「investigate」の検索結果

Systematic research methodology for building comprehensive, current knowledge on any topic. Requires web_search tool. Use when questions require thorough investigation, recent developments post-cutoff, synthesis across multiple sources, or when Claude's knowledge may be outdated or incomplete. Triggered by "Research", "Investigate", "What's current on", "Latest info on", complex queries needing validation, or technical topics with recent changes.

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Investigate an unexplained defect or unexpected behavior without changing application code. Use for root-cause diagnosis and an evidence-backed handoff, including a bounded unresolved result. Not for implementing a known fix, new features, or explanation-only questions.

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Investigate an unexplained defect or unexpected behavior without changing application code. Use for root-cause diagnosis and an evidence-backed handoff, including a bounded unresolved result. Not for implementing a known fix, new features, or explanation-only questions.

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Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

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Investigate unexpected behavior and mysterious bugs. Use when the cause of a problem is unknown and the user needs to understand WHY something is happening — symptoms like: sudden unexplained changes in metrics or behavior, works locally but not in staging/production, inconsistent or intermittent failures, correct code producing wrong results, operations succeeding but having no effect, environment-specific failures, duplicate executions, stale data, or any "why did this change?" or "why is this happening?" situation. Covers infrastructure anomalies (cache hit rates dropping, latency spikes, queue behavior shifts) as well as code bugs. The key signal is confusion about root cause, not a request to implement a known fix. Do NOT use for feature requests, known fixes, planning, or documentation tasks.

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Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.

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Performance profiling and optimization for web apps — Core Web Vitals (LCP, INP, CLS), Lighthouse audits, bundle analysis, backend profiling (CPU, memory, DB queries), N+1 detection, caching strategies (Redis, CDN, HTTP), and performance budgets. Use when user asks to improve performance, run Lighthouse audit, profile a Node.js app, optimize Core Web Vitals, reduce bundle size, or investigate slow response times. Do NOT use for database schema optimization (use db-sculptor), Docker image optimization (use docker), or CDN configuration.

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Use when asked to prove, disprove, investigate, or find evidence for a claim about people, concepts, ideas, or their relationships. Also use when asked to explore connections, find who influenced what, trace how ideas developed, or answer questions that require reasoning over multiple linked notes.

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Use only after explicit Codex `$superloopy:superloopy-research` or Claude Code `/superloopy:superloopy-research` invocation, a research task started with a leading `loopy` or `루피` (such as `loopy research`), or an active Superloopy loop explicitly routing a research deliverable here. Evidence-backed Superloopy research orchestration with automatic advisory usage targets, parallel read-only lanes, per-retrieval verdicts, graded and dated sources, empirical verification, a priced claim ledger, cited synthesis, and optional reports. Do not activate from research, investigate, look-up, summarize, deep-dive, report, or similar vocabulary alone, in any language; ordinary questions, debugging, and implementation context-gathering stay with their primary workflows.

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Investigates a bug, failing test, build error, regression, flaky behavior, performance symptom, or unexpected result by reproducing it, minimizing the case, testing ranked hypotheses, tracing the first incorrect state, and locking the root cause with a regression check. Use before proposing a fix. Not for planning a new feature or reviewing a healthy diff.

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Plan, execute, investigate, and report HitKeep quality gates through hk. Use while iterating, before calling contributor work complete, when choosing change-aware versus PR-parity versus exhaustive validation, when CI parity matters, or when investigating a failed or asynchronous gate.

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Investigate and remediate data quality alerts using Monte Carlo MCP tools. Runs root cause analysis, assesses blast radius, discovers available tools (MCP/CLI/API), proposes and executes fixes, or escalates with full context when uncertain.

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Investigate data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling. Activates when a user asks about data issues, incidents, alerts, or why data looks wrong.

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Orchestrate incident response — triage, root cause, remediate, prevent recurrence. USE WHEN active alerts, data broken, stale, pipeline failure, or investigate and fix a data incident.

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Search and ask questions about coding agent session history across Claude Code, Codex, and Cursor. Use when asking what was worked on, what was tried before, how a problem was investigated across sessions, what happened recently, or any question about past agent sessions. Also use when the user references prior sessions, previous attempts, or past investigations — even without saying 'sessions' explicitly.

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Plan implementation before coding: investigate the repo, write spec and plan, and validate with a peer. Use for "plan", "design approach", "scope", or any coding task needing a plan. Not system-design thinking (/ship:arch-design) or full /ship:auto.

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Systematic debugging with root cause investigation. (gstack)

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Investigate and resolve Course Management Platform CloudWatch alert issues end to end. Use when Codex is asked to inspect a CMP GitHub alert issue, pull dev or production AWS logs, diagnose 5xx responses or failed background work, fix an incident with test-driven development, validate a fix in dev, or deploy and verify an authorized production fix.

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Systematic debugging method: 5-step root-cause analysis (capture, isolate, hypothesize, investigate, fix & verify) plus common bug-pattern reference. Use when errors, exceptions, test failures, or unexpected behavior appear. Loaded automatically by the debugger agent.

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Add message IDs to the relayer denylist. Use after investigating stuck messages with /investigate-stuck-messages, or when you have specific message IDs to denylist.

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Debug validator checkpoint inconsistencies where some validators are behind others. Use when alerts mention "checkpoint inconsistency", "validators behind", or "inconsistent latest checkpoints", or when asked to debug validator sets, investigate validator delays, or troubleshoot metadata fetch failures for a chain. Defaults to default_ism app context if not specified.

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Debug why a Hyperlane message is not being processed. Use when given a message ID or explorer URL to investigate delivery failures, gas estimation errors, validator issues, or other processing problems.

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SEO audit and keyword strategy — investigate a site's real search opportunities, shortlist candidates, and recommend the one to three changes most likely to grow organic traffic that converts. Evidence-first — metrics come from a tool or are written as unknown. Use when asked to "improve our SEO", "do keyword research", "audit our SEO", "why aren't we ranking", or "what should we fix first for search".

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Investigate performance bottlenecks and validate optimizations without weakening retrieval quality or correctness.

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。