「investigate」の検索結果

Evaluating online information for credibility, accuracy, and context. Covers the SIFT method (Stop, Investigate, Find, Trace), lateral reading, reverse image search, primary source verification, and common misinformation tactics. Use when assessing the trustworthiness of a web page, article, video, social media post, or any digital claim before relying on it or sharing it.

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Generate intelligence briefings for the planning dashboard. Use this skill whenever a request file appears in `.planning/console/inbox/pending/` whose `type` field starts with `intelligence.` (refresh_briefing, triage_finding, snapshot_diff, investigate_section, dismiss_finding). The skill reads the per-project KB at `.gsd/intelligence/intelligence.db`, synthesizes a briefing with a causal hypothesis + acknowledged uncertainty + confidence label and ranked moves, then writes the result back to the KB. Always trigger this skill for these request types — do not generate briefings manually.

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Investigate pytest failures without treating tests or implementation as automatic authority. Use when diagnosing regressions, debugging test errors, or deciding whether to fix a test or code; trace the contract, product, fixture, and observation before choosing an evidence-backed correction.

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Trace bugs, CI failures, and unexpected behavior from reproduced evidence to a causal mechanism and governing contract. Distinguish product, architecture, test-oracle, and harness defects; investigate intermittent failures with controlled experiments and report unresolved evidence explicitly.

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Investigate why something happens — confirm the question and success criteria with you, then trace the root cause with an evidence chain.

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Reviews Node.js runtime behavior in JavaScript or TypeScript, including server entrypoints, route handlers, middleware, streams, processes, dependencies, and environment configuration. Loads alongside language and CLI checks when applicable; investigates blocking I/O, backpressure, injection, lifecycle leaks, and misleading failure signals.

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Use when analyzing failing test cases to determine whether failures indicate genuine bugs or test implementation issues. Activates on "analyze failing tests", "debug test failures", "investigate test errors", or when provided with specific failing test names or output. Applies balanced investigative reasoning — does not auto-fix tests without establishing root cause.

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Shapes for running many sub-agents at once — fan-out with a barrier, maker/checker, generate-and-filter, tournaments for ranking, hypothesis fan-out for root cause, and loops that stop on a condition with a cap. Use when a phase has several independent targets, when a task is too large or too repetitive for one window, when the same edit applies across many files, when ranking or judging a large set, when several hypotheses compete, or when work must repeat until a signal says stop. Does not apply when units depend on each other's results or the cause is still unknown — investigate first, then parallelize.

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An AI analyst for buying B2B software. Investigates what vendors claim, checks the evidence, surfaces contradictions and unanswered questions, and produces a shareable Decision Brief with the questions to ask before you buy. Quick Eval by default; Deep Eval on request. Use when asked to evaluate, compare, vet, or research B2B software vendors.

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Investigate one Anabasis v2 campaign from its log and turn what it shows into findings with one owner each. Answers whether a comparison's reading (step, no-step, not-scored) may be believed, whether a fail was the solver's or the exam's, whether the harness could know the exam, whether the tasks bracket the solver, whether the campaign is climbing, and where the hours and dollars went. Reads evidence; starts no solve, session or round, and changes no verdict.

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Query and manage Datadog monitoring data — logs, metrics, monitors, dashboards, events, SLOs, traces, and incidents. Use this whenever the user wants to search logs, look at a metric, check which monitors are alerting, investigate a trace, pull SLO status, mute an alert, or ask "what's happening in Datadog" — even if they don't say "API". Also use it for any URL under *.datadoghq.com. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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Post a sitrep (situation report) for a live incident, in its incident channel: a short fixed-layout update for people who haven't read the whole channel — the current picture and what moved since the previous update. Use on "sitrep", "status update", "where are we", "what's the latest", "catch me up", "summarize the incident so far", when asked to keep posting on a schedule ("post a sitrep every hour until resolved"), or when the oncall memory sets an update cadence for this severity and the incident owner asks Claude to keep it. Reads the channel, alert thread and findings posted, re-reads the one key signal, and writes: status line, TL;DR, impact, what changed since the last sitrep, what's in progress and who has it, what's needed, when the next update comes, one chart. Facts come only from what people said or Claude read first-hand; cause and recovery time are never guessed. Fits one phone screen. Not investigation (`incident-investigate`) or the write-up afterwards (`incident-postmortem`).

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Automatic init the first time Claude acts in an incident or alerts channel — one named like #inc-…, #incident-…, #sev0-…/#sev1-…, or whose recent messages are pages or alerts from PagerDuty, Opsgenie, incident.io, Datadog, Sentry or Grafana, or relayed by people (talk about being paged is not an alert) — that isn't already a team's oncall / monitoring channel. Run once per channel, on first contact (just invited, first @-mention, channel just created), before doing what was asked: load the oncall memory oncall setup wrote to shared workspace memory, pick the owning team's section, work out which monitoring / paging / code tools Claude can reach and which are missing, post one short zero-context message, note findings in this channel's memory, then hand off to `incident-investigate` if something was asked — or immediately, unasked, when the channel was plainly opened for a live outage. Works with no oncall memory (offers setup once). Also on "load the oncall setup here" / "init this incident".

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Research an issue deeply and present all context to the user. but the user writes the code. The LLM investigates code paths, finds relevant files, explains patterns, and identifies constraints. The user thinks through the approach and implements it. Use when ready to start working on an issue after oss-prep-to-contribute. Not for writing tests or documentation as a standalone contribution. Use oss-write-tests or oss-write-docs for those.

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Multi-source comprehensive research using perplexity-researcher, claude-researcher, and gemini-researcher agents. Launches up to 10 parallel research agents for fast results. USE WHEN user says 'do research', 'research X', 'find information about', 'investigate', 'analyze trends', 'current events', or any research-related request.

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Investigates anomalous patterns in AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection. Correlates log spikes with deployment events via AWS CodeDeploy API.

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Investigates CloudWatch metric anomalies using the AWS SDK CloudWatch.getMetricData and Logs.filterLogEvents APIs. Correlates metric spikes with log patterns and deployment events from CodeDeploy.

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Deep exploration and synthesis workflow using agent teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".

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Systematic, hypothesis-driven debugging workflow with triage-based track routing. Use when asked to "fix this bug", "debug this", "why is this failing", "this is broken", "investigate this error", "track down this issue", or any debugging situation. Supports --deep flag to force full investigation.

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Deep exploration and synthesis workflow using Agent Teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".

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Deep research with cross-verification and source tiering. Use when investigating technologies, comparing tools, fact-checking claims, evaluating architectures, or any task requiring verified information. Triggers on "조사해줘", "리서치", "research", "investigate", "fact-check", "비교 분석", "검증해줘".

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Investigate datasets and resolve entities using OpenPlanter methodology — cross-reference heterogeneous sources, build Admiralty/ACH confidence-tiered evidence chains, and apply OSINT tradecraft. Triggers on entity resolution, cross-reference datasets, evidence chain, OSINT investigation, structured investigation.

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Data analysis workflow kit using the ALIVE loop (Ask, Look, Investigate, Voice, Evolve)

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Deep, iterative, multi-source research that explores a topic from multiple perspectives, detects contradictions, and produces a comprehensive vault note with fact-level citations and a confidence map. Use this skill whenever the user says /deep-research, "deep research X", "research X thoroughly", "investigate X deeply", "I need to really understand X", or wants research that goes beyond a quick web search. Also use when the user wants to understand a complex, contested, or multi-faceted topic, compare competing approaches with evidence, or build a comprehensive mental model they can trust for decisions. This is heavier than /research — it searches web, academic papers, Reddit, and the vault, then loops through exploration and critique rounds until the evidence base is solid. When in doubt between /research and /deep-research, prefer this one if the topic is complex, contested, or the user wants depth over speed.

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。