「analyze」の検索結果

Analyze escaped defects and test suite health through blameless postmortems. Covers bug pattern analysis, test suite health reviews, 5 Whys root cause analysis, process improvement cycles, and postmortem/retro meeting templates with action item tracking. Use when: "QA retro," "escaped bugs," "postmortem," "quality incident," "defect analysis," "improvement cycle." Not for: live release go/no-go decisions — use release-readiness. Not for ongoing metric dashboards — use qa-metrics. Not for reviewing existing test code quality — use ai-qa-review. Related: qa-metrics, test-reliability, test-strategy.

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Review EXISTING test code for quality, smells, and testability issues. Detects test smells across six dimensions — readability, reliability, diagnostic value, design, AI-generated, and coverage — analyzes testability of application code, and backs the qualitative smells with mutation testing. Use when: "review my tests," "test quality audit," "test smells," "testability analysis," "are these tests any good." Not for: generating new tests — use `ai-test-generation`. Not for: testing AI features in your product — use `ai-system-testing`. Related: unit-testing, shift-left-testing, coverage-analysis, ai-test-generation.

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Finds and analyzes 5-10 comparable recent sales to estimate fair market value, calculate price adjustments, and score the property's value proposition

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Assess competitor defensibility and define our own moat strategy during PRD v0.3 Commercial Model. Triggers on requests to analyze competitor moats, define our defensibility, assess switching costs, identify vulnerabilities, find wedge opportunities, or when user asks "what's our moat?", "how defensible are they?", "where can we compete?", "switching costs?", "defensibility", "who to target". Consumes Competitive Landscape (v0.2) CFD- entries. Outputs CFD- entries for competitor moats and BR- entries for targeting rules and our defensibility strategy.

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DyNote: systematically and efficiently extract raw Douyin/DY video data and analyze videos, comments, accounts, hashtags, and short-video scenes into evidence-graded learning notes, summaries, research briefs, scripts, and knowledge-base material. Use when the user asks to 抓取/提取/整理 抖音视频字幕、视频文案、ASR 转写、Qwen3-ASR 中文转写、原始材料归档、学习笔记、analysis plan、note budget、避免返工、复用已有素材、评论洞察、账号分析、赛道/话题研究、竞品拆解、电商/本地生活视频分析、事实核查、自动搜索素材, save Douyin content as Markdown/TXT, or use subtitle/local ASR as the factual spine with logged-in Douyin Web built-in AI / Doubao fallback as visual or quick-reading supplements.

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Root cause based one-shot bug fix. debugger diagnosis → requirements.md generation → /execute. /bugfix "error description" Full investigation pipeline: debugger + gap-analyzer + standard verify. QA suggestion after successful fix.

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GitHub issue creation skill. Analyzes the entire codebase impact based on user request, then creates a structured issue with AI-verified/human-judgment-needed/caution sections. /issue "issue description" Trigger: "/issue", "이슈 만들어", "issue 만들자", "깃헙 이슈"

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Map customer journeys from awareness to advocacy. Visualize touchpoints, identify drop-off points, analyze channel interactions, and optimize the end-to-end experience. Use when the user asks about customer journey, funnel mapping, touchpoint analysis, or experience optimization.

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Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.

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Extract brand identity from a website URL — voice, colors, typography, imagery, values, and target audience — into a structured brand-profile.json. The profile feeds downstream skills (seo-content-writer, email-composer, frontend-design, pro-deck-builder, cross-platform-audit) for brand-consistent output. Use when the user says brand DNA, brand profile, extract brand, analyze brand, brand voice, brand identity, brand colors, or brand style guide.

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Extract and analyze competitor advertising from public ad libraries (Facebook Ad Library, Google Ads Transparency Center, TikTok, LinkedIn). Identifies messaging patterns, creative formats, pain points targeted, and positioning gaps. Use for competitive intelligence, campaign planning, and creative inspiration.

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生成一份对外可分享、脱敏的 AI-Native 开发者 README。 量化展示我对 Claude Code + Codex CLI + Kiro (AWS) + Trae (ByteDance) + Gemini Antigravity (Google) + Cursor 的使用深度、AI 协作风格、 项目与领域分布、兴趣主题，以及与 GitHub 提交的产出关联。 Trigger when the user says: "生成我的 AI 档案" / "做一份 AI-native README" / "分析我的 Claude / Codex / Kiro / Trae / Antigravity / Cursor 使用情况" / "总结我的 AI 使用" / "生成 AI 月度报告" / "按月份分析我的 AI 编码" / "分析 2026-05 的 AI 使用" / "build my AI usage profile" / "build my monthly AI coding report" / "analyze my AI usage for May 2026" / "summarize my Claude / Codex / Kiro / Trae / Antigravity / Cursor history" / "生成开发者画像". 全程本地、只读、默认匿名、不上传任何数据。

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Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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Vulnerability audit and dataflow analysis built on the neug CPG engine. file_audit (default, recommended): first builds a function-level summary graph (tier1) over the entire project, then incrementally builds detailed CPGs (tier2) on demand for chosen functions; both tiers coexist in a single neug database (unified schema, cross-layer refines edges; C/Java/Python/Rust). All capabilities are exposed as CLI tool subcommands (audit/overview/build/query/methods/paths), so an agent can drive the audit step by step via shell; the audit subcommand can also run the built-in LLM loop. The same tooling also supports a dataflow analysis mode (Workflow C): no vulnerability hunting, only variable-level dataflow tracing for functions of interest, producing dataflow_analysis.json. repo_audit: multi-round LLM/Cypher audit over a full-repo CPG. Use when the user asks to audit a codebase/project for vulnerabilities, scan a project with CPG/neug, run repo_audit/file_audit, find source-to-sink taint paths, or analyze dataflow inside a function. Supports C, Java, Python, and Rust.

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Analyze new commits and generate related test cases.

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Build autonomous financial research agents that analyze stocks, SEC filings, earnings calls, and market data to produce investment reports. Use when: automating investment research, building AI-powered stock analysis, creating financial due diligence agents.

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ESLint is the standard linting tool for JavaScript and TypeScript projects. It statically analyzes your code to find problems, enforce coding conventions, and catch bugs before they reach production. ESLint 9 introduced the flat config format, replacing the legacy `.eslintrc` system with a simpler, more composable `eslint.config.js` file. With a rich plugin ecosystem covering React, TypeScript, accessibility, and import ordering, ESLint is the backbone of code quality in modern web development.

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Read, transform, analyze, and generate Excel and CSV files. Use when a user asks to open a spreadsheet, process Excel data, merge CSVs, create pivot tables, clean up data, convert between Excel and CSV, add formulas, filter rows, or generate reports from tabular data. Handles .xlsx, .xls, and .csv.

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Audits and troubleshoots DNS records for domains including A, AAAA, CNAME, MX, TXT, SPF, DKIM, DMARC, CAA, and NS records. Use when someone needs to verify DNS configuration, debug DNS propagation issues, check email authentication records, or audit domain security. Trigger words: DNS records, dig, nslookup, SPF, DKIM, DMARC, MX records, DNS propagation, nameservers, CAA, domain configuration.

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Audit project dependencies for outdated packages, security vulnerabilities, and breaking changes. Use when someone asks to "check for outdated packages", "audit dependencies", "find vulnerable dependencies", "plan dependency upgrades", "detect breaking changes", or "update npm packages safely". Analyzes package.json/lock files, cross-references changelogs, and produces ordered upgrade plans.

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Transcribes and analyzes speech with the Deepgram API: pre-recorded audio, real-time streaming, text-to-speech and voice agents. Use when a user asks to convert speech to text, implement real-time transcription, add speaker labels or summaries to a recording, detect languages, build a voice agent with turn detection, or integrate Deepgram SDKs (JavaScript or Python).

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Finds orphaned, idle, and underutilized cloud resources across AWS, GCP, or Azure accounts. Use when someone needs to audit cloud spending, find unused EBS volumes, stale snapshots, unattached IPs, idle load balancers, or oversized RDS instances. Trigger words: cloud waste, orphaned resources, unused volumes, cloud audit, infrastructure cleanup, cloud bill analysis.

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Test web application security with Burp Suite. Use when a user asks to intercept HTTP traffic, test for web vulnerabilities, fuzz API endpoints, analyze authentication flows, or perform manual web application pentesting.

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AssemblyAI is a hosted speech-to-text API that transcribes audio and video files or live streams and adds speaker labels, sentiment, entity detection, PII redaction and LLM analysis of the transcript. Use when a user asks to transcribe a recording, label who said what, analyze call sentiment, redact personal data, stream live transcription, or summarize and question a transcript with LLM Gateway (the replacement for LeMUR).

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。