「intel」の検索結果

TikTok Shop policies and compliance — restricted products, intellectual property, content guidelines

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Product review analysis and customer feedback intelligence. Pain point identification, praise pattern analysis, feature request extraction, sentiment analysis, and product improvement insights. Use when the user asks about review analysis, customer feedback, product reviews, or sentiment analysis.

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。

Run all four review skills (investor, customer, operator, skeptic) on a business plan in sequence, then auto-decide where verdicts agree and surface only disagreements + taste decisions to the user. One command = full review gauntlet, intelligently filtered. Use when a plan is ready to be stress-tested before commit. Triggered by: 'panel review', 'review panel', 'run all reviews', 'review gauntlet', '审议会', '四方评审'.

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。

Discover profitable business ideas from scratch. Analyzes market gaps, trending niches, user skills, and competitive landscapes with a competitive intelligence protocol including 4-filter benchmark stress test and Blue Ocean differentiation grid. Use when the user has no idea what to build, wants to explore opportunities, needs market research, competitive benchmarking, or says 'find me a business idea', 'what should I build', 'market research', 'find opportunities', or 'competitive analysis'.

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Trace error propagation through a multi-agent session by agent depth — where failures originated, the depth at which they appeared, and how they cascaded up to parent agents — using the Agent Monitor workflow intelligence API and the session event stream. Use when a multi-agent run failed and you need to find the origin and blast radius of the failure.

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。

Render the multi-agent orchestration DAG for a session — parent→child subagent edges, tree depth, and fan-out — from the Agent Monitor workflow intelligence API. Cross-checks the orchestration dataset against the raw agent records and session detail. Use when visualizing how a session's agent structure was organized.

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 a concise stakeholder report from CCAM sessions, analytics, cost, alerts, and workflow intelligence. Use for release summaries, leadership updates, operational reviews, or a scoped Markdown report with evidence.

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。

Report concurrency and parallelism for a session — how many agents ran in parallel, concurrency-lane utilization, peak parallel width, and serialization bottlenecks (sequential chains that could have run as parallel lanes) — using the Agent Monitor workflow intelligence API. Use when checking whether a multi-agent session used parallelism efficiently.

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。

Audit model delegation and subagent effectiveness for a session — which models handled which subagent types, per-type success rates and average durations, and wasted delegations (heavy models on trivial work or types that consistently fail) — using the Agent Monitor workflow intelligence API. Use when reviewing how a session delegated work across models and subagents.

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 workflow-intelligence report from CCAM orchestration, delegation, tool flow, concurrency, complexity, compaction, and fleet-run data. Use for architecture reviews, agent-fleet analysis, or workflow optimization reports.

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。

Analyze workflow patterns using the Agent Monitor's workflow intelligence API — orchestration DAGs, tool flow transitions, subagent effectiveness, model delegation patterns, error propagation by depth, concurrency lanes, compaction impact, and agent co-occurrence. Produces prioritized optimization recommendations with quantified impact.

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。

Compile a weekly productivity report using Agent Monitor data — daily_sessions and daily_events trends, per-session costs from pricing engine, token volumes (input/output/cache_read/cache_write + baselines), tool usage top 20, session completion rates by status, and workflow intelligence metrics.

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。

Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.

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。

Detect recurring patterns using the Agent Monitor's workflow intelligence — toolFlow transitions (tool A → B frequency matrices), recurring workflow patterns, agent co-occurrence pairs, model delegation habits, error propagation paths by agent depth, and compaction triggers. Use to discover habitual usage patterns and anti-patterns.

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。

Debug a specific session by inspecting its full event chain (PreToolUse, PostToolUse, Stop, SubagentStop, Compaction, APIError, TurnDuration, Notification events), agent hierarchy (recursive parent/child tree with subagent_type and depth), token usage with compaction baselines, workflow intelligence data (orchestration DAG, error propagation by depth), and session metadata (thinking_blocks, turn_count, total_turn_duration_ms).

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。

Compare two sessions side-by-side using Agent Monitor data — per-model token usage (input/output/cache_read/cache_write + compaction baselines), pricing engine cost breakdowns, workflow intelligence (complexity scores, tool flow transitions, subagent effectiveness), session metadata (thinking_blocks, turn_count, turn_duration_ms, usage_extras), and full event timelines with all 10+ event types.

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。

Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).

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When the user wants to find information sources for content ideation, competitor monitoring, or industry tracking. Also use when the user mentions "research sources," "information sources," "content ideation," "industry monitoring," "competitor monitoring," "market intelligence," "content research," or "topic research." For keywords, use keyword-research.

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 the user wants a Process Intelligence bug-report draft or support summary for a failed operation.

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 Process Intelligence needs building, explicit cloud/account/environment configuration, sign-in, connection diagnostics, or recovery from an authentication error.

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。

How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservation, the AI slop problem, disclosure and transparency, team calibration, and the ethics of intellectually honest AI-assisted content production. Triggers on AI content workflow, AI-assisted writing, hybrid content production, AI in editorial, AI slop, AI disclosure, AI usage policy, AI content ethics, voice preservation with AI, team AI calibration. Also triggers when content feels generic despite quality tools, when team AI usage has drifted into inconsistency, or when a regulated or trust-sensitive context requires explicit AI policy.

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情商（EQ）训练专家系统——基于Mayer-Salovey能力模型与Goleman混合模型的 完整情商培养框架。涵盖自我意识、自我管理、社会意识、关系管理四大维度， 提供科学评估工具（MSCEIT/EQ-i 2.0/TEIQue）与实用训练方法。 触发词：「情商训练」「EQ提升」「情绪智力」「情绪管理」「情商测评」 适用场景：个人成长、职场发展、领导力培训、心理咨询辅助

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。

Unified static analysis — git history, AST symbols, project metadata

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Senior Regulatory Affairs Manager for HealthTech and MedTech. Use for regulatory strategy, FDA 510(k)/PMA/De Novo submissions, EU MDR CE marking, global market access, and regulatory intelligence.

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。