Güvenlik uyarılı skill

Bu skill destructive command içerebilir. Skill detaylarını ve kaynak içeriği incelemeden kurulum yapmayın, kopyalamayın, indirmeyin veya çalıştırmayın. Bilinçli ve kontrollü şekilde kullanın.

Tarayıcı bulguları

highscript_injection

View source repo ↗

Dd Orchestrator

datadog-labs/agent-skills/dd-orchestrator

Çoklu dosya

Datadog Labs· v1.0.0

ai-mlResmi

Resmi Sağlayıcı Skill'iView repo

Entry point for Datadog onboarding. Takes a developer's plain-language goal, ensures a valid Datadog account with dd-account-setup, asks dd-product-recommender which products fit, detects the project's platform and cloud, then composes an ordered plan across the existing skills (agent install, product enable, verify, and optional cloud integration) and dispatches to each by source URL — honestly flagging products with no skill yet. Use when the user says "set up Datadog", "onboard my app / this

Dosyalar5 dosya

SKILL.md395 satır

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Kurulum

Önerilen

Tek komut — ajanınız otomatik olarak devreye alır.

AI aracını seç

Kurulum komutunu görmek için yukarıdan bir AI aracı seçin.

veya

Manuel Kurulum

Daha fazla adım

Arşivi indirin ve dosyaları projenize manuel olarak ekleyin.

Skill detayları

Versiyonv1.0.0

YazarDatadog Labs

Kategoriai-ml

Skill IDdatadog-labs/agent-skills/dd-orchestrator

Dosyalar5 dosya

İlgili skill'ler

Agent InstallInstall the Datadog Agent on Kubernetes using the Datadog Operator — required before enabling Single Step Instrumentation (SSI), which automatically instruments applications for APM without code changes. Only use if no Datadog Agent is deployed on the cluster yet.Agent InstallInstall the Datadog Agent on Linux hosts via SSH with Single Step Instrumentation (SSI) enabled — SSI automatically instruments applications for APM without code changes. Only use if no agent is installed yet.Agent Observability Auto ExperimentRun an iterative code-improvement hill-climb against real Datadog LLM-Obs data, locally, with Claude Code as the agent. Establishes a baseline eval, makes one focused change, re-scores with the same harness, keeps the change if it improves the score in the goal's direction (labeling within-noise gains tentative), and repeats. Use when the user says "run an auto experiment", "hill-climb this code", "iteratively improve X and measure the delta", "optimize this prompt/file against my traces", "auto Agent Observability Build Eval From AnnotationsFit a Datadog LLM-Obs evaluator to human labels. Takes an annotation queue, works out where in the trace the labelled property actually lives, drafts an LLM-judge that predicts the human label, scores that judge against the already-labelled rows with a metric agreed with the user, then hill-climbs it — inspect the errors, make one focused change, re-score, keep it only if it beats the best — for a bounded number of iterations, and finally publishes the winner to Datadog as a DISABLED evaluator (Agent Observability Eval BootstrapBootstrap evaluators from production traces — by default propose online LLM-judge evaluators and, after you confirm, create them in Datadog as disabled drafts (never auto-enabled); on request emit Python SDK code or a framework-agnostic JSON spec instead. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge confi Agent Observability Eval PipelineEnd-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a "continue" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `ag