「cause」の検索結果

既存機能の不具合を失敗するテストで再現し、修正、レビュー、承認後のコミットまで、各工程を担当するECCエージェントに割り振って進めるスキル。

• クラッシュや誤った出力を直したいとき
• 不具合の再発を検知するテスト作成
• 原因が分からない不具合の調査

ECC ToolsのGitHub Appで起きるPRの過剰作成や利用上限の抜け、高額モデルの意図しない使用をコードから追い、費用増加の原因と修正を検証するスキル。

• PRの過剰作成の原因調査
• 利用上限を超える同時処理の点検
• 無料枠での高額モデル利用の確認

Blenderのキャラクターやアニメーションを、骨格・座標・接地状態などのデータと画像で検査し、足滑り、向きの不一致、ねじれの原因と修正点を整理するスキル。

• 歩行時の足滑りや床へのめり込みの確認
• キャラクターの向きと進行方向の比較
• 別の骨格へ動きを移した後の姿勢検査

AIエージェントの失敗や同じ操作の繰り返しを記録し、原因の切り分け、小さな復旧操作、結果の報告まで進める手順を示して、根拠のある再試行につなげるスキル。

• 同じツール操作を繰り返す原因の調査
• 会話の肥大化による品質低下の調査
• ファイルパスや環境の食い違いの確認

REST・GraphQL APIの不具合を、接続、認証、送受信データの順に切り分けます。再現用コマンドや回帰テストを整え、原因と修正案をまとめるスキル。

• APIの認証エラーを調べたいとき
• Postmanとコードの動作差を再現
• ページ分割や回数制限の確認

4-phase root cause debugging: understand bugs before fixing.

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。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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 asked to debug, fix a bug, investigate an error, or do root cause analysis, and when users report errors, stack traces, unexpected behavior, or say something stopped working.

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。

Systematic debugging with root cause investigation. (gstack)

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。

Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.

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。

OpenDesign's incident retro: the daemon-restart data bug, the root cause, the fix, and the systemic follow-ups. Built as a decision-grade product management deck for engineering, SRE, leadership.

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。

Cluster a GitHub issue backlog by root cause into a small set of plan-master issues, redirect children with a standardized comment, and bundle architectural-fix PRs that close clusters atomically. Use when an issue tracker has accumulated dozens of reports that share underlying defects, when asked to triage / consolidate / cluster / dedupe issues, when asked to build a plan series or roadmap from open issues, or when routing a new incoming bug into an existing plan.

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。

Retrieve detected internet outages (Cloudflare Radar) with country, cause, severity, and time bounds. Use when the user asks whether a country's internet is down, throttled, or experiencing a shutdown.

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。

Diagnose why a GAIA question failed — extract trace, classify failure mode, and propose a fix. Use when a GAIA benchmark run reports a failed/incorrect task_id and you need to root-cause it before resubmitting.

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 reviewing article images, cards, galleries, or any component rendered at multiple sizes across breakpoints. Check both the markup and the CSS layout because an incorrect `sizes` attribute can negate a perfectly good `srcset`.

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 reviewing dashboards, admin tables, search results, or feeds with many repeated items. Confirm the bottleneck is DOM or rendering cost before introducing virtualization because small lists usually do not need the added complexity.

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 reviewing slow initial loads caused by optional features. Prioritize code that is expensive to download or execute and not required for the first viewport, first input, or core task completion.

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 reviewing a website for layout and functionality issues caused by adblocker filter rules targeting common ad-related class names and IDs.

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 a high-signal bug issue or PR in the repo that owns the defect. Use this whenever the user asks to report, file, open, or triage a LazyCodex, lazycodex-ai, omo-codex, Codex plugin, or upstream Codex CLI bug, especially when they need source-backed root cause, reproduction steps, fix guidance, and GitHub routing.

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。

Team Mode security research skill. Orchestrates 3 vulnerability hunters and 2 PoC engineers to audit a codebase in parallel, prove exploitability, classify root causes, and calibrate severity by actual exploitability. Use for security review, vulnerability research, exploitability audit, pre-release security check, threat model validation, and `/security-research`. Triggers: 'security-research', 'security research', 'security review', 'vulnerability audit', 'exploitability audit', '보안 리뷰', '취약점 감사'.

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。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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。

Fix security vulnerabilities found by a Strix pentest (open-source CLI or app.strix.ai cloud) — triage by severity, patch the root cause rather than the symptom, and re-run Strix to prove each fix actually closes the exploit. Handles injection, XSS, SSRF, broken access control, IDOR, and other validated findings. Use after a Strix scan reports findings, or when the user asks to remediate, patch, or fix security issues from a strix_runs report, vulnerabilities.json, findings.sarif, or a cloud scan.

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。

Fetches current, version-specific library documentation and code examples through the Context7 MCP server. Use whenever the user asks about a library, framework, SDK, API, CLI tool, or cloud service, including API syntax, configuration, setup instructions, version migration, CLI usage, and library-specific debugging. Use when generating code that calls a third-party library, and when the user names a version such as Next.js 15 or React 19. Use even for well-known libraries like React, Vue, Next.js, Prisma, Supabase, Express, Tailwind, Django, and Spring Boot, because training data may not reflect recent changes. Prefer this over web search for library documentation. Do not use it for refactoring, writing scripts from scratch, debugging business logic, code review, or general programming concepts, or when the user has already supplied the relevant documentation.

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。

Fetch up-to-date documentation and code examples for any library, framework, SDK, CLI tool, or cloud service. Use whenever the user asks about a specific library — even well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot — because training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer. Do not rely on training data for API details, signatures, or configuration options — they are frequently out of date. Prefer this over web search for library documentation.

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。