Results for “arize”

PR作成時に使用。実装レポート（計画との対応、品質チェック結果、レビュー指摘対応）をPR descriptionに出力する。

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DBマイグレーション作成時に使用。バージョン番号競合防止。mainブランチ確認必須。

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マージ前に必ず実行。静的チェック・テスト・体制レビュー（変更内容で決まる統合レビュアー＋反証型QA＋専門家最大1体、基本1サイクル）と追加テスト（test-recommendation）を実施し、通過後のみmainへのマージ・直接pushが可能。

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Use when implementing any feature or bugfix, before writing implementation code

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変更内容からミューテーション/E2Eの推奨度を判定し、強く推奨・範囲が小さい・副作用なしのものは確認なしで実施し、それ以外は最後の報告で確認する。quality-check Step 5から参照実行されるほか、開発中の任意タイミングで単体実行できる。

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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

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Use before implementing any feature, behavior change, or refactor - settles requirements and design, then gets the design independently reviewed and approved by the user before code is written

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Use when selecting skills for a task or resolving how skill workflows apply

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Use when a multi-step change needs explicit sequencing, dependencies, or a handoff

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Use when creating new skills, editing existing skills, or verifying skills work before deployment

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作業開始時に使用。mainブランチでの作業禁止。Issue先行作成必須。

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git logからCHANGELOGとリリースノートを自動生成する。リリース作業時に使用。

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バックエンド実装時に使用。DRY原則遵守。コーディング規約準拠。

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Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback

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UI実装後の検証時に使用。agent-browser CLIでブラウザ上の動作を手動検証する。「UIを確認」「画面テスト」と言われたら使用（プロジェクトの E2E スイート実行は quality-check Step 5（推奨度・範囲で自動実施または確認）/ server-startup が担当）。

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機能・サービス・要件・前提条件などプロジェクトの知識をドキュメント化する。新規機能/サービスを作るとき、または既存機能を変更するときに必ず実行。新規ならドキュメントを作成、既存があれば更新する。

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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10 product agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. PM toolkit (RICE), agile PO, product strategist (OKR), UX researcher, UI design system, competitive teardown, landing page generator, SaaS scaffolder, research summarizer. Python tools (stdlib-only).

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Monitor a build process (webpack, turbo, docker) for warnings and errors as they stream. Summarize issues and fix them before the build finishes.

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Focused Signals scout for PostHog projects running Replay Vision scanners — the standing LLM probes that watch session recordings and write `$recording_observed` events. Watches two promises: that enabled scanners are actually observing (throughput / success-rate cliffs, exhausted quota — a silent watch gap), and that what the scanners see in aggregate gets surfaced (a monitor's `yes`-rate or a scorer's score stepping away from its own baseline, a classifier tag or a recurring summarizer theme concentrating across many sessions). It is the agentic pull complement to the per-session push path: scanners with `emits_signals` already emit one signal per session into this same inbox, so this scout never repeats them — it adds the cross-session shape the per-session probe can't see. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

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Finds the most informative session recording linked to an error tracking issue. Use when a user has an error tracking issue ID and wants to watch a replay showing what the user was doing when the error occurred. Ranks linked sessions by recency, activity score, and journey completeness, then summarizes the pre-error context. Replaces blind session picking from potentially hundreds of linked recordings.

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Guides agents through creating and safely sizing a Replay Vision scanner: choosing the scanner type (monitor/classifier/scorer/summarizer), shaping the RecordingsQuery that selects sessions, and — crucially — estimating observation volume and checking the org's monthly quota before creating, so a broad scanner doesn't exhaust the budget on its first scheduled sweep. TRIGGER when: user asks to create, set up, or configure a Replay Vision scanner, OR when you are about to call vision-scanners-create, OR when widening an existing scanner's query or sampling_rate via vision-scanners-update. DO NOT TRIGGER when: only reading scanners or observations, deleting a scanner, or running an existing scanner against a single session on demand (vision-scanners-scan-session).

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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).

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Assesses what a page's heatmap is telling you and recommends concrete changes. Pulls click / rageclick / scroll-depth data for a URL, names the hot elements by cross-referencing autocapture events on the same page, and can create a saved heatmap the user opens in PostHog, then summarizes the behavior and proposes improvements. TRIGGER when: user asks what a heatmap shows, why people aren't clicking something, where users rage-click, how far they scroll, what to change on a page based on heatmap/click data, or to 'analyze/assess/review the heatmap' for a URL. DO NOT TRIGGER when: the user only wants to create a saved heatmap screenshot with no analysis (use heatmaps-saved-create directly), or is asking about session replay in general (use investigating-replay).

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