrong" entry after a refresh, (4) a test that reports the same key twice and then overflows the cap sees the refreshed entry evicted instead of the oldest. Root cause: Equatable's default map comparison is structural (unordered), so reordering without changing keys/values produces an "equal" state that `Cubit.emit` suppresses.

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Capture and save screenshots during test execution

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。

Capture and manage screenshots during test execution

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Use A4 Eight Page Booklet 2.2 to create, write, repair, impose, and validate a printable A4 one-sheet eight-page mini book or zine from text, notes, images, BIRD Graph JSON, iMandalArt/九宮, FIRE, outlines, templates, or PDFs. Use whenever the user says A4八頁小書, 八頁小書, 八頁小冊, 八格小書, A4一張八頁, 八頁在一頁, 8頁在一頁, 八張一頁, 一張紙變八頁, 摺頁書, 折頁小書, 迷你書, 小誌, zine, mini zine, booklet, 做成小書, 印成小書, 單張印出八頁, 幫我拼版, 八頁PDF, 合併八頁, 6-7-8-1／5-4-3-2排列, or asks for eight upright page PDFs, an eight-page reading PDF, an editable DOCX, or a correctly rotated A4 landscape print sheet.

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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.

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Translate Python programs to equivalent Lean4 code while preserving semantics and ensuring type safety. Use when users ask to convert, translate, or port Python code to Lean4, or when they need to verify Python algorithms using Lean4's theorem proving capabilities. Handles functions, classes, data structures, control flow, and ensures the generated Lean4 code is well-typed, executable, and can successfully run.

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Extract abstract mathematical models from functional code (Haskell, OCaml, F#) for formal reasoning in Isabelle/HOL. Use when users need to: (1) Convert functional programs to Isabelle definitions, (2) Extract high-level algorithm essence from implementation code, (3) Generate formal specifications and properties from code, (4) Create verification-ready models that capture mathematical properties while abstracting away implementation details. Focuses on structural recursion, algebraic data types, higher-order functions, and invariant extraction.

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Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

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Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with actionable refactoring suggestions.

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Uses abstract interpretation to automatically infer loop invariants, function preconditions, and postconditions for formal verification. Generates invariants that capture program behavior and support correctness proofs in Dafny, Isabelle, Coq, and other verification systems. Use when adding formal specifications to code, generating verification conditions, inferring contracts for functions, or discovering loop invariants for proofs.

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Generate concise summaries of source code at multiple scales. Use when users ask to summarize, explain, or understand code - whether it's a single function, a class, a module, or an entire codebase. Handles function-level code by explaining intention and core logic, and large codebases by providing high-level overviews with drill-down capabilities for specific modules.

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Generate GitHub Actions deployment workflows for automated deployment to staging and production environments on cloud platforms (AWS, GCP, Azure). Use when setting up continuous deployment pipelines, creating deployment automation, or configuring multi-environment deployment strategies. Includes templates for environment-specific deployments with approval gates, secrets management, and rollback capabilities.

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Translate C/C++ programs to equivalent Dafny code while preserving semantics and ensuring verification. Use when users ask to convert, translate, or port C/C++ code to Dafny, or when they need to formally verify C/C++ algorithms using Dafny's verification capabilities. Handles functions, structs, pointers, arrays, memory management, and ensures the generated Dafny code is well-typed, executable, verifiable, and can successfully run.

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Plan sprints with story point estimation, capacity planning, sprint goals, and retrospective frameworks. Organize teams for effective iteration with clear goals, realistic commitments, and continuous improvement.

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。