958 件 ・ 関連度順
概要と使いどころ
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.
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Triages AWS CloudWatch alarms using the CloudWatch DescribeAlarms API, GetMetricData for historical analysis, and CloudTrail LookupEvents for root cause correlation. Prioritizes alerts by blast radius and provides remediation playbooks.
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Research, implement, modify, and audit behavior inside Bricks Builder sites using the bundled Bricks Academy corpus, stable development workflows, and version-aware verification against an authorized active Bricks installation. Use when the requested outcome concerns Bricks elements, controls, schema, hooks, templates, styling, responsive settings, dynamic data, queries, forms, interactions, custom elements, or page JSON. Do not use merely because Bricks Builder is mentioned, for generic WordPress, PHP, CSS, or JavaScript work, or for maintaining, naming, versioning, documenting, or releasing this Skill repository.
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Design a rifle-scope reticle from a plain-language description and emit it as a BallisticCalculator `.reticle` file (BXml) plus a rendered SVG preview. Covers the complete `.reticle` element/attribute specification, reticle design principles (mrad vs MOA subtension, FFP/SFP, stroke-weight hierarchy, hash-mark ladders, christmas-tree wind holds, clutter budget, BDC anchors) and worked, verified patterns for duplex, mil-dot, hash-ladder, christmas-tree, chevron, circle-dot, dense-grid and rangefinding-stadia reticles. Also covers reproducing an existing commercial reticle from its manufacturer's technical data sheet. Use this skill whenever the user wants to create, modify, review or render a scope reticle, crosshair, mil-dot / MOA grid, holdover ladder, christmas tree, BDC ladder or rangefinding stadia — including any mention of a `.reticle` file, `ReticleDefinition`, `ReticleDrawController`, `SvgCanvasFactory`, or "draw me a reticle", and including requests to clone or model a named commercial reticle (Horus H58/H59, TReMoR, Mil-C, MOAR, Tremor3, EBR, and the like). Trigger it even when the user only says something like "make a crosshair with 0.2 mil hashes out to 5 mils" or "turn this reticle picture into a file", and trigger it before hand-writing reticle XML or hand-rolling reticle geometry, because the file format silently ignores misspelled attributes and mis-cased booleans.
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Use for debugging and system comprehension. Triggers: debug this, root cause, how does X work, reverse engineer, 왜 안 돼, 디버깅, 원인 분석, 로직 파악, 뜯어봐.
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Use when Codex must make a high-quality judgment on any complex problem, not only technical problems. Trigger when a question has high complexity, uncertainty, cost of being wrong, long-term impact, value tradeoffs, possible structural causes, or when the user asks for 全面思考, 深度理解, 系统性分析, 帮我想清楚, 从根上看, 这件事到底怎么看, 方法论, 复杂问题拆解, 系统方案, 架构判断, 策略判断, or 根因分析. Also trigger for project and engineering high-risk scenarios such as multi-module, multi-environment, multi-data-source, multi-state, financial truth, live trading, ledger, revenue, customer assets, order governance, broker interfaces, online availability, major solution selection, and system closure.
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Living catalogue of 7 known hazard entries (SE-01 through SE-07) specific to agent-studio: Windows backslash paths, prototype pollution, hook exit codes, async swallowing, ReDoS in glob-to-regex, DST arithmetic, array mutation during iteration. Each entry: symptom, root cause, fix, test assertion.
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Write effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents, or improving incident response processes.
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Systematic 4-phase debugging with root cause investigation. Use when fixing bugs to prevent random fixes.
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Use when EZ_math_model coder or packaging fails repeatedly with the same error and needs root-cause analysis instead of another blind retry.
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。
用户面对多个相互关联的问题, 不知道先解决哪个时; 当需要区分根本原因 与表面症状时; 当问题有先后因果链需要追溯源头时。 不适用于: 只有一个简单问题的场景、问题之间没有因果关系的场景。
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Tested reasoning approaches for Claude prompts — 18 variants across 6 categories (Analytical, Strategic, Creative, Technical, Research, Comparative). Use when the user wants a specific reasoning template — retrieving, reviewing, customizing, or combining reasoning instructions for a prompt. Trigger on: "give me the Root Cause Diagnosis approach," "show me the reasoning template for," "I need the Evidence Synthesis method," "show me analytical reasoning options," "combine reasoning approaches for," "show me all reasoning variants." Also use when a prompt produces shallow output and the fix is a reasoning upgrade. Provides 18 tested approaches for analytical, strategic, creative, technical, research, and comparison tasks. If the user is unsure which reasoning method fits, use rootnode-block-selection first if available. Do NOT use for evaluating existing prompts — use rootnode-prompt-validation if available. Do NOT use for project-level audits — use rootnode-project-audit if available.
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Detects seven structural anti-patterns in Claude Projects that cause unpredictable output, ignored instructions, and degraded quality. Diagnoses Monolith, Orphan File, Echo Chamber, Phantom Conversation, Kitchen Sink, Misaligned Hierarchy, and Blurred Layers. Use when user says "what's wrong with my project," "Claude ignores my instructions," "diagnose my project," "why is output inconsistent," "review my project setup." Also trigger on symptom-phrased: "Claude doesn't follow my rules," "my instructions keep getting overridden," "my Project isn't behaving as designed." Use alongside rootnode-project-audit if available for deeper structural analysis. Activate whenever the user describes symptoms of unreliable, inconsistent, or degraded Claude Project output, even if they do not name a specific pattern. Do NOT use when the user's primary request is Memory-layer rebalancing (use rootnode-memory-optimization if available) or scoring a single prompt (use rootnode-prompt-validation if available).
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Create or improve documentation based on the current conversation. Use this when a plan has been created and changes were requested because it didn’t follow the repository’s conventions.
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Implement regenerative recovery using triage, scaffolding, and progressive rebuild. Covers damage assessment, wound classification, emergency stabilization, scar tissue management, and resilience strengthening for systems that have sustained structural damage. Use when a system has suffered an incident needing structured recovery, when a failed transformation left the system in a damaged intermediate state, when accumulated technical debt has caused partial failure, or when a system is functional but degraded and the degradation is worsening.
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Restore legibility to an auto-generated graph diagram that has outgrown its canvas - rendering at single-digit percent scale with sub-pixel labels. Covers the levers that measurably reduce layout units (source-level label wrapping, invisible chain links so unconnected nodes stop spreading into one row, per-cluster splitting, layout direction picked by rendering both and measuring) and the rejected ones (raising the render font is a no-op because layout engines measure in text units; a topology heuristic for direction regressed 13 of 38 diagrams). Use when a generated Mermaid/Graphviz/DOT diagram is too small to read; not for hand-authoring a small diagram.
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Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.
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Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
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AI solution exploration using ant colony optimization — deploying scout hypotheses, reinforcing promising approaches, detecting diminishing returns, and knowing when to abandon a strategy. Use when facing a problem with multiple plausible approaches and no clear winner, when the first approach is not working but alternatives are unclear, when debugging with no obvious root cause requiring parallel hypothesis investigation, or when previous attempts have converged prematurely on a suboptimal approach.
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Instrument applications with OpenTelemetry for distributed tracing, including auto and manual instrumentation, context propagation, sampling strategies, and integration with Jaeger or Tempo. Use when debugging latency issues in distributed systems, understanding request flow across microservices, correlating traces with logs and metrics for root cause analysis, measuring end-to-end latency, or migrating from legacy tracing systems to OpenTelemetry.
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Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.
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Conduct a GxP audit of computerized systems and processes. Covers audit planning, opening meetings, evidence collection, finding classification (critical/major/minor), CAPA generation, closing meetings, report writing, and follow-up verification. Use for scheduled internal audits, supplier qualification audits, pre-inspection readiness assessments, for-cause audits triggered by deviations or data integrity concerns, or periodic compliance posture reviews of validated systems.
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Unify metrics, logs, and traces for cohesive debugging. Implement exemplars for log-to-trace linking, build unified dashboards using RED/USE methods, and enable rapid root cause analysis across observability signals. Use when investigating complex incidents spanning multiple systems, reducing mean time to resolution, implementing distributed tracing, or moving from siloed tools to a unified observability platform.
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Address unwanted dog behaviors through desensitization, counter-conditioning, and management. Covers reactivity (to dogs, people, sounds), separation anxiety, resource guarding, excessive barking, and leash pulling using force-free methods with systematic threshold management. Use when a dog shows reactivity toward other dogs, people, or stimuli; when separation anxiety causes destructive behavior or vocalization; when resource guarding is present; or when behaviors like excessive barking or leash pulling interfere with daily life — after basic obedience is established.
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。