Scans AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection API. Identifies unusual error patterns, latency spikes, and log volume anomalies across log groups.
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概要と使いどころ
Scans AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection API. Identifies unusual error patterns, latency spikes, and log volume anomalies across log groups.
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。
Triages AWS CloudWatch alarms using boto3 CloudWatch.describe_alarms, CloudWatch Logs Insights queries, and AWS X-Ray trace analysis via the xray-sdk. Correlates alarm triggers with deployment events.
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。
Uses AWS SDK CloudWatchClient GetMetricData and CloudWatch Logs Insights StartQueryExecution to automate incident triage. Correlates alarms via DescribeAlarms with X-Ray trace segments for root cause 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。
Expert product discovery guidance for user research and problem validation. Use when conducting user interviews, validating problems, applying jobs-to-be-done framework, sizing opportunities, customer segmentation, competitive analysis, prototype testing, usability testing, designing surveys, or synthesizing research insights. Covers discovery sprints, continuous discovery, and research operations.
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Extracts reusable patterns from sessions. Use at session end to capture debugging insights and project-specific knowledge.
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WHOOP CLI with health insights, trends analysis, and data fetching (sleep, recovery, HRV, strain).
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Read your Threads (Meta) audience and niche from real data. Scan a niche query or hashtag for the top posts right now with likes, replies, reposts, and quotes, and pull a profile (yours or a competitor) for follower count, bio, verified status, and recent posts with engagement. Powered by Apify, no login. Triggers on "analyze Threads", "what is working on Threads", "top posts for", "competitor Threads profile", "niche scan", "follower count". Not for writing posts (use threads-post-writer).
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Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.
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File a librarian query result back into the library as a new page with provenance tracking. Karpathy's "good answers can be filed back into the wiki as new pages" insight made concrete. The library compounds from explorations, not just from external sources.
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。
Reflective Memory. Use when working with memory, reflection, context management, or intent tracking. Provides the reflective memory practice — how and when to use keep tools for capturing insights, tracking commitments, and maintaining awareness.
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Guide a person through a meditation session from preparation through closing. AI coaches posture selection, breath anchoring, working with distractions, shamatha (calm abiding), vipassana (insight), and session integration. Use when a person wants to begin or deepen a meditation practice, when someone needs to prepare their mind for focused work, when grounding is needed before or after energy healing work, when managing stress or anxiety, or for integration after intense experiences.
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Organize, extract, prune, and verify Claude Code persistent memory files. Covers MEMORY.md as a concise index, topic extraction to dedicated files, staleness detection, accuracy verification against project state, the dual 200-line / 25KB load cap, and reachability of topic files from the index. Use when MEMORY.md is approaching either load cap, after a session produces durable insights worth preserving, when a topic section has grown beyond 10-15 lines and should be extracted, when project state has changed and memory entries may be stale, or before and after rewriting the index so that no topic file loses its only pointer.
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Guide a person in developing active listening skills. AI coaches receptive mindset preparation, full-presence attending, reflective paraphrasing, clarifying questioning, synthesis of understanding, and integration of insights into communication practice. Use when a person wants to improve communication, when someone keeps misunderstanding others or feels misunderstood, when preparing for a difficult conversation such as a feedback session or conflict resolution, or when a person notices they talk more than they listen.
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Master skill composing the 5-step synoptic cycle for panoramic synthesis across multiple domains. Orchestrates meditate, expand-awareness, observe, awareness, integrate-gestalt, and express-insight into a coherent process that produces unified understanding rather than sequential compromise. Use when a problem genuinely spans 3+ domains and the interactions between domains matter more than depth in any one, when sequential analysis feels like compromise rather than integration, or before major architectural decisions affecting multiple stakeholders.
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Analyze datasets and extract insights. Use when user needs to understand data patterns, statistics, or trends.
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Analyze queried data for trends, week-over-week comparisons, distributions, funnels, cohorts, top-N lists, anomalies, sanity checks, and report-ready findings. Use after or alongside ClickHouse queries when the user wants insight rather than raw rows.
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Analyze queried data for trends, week-over-week comparisons, distributions, funnels, cohorts, top-N lists, anomalies, sanity checks, and report-ready findings. Use after or alongside ClickHouse queries when the user wants insight rather than raw rows.
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当用户需要深入理解目标客户的心理动机来撰写有共鸣的文案时调用。 适用于:写文案前分析客户心理、客户画像不够深入、文案太平面缺乏情感共鸣、 做用户调研准备。核心信号:用户在问"客户到底在想什么""怎么打动他们"。 不适用于:纯功能型产品文案(技术规格就够了)、不需要情感共鸣的B2B技术文档。
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Use when operating Inngest API resources from the terminal with `npx inngest-cli@latest api`: Cloud/local run debugging, event-run lookup, function traces, function invocation, app syncs, webhooks, environments, keys, account checks, and Insights queries. Provides prescriptive command routing for agents: which CLI command to run for a run ID, event ID, app ID, function ID, Cloud environment, API key, missing ID, or potentially mutating operation. Use `inngest-cli` for dev server setup/general CLI workflows and `inngest-api` only when raw REST API v2 docs or OpenAPI fallback are needed.
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Inspect and operate Inngest Cloud through its OAuth MCP connection. Use for deployed apps and functions, failed or slow runs, traces, events, environments, sessions, experiments, and Insights. Prefer this for Cloud operations when MCP tools are available; use CLI or REST skills for explicit terminal or HTTP tasks.
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Use when building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based evaluation.
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Find one insight that eliminates multiple components - "if this is true, we don't need X, Y, or Z"
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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
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Comprehensive, data-backed email marketing knowledge base. 908 sources, 4,798 insights. Use when reviewing email setups, building automation flows, diagnosing deliverability, writing email copy, selecting platforms, or pulling Standards. Covers strategy, flows, deliverability, copywriting, segmentation, compliance, cold email, and 19 industry playbooks.
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。