「explore」の検索結果

Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer

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。

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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。

Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.

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。

Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs. Identifies wallet clusters, tracks fund movement through mixers and exchanges, and supports law enforcement attribution. Activates for requests involving ransomware payment tracing, bitcoin wallet analysis, cryptocurrency forensics, or blockchain intelligence gathering.

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。

Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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。

Signals scout that watches a PostHog project's most-viewed dashboards and insights for recent anomalies — sudden bursts, drops, flat-lines, and trend breaks at the daily or hourly level. It discovers what the team actually looks at (view counts, dashboard access), curates a durable watchlist in the scratchpad, and balances re-checking known high-value insights (exploit) against discovering new ones (explore) across runs, since no single run can cover a busy project. Anomalies are scored by robust deviation from each insight's own seasonality-matched baseline; it emits a finding only when a move clears the confidence bar, otherwise it updates the baseline memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

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。

Explore PostHog's Inbox — the surface where signal reports surface as actionable issues and trends. Use when the user asks "what's in my inbox?", "what should I look at?", "which reports are actionable?", "what's PostHog flagged recently?", asks about a specific report by ID or title, or wants to see which signal sources are configured. Covers listing, filtering, and drilling into reports, plus pointers to the deeper `signals` skill when raw signals or semantic search are needed.

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。

Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".

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。

Explore and diagnose a PostHog endpoint's execution logs — error messages, failed runs, cache misses, slow runs, or unexpected row counts during endpoint invocations. Use when the user says "my endpoint is failing", "show me the logs for endpoint X", "what error did endpoint Y produce", "why did endpoint Z return no rows", "is this endpoint hitting cache", or "check the last N runs". Focused on a single named endpoint's runtime log entries, not project-wide auditing or query performance profiling.

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。

Launch multiple explore subagents in parallel to investigate architecture, data models, auth, APIs, and deployment. Synthesize into an onboarding document.

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。

Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.

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。

Explore goals, constraints, and alternative approaches before choosing a design. Use when the user requests ideation or design exploration.

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。

Design-first ideation workflow — explore user intent, clarify constraints, propose approaches, and produce an approved design document before planning

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。

Explores a CARLA map's road network through the Map/Waypoint API — a natural-language rundown of the whole map ("give me the topology"), drawing the topology to confirm the modelled map matches the visible one, enumerating junctions to resolve phrases like "the 4-way junction in the middle", nearest-waypoint lane facts, and next/previous lane navigation. Use when the user asks about the map's layout, lanes, junctions, topology, or to locate/navigate specific map elements.

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。

Explore possible next roles from a user's actual tasks, interests, and constraints, including adjacent roles and changes within their current workplace. Build evidence-linked career hypotheses, identify unknown duties and requirements, and propose small ways to test them. Use when someone does not know what work to pursue or asks what they could do beyond their current job; do not diagnose aptitude, rank careers, or predict salary or hiring success.

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。

Re-analyze an existing GitHub Issue using its current title and body, refresh the implementation plan against the latest code state, and update the Issue in place. Use when the user provides an Issue number (numeric, `#`-prefixed, or Issue URL) and wants to regenerate the code analysis via the explore-agent subagent. For reflecting comment-driven updates, use update-issue. For creating a brand-new Issue from a natural-language task description, use create-issue.

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。

Builds an interactive HTML dashboard visualizing the Agent Skills ecosystem by fetching live data from github.com/agentskills/agentskills. Use this skill whenever the user wants to explore, research, or visualize Skills architecture, the SKILL.md spec, ecosystem adoption, repo stats, or any Skills intelligence dashboard. Trigger when the user mentions "skills dashboard", "skills ecosystem", "skills architecture overview", "agentskills repo", or wants to analyze how different tools implement Agent Skills. Optimized for Perplexity Computer — Computer fetches all data live, no static files required.

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。

Map the repository and recommend the next CodeConductor slash command. Read-only.

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。

Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer

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。

Execute an explicitly authorized exploratory charter and record notes, findings, evidence, and follow-up without claiming false coverage.

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 before any creative work - creating a feature, building a component, adding functionality, or changing behavior - to explore intent and design before implementation

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。

Analisa custo AWS e identifica desperdício em EC2, RDS, S3, EKS, EBS, Elastic IP, NAT Gateway e tráfego. Use quando o usuário disser "custo", "fatura", "está caro", "FinOps", "reduzir gasto AWS", "Cost Explorer", "otimizar custo". Quase todo leitura — seguro por natureza.

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。

Facilitate ideation, explore alternatives, and refine concepts through structured brainstorming techniques before implementation. Use when starting new projects, designing features, exploring solutions, evaluating tradeoffs, challenging assumptions, generating creative ideas, planning architecture, or refining rough concepts into detailed designs.

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。

Design shotgun: generate multiple AI design variants, open a comparison board, collect structured feedback, and iterate. Standalone design exploration you can run anytime. Use when: "explore designs", "show me options", "design variants", "visual brainstorm", or "I don't like how this looks". Proactively suggest when the user describes a UI feature but hasn't seen what it could look like. (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。