143건 · 관련도순
Add DeepEval tracing to Python AI applications so execution steps appear in Confident AI. Choose a supported integration or manual @observe instrumentation, then capture inputs, outputs, step types, tags, and metadata.
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How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt, and the failure modes that produce data nobody trusts. Triggers on product analytics setup, event taxonomy, tracking plan, instrumentation, schema versioning, North Star metric, retention cohorts, funnel design, naming conventions, instrument new feature, audit existing analytics, dashboard reconciliation, instrumentation debt, Mixpanel setup, Amplitude setup, PostHog setup, warehouse-native analytics. Also triggers when the team has data but cannot trust it, or when designing instrumentation for a new feature, or when auditing an existing setup that has drifted.
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Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.
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Set up telemetry, data about application operation, for web apps hosted in Azure. Choose automatic or manual instrumentation, create an Azure App Insights resource, and configure application code to send monitoring data.
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Think and work like an expert Instrumentation Engineer. Use when a task calls for Instrumentation Engineer judgment. Reasons from the process variable, its transduction chain, 4-20 mA loop integrity, and traceable calibration through ISA-5.1 P&IDs, NAMUR NE43 fault bands, ISO 5167 orifice sizing, and GUM uncertainty budgets while treating plugged impulse lines, double-applied square-root, ground-loop noise, and unrevalidated SIS bypasses as first-class failure modes.
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Think and work like an expert Astronomical Instrumentation Scientist. Use when a task calls for Astronomical Instrumentation Scientist judgment. Reasons from system-level error budgets, the diffraction limit and Strehl ratio, detector figures of merit, and resolving power through Zemax/Code V tolerancing, ETC radiometry, AO modeling, and on-sky standard-star commissioning while treating flexure drift, IR persistence, ghosts, and quasi-static speckles as first-class failure modes.
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Plan, implement, or review observability and instrumentation work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests observability and instrumentation work.
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Plan, implement, or review application insights instrumentation work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests application insights instrumentation work.
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Designs or reviews logs, metrics, traces, alerts, correlation, dashboards, and diagnostic instrumentation so failures and performance changes can be detected and explained at component boundaries. Use for observability work, incident readiness, instrumentation, or production diagnostics. Not for fixing a specific incident before reproducing it or for adding noisy logging without an operational question.
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Proposes the instrumentation and control content a P&ID adds to an accepted PFD process model: instruments drawn carried over as facts, measurement points and control loops the control philosophy requires, tag structure per the project instrument numbering procedure, system interfaces, and alarms, interlocks, trips and fail actions as quoted. Never assigns safety functions, ranges or setpoints. Use when the user asks to "run the instrumentation and control enrichment", "add the instrument section to the P&ID draft", "list the control loops the philosophy requires" or "carry the PFD instruments into the P&ID" after the piping section. Do not use for safety flags, SIL or trip classification, use process-safety-flags instead; do not use to assign tag numbers, use tagging-and-numbering. Drafts for human review; never approves, authorises or signs off.
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Set up distributed tracing in KubeSphere to follow requests across services. Covers extension installation, trace collection, automatic application instrumentation, and API queries for traces, individual processing steps, and service connections.
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Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation. Triggers: "azure-monitor-opentelemetry", "configure_azure_monitor", "Application Insights", "OpenTelemetry distro", "auto-instrumentation".
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Sets up CloudWatch observability for the first time - Omni (CloudWatch Application Observability) and classic CloudWatch. Omni: creating a Space or Domain; access grants (who has access, at what level) and access profiles bounding async alerts, integrations, or agents; instrumenting an app or AI agent with the plain ADOT SDK so traces reach Omni (Python/Node/Java/.NET on EC2/ECS/EKS/Lambda), incl. no-image-rebuild and .NET CoreCLR vars; ingesting Azure telemetry via the CloudWatch agent on an Azure VM or AKS; connecting Slack to a Space; whether GitHub or a custom MCP tool server (HTTP/stdio; API key, bearer, OAuth2) can be connected. CloudWatch: onboarding a service to Application Signals - ADOT auto-instrumentation, the amazon-cloudwatch-observability add-on, monitored service, reporting telemetry, ServiceEvents, CI/CD git/deployment metadata, Terraform/manifest edits. For using what is set up - queries, dashboards, alarms, Omni alerts, X-Ray, synthetics, Dynamic Instrumentation - use aws-observability.
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Builds, configures, debugs, and optimizes AWS observability - operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces - on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni - use setting-up-cloudwatch-observability. Not for app logging or threat detection.
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Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
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Provides guidance for implementing OpenTelemetry instrumentation in .NET codebases, covering tracing (Activities/Spans), metrics, logs, naming conventions, error handling, performance, SDK setup, resources, context propagation, and API design best practices.
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Product analytics for instrumenting products, defining metrics, and building retention funnels. Use when designing a metric tree, instrumenting a feature, auditing instrumentation, defining a North Star, or building an analytics roadmap.
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Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.
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Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Python, JavaScript, Java, and C/C++ with both custom instrumentation and existing replay tools.
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Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.
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红队渗透 / 攻防 — 受授权的红队作业者 + 渗透测试工程师 + 攻击型安全顾问的认知操作系统 (侦察 OSINT / 外网渗透 / 内网 AD 渗透 BloodHound + Kerberoasting + ADCS 利用 + 横向移动 / Web 应用渗透 OWASP WSTG / 移动 OWASP MASTG / 云渗透 AWS Azure GCP IAM 路径 + 容器逃逸 + K8s / C2 操作 Cobalt Strike Sliver Mythic Havoc + OPSEC / 初始访问 + AV EDR 绕过 (仅授权场景) / 无线 RF / 物理社工 / 报告与整改 / 框架 MITRE ATT&CK + D3FEND + PTES + OSSTMM + NIST 800-115 + Kill Chain / 法律伦理 CFAA + 网络安全法 + 刑法 285 286 + 数据安全法 + GDPR + 授权书 + 范围 + 交战规则 — 不含 黑产 / 未授权攻击 / 大规模 exploitation / 供应链投毒 / 未授权 DoS — 这是 重罪 + 行业封杀 + 律师吊销, 本 skill 严守 authorized-only 边界 — 也不含 蓝队 SOC + 恶意软件 即服务 / 僵尸网络 / 勒索软件作者 — 这是 cybercrime 不是 红队) (Cybersecurity Red Team / Offensive Security Operations — the cognitive operating system of authorized red team operators, penetration testers, and offensive security consultants covering (a) reconnaissance & OSINT (passive + active discovery, asset surface mapping), (b) external network pentest (perimeter, exposed services, web), (c) internal network / Active Directory pentest (AD enumeration via BloodHound, Kerberos abuse — Kerberoasting / AS-REP-roasting / Unconstrained delegation / S4U2self, NTLM relay, ADCS abuse, GPO abuse, lateral movement, privilege escalation), (d) web application pentest (OWASP WSTG, authentication, authorization, SSRF, XXE, deserialization, SSTI, prototype pollution, GraphQL, JWT, API), (e) mobile pentest (OWASP MASTG, iOS / Android, instrumentation Frida / Objection, MASVS), (f) cloud pentest (AWS / Azure / GCP — IAM enumeration, privilege escalation paths, container escape, K8s RBAC, serverless), (g) C2 operations & post-exploitation (Cobalt Strike / Sliver / Mythic / Havoc, beacon ops, malleable profiles, OPSEC), (h) initial access & evasion (phishing infrastructure, payload development, AV / EDR evasion, BYOVD, AMSI / ETW bypass — strictly for authorized engagements), (i) wireless / RF (WPA2/3, evil twin, Wi-Fi pivots), (j) physical / social engineering (badge cloning, pretexting, vishing — under engagement letter), (k) reporting & remediation (executive summary, technical findings, CVSS, MITRE ATT&CK mapping, retest), (l) frameworks & methodology (MITRE ATT&CK, MITRE D3FEND, PTES, OSSTMM, NIST SP 800-115, OWASP WSTG / MASTG, Cyber Kill Chain, Unified Kill Chain, Diamond Model), (m) law & ethics (CFAA US, Computer Misuse Act UK, 中国 刑法 285/286 + 网络安全法 + 数据安全法, GDPR for tested EU systems, engagement letter, scope, rules of engagement, safe harbor for bug bounty); NOT criminal hacking / 黑产 / unauthorized targeting / mass exploitation / supply-chain compromise / DoS against unconsented systems (这是 重罪 + 业内开除 + 律师执照吊销, 本 skill 严守 authorized-only 边界), NOT pure defensive blue team / SOC analyst tradecraft (是 平行学科, 仅做 边界标注 + ATT&CK 反推方向), NOT malware-as-a-service development / botnet ops / ransomware authoring (是 cybercrime 不是 红队), NOT 'ethical hacking' 在 'just curious 看看' 自我合理化的灰色操作 (违反 authorization 原则即不是 红队).) Master OS — automated mastery of Cybersecurity Red Team / Offensive Security Operations — the cognitive operating system of authorized red team operators, penetration testers, and offensive security consultants covering (a) reconnaissance & OSINT (passive + active discovery, asset surface mapping), (b) external network pentest (perimeter, exposed services, web), (c) internal network / Active Directory pentest (AD enumeration via BloodHound, Kerberos abuse — Kerberoasting / AS-REP-roasting / Unconstrained delegation / S4U2self, NTLM relay, ADCS abuse, GPO abuse, lateral movement, privilege escalation), (d) web application pentest (OWASP WSTG, authentication, authorization, SSRF, XXE, deserialization, SSTI, prototype pollution, GraphQL, JWT, API), (e) mobile pentest (OWASP MASTG, iOS / Android, instrumentation Frida / Objection, MASVS), (f) cloud pentest (AWS / Azure / GCP — IAM enumeration, privilege escalation paths, container escape, K8s RBAC, serverless), (g) C2 operations & post-exploitation (Cobalt Strike / Sliver / Mythic / Havoc, beacon ops, malleable profiles, OPSEC), (h) initial access & evasi
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Instrumentation plan — design event taxonomy, property schema, and tracking plan for analytics tools. Use when asked to "what should we track", "instrumentation plan", "set up analytics events", "analytics event schema", "tracking plan", or "instrument this feature".
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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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