「ai-assisted」の検索結果

AIが修正したコードで同じ不具合が再発しないよう、APIの必須項目や処理分岐を自動テストで確認し、テストとビルドを先に行うレビュー手順を整えるスキル。

• バグ修正後に再発防止テストを足したいとき
• API応答の必須項目を確認したいとき
• テスト用と本番用の応答を見直したいとき

Evaluate performance fairly when output is AI-assisted — what still measures the human, what now measures the tooling, and how to run the review conversation. Use when reviewing someone whose work is heavily AI-assisted, when output volume stopped meaning anything, when calibrating a team with uneven AI adoption, or when writing review criteria for the AI era. Produces review guidance: a what-measures-whom analysis, rewritten criteria, calibration rules for mixed-adoption teams, and conversation scripts. For the general review document use performance-review; for redesigning the role itself use role-redesign-for-ai.

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Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and funding proposals (NSF Project Summary/Description, NIH Specific Aims): preserve scholarly conventions, match claims to evidence (and, for proposals, claims to feasibility), and match the author's own voice. It never changes a number, result, or citation, and it is not for evading AI-use disclosure. Use when editing AI-assisted academic prose or grant proposals.

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Design an AI-assisted workflow for a recurring task — which steps to hand to AI, which to keep human, and how they connect — so you get leverage without losing quality or control. Use when asked how do I use AI for [process], automate this with AI, design an AI workflow, or where does AI fit in my process. Produces a map of the task's steps split into AI-does / human-does / human-checks, the right tool/prompt for each AI step, the hand-offs and review points, the failure modes to guard against, and a start-small rollout — turning a manual process into a reliable AI-assisted one that keeps you in control.

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How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservation, the AI slop problem, disclosure and transparency, team calibration, and the ethics of intellectually honest AI-assisted content production. Triggers on AI content workflow, AI-assisted writing, hybrid content production, AI in editorial, AI slop, AI disclosure, AI usage policy, AI content ethics, voice preservation with AI, team AI calibration. Also triggers when content feels generic despite quality tools, when team AI usage has drifted into inconsistency, or when a regulated or trust-sensitive context requires explicit AI policy.

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Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.

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撮影済みの動画や画面収録から見どころを選び、カット・構成整理・字幕や音声の追加を進めます。長い録画の短編化からSNS向けの画角調整、編集ソフトでの仕上げまで扱うスキルです。

• 長い録画から見どころを切り出したいとき
• 撮影素材からVlogを作りたいとき
• 製品デモに文字やナレーションを加えたいとき

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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Analyzes Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.

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Pre-build product and feature risk review for founders, product managers, and AI-assisted builders. Use this skill when the user is about to build a landing page, MVP, SaaS product, internal tool, agent workflow, or major feature and needs to check demand, positioning, monetization, retention, trust, distribution, and adoption risk before implementation starts.

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Open the context-mode Insight dashboard in your default browser. Insight is the hosted analytics layer for AI-assisted engineering teams — per-engineer productive rate, retry waste, blocker detection, role-narrowed views. Trigger: /context-mode:ctx-insight

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Comprehensive OSINT methodology skill for offensive security, red team intelligence gathering, and bug bounty reconnaissance. Covers domain recon, email harvesting, social media profiling, GitHub/code leaks, Shodan/Censys enumeration, breach data lookup, employee profiling, infrastructure mapping, cryptocurrency tracing, geospatial intelligence, and AI-assisted analysis workflows. Use when performing reconnaissance against a target domain or organization, investigating a person or entity, tracing cryptocurrency flows, geolocating images or events, or building an attack-surface map.

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Use CircleCI Chunk for AI-assisted CI/CD work through either the Chunk web UI or the chunk-cli. Trigger this skill when users ask to set up Chunk, troubleshoot or fix failing builds with Chunk, configure Chunk environments, schedule/proactively run Chunk tasks, or use chunk-cli commands such as init, validate, build-prompt, auth, sandbox, task, and skill install.

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Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Triggers on "learning exercise", "help me understand", "teach me", "why does this work", or after creating new files/modules. Do NOT use for urgent debugging, quick fixes, or when user says "just ship 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。

Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack

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。

Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack

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。

VS-Enhanced Academic Style Humanizer - Transforms writing patterns to achieve authentic scholarly voice Applies transformations from G5 analysis to create natural academic prose Use when: improving AI-assisted writing quality, preparing manuscripts, enhancing scholarly voice Triggers: humanize, transform, make natural, improve writing quality, improve style

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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。

Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.

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。

AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods

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。

Interactive click-to-segment using Segment Anything 2 — AI-assisted labeling for Annotation Studio

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。

Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Supports the user's stated goal of understanding design choices as learning opportunities.

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 TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices. Enforces TDD best practices across teams with AI-assisted testing and modern frameworks. Use PROACTIVELY for TDD implementation and governance.

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。