Monitoring & ops skills.
3,144 monitoring & ops skills, including lark-okr, run-train and agent-eval, are listed on Ahel today. Each one has a page of its own that says what it does and whether Ahel can serve it in Claude, Claude Code, ChatGPT, Codex and Cursor.
Category: Monitoring & ops
3,144 results · page 90 of 105
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kb-audit-querySkillMonitoring & ops
Filter and summarise the cross-library KB audit log. Reports confidentiality events (attribution drops, synthesis aborts, dispatcher failures, no-evidence markers, cross-library promotions) over a date range, by event type, and by source handle.
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kb-strands-agentcoreSkillMonitoring & ops
Knowledge of the Strands Agents framework. Covers agent creation, tool definitions, event handling, and conversation history management (CDK: /kb-agentcore-cdk, Observability: /kb-agentcore-observability, Identity authentication: /kb-agentcore-identity).
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llama-cppSkillMonitoring & ops
Run GGUF models directly, load LoRA adapters, benchmark inference speed, and serve models via llama-server using llama.cpp. Includes Qwen 3.5 serve scripts (9B dense + F16, 35B MoE) with asymmetric KV cache and thinking mode. Secondary to Ollama; use when needing direct model control or LoRA hot-loa
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micrometer-tracingSkillMonitoring & ops
Distributed tracing and observability with Micrometer and Spring Boot 3
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model-gateway-routingSkillMonitoring & ops
Model gateway / LLM router architecture: a control point in front of multiple models/providers for routing (cost/quality/latency), fallback, rate limiting, caching, observability, and governance. Architect-level, multi-provider.
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score-2xSkillMonitoring & ops
The generic 2×-then-discover loop the scorecard family runs — enumerate exhaustive raw debt, drive it down 2× with genuine fixes, rescore to PROVE the drop, and continuously harden discovery under a new metric...
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spring-aopSkillMonitoring & ops
Spring AOP (Aspect-Oriented Programming) for Spring Boot 3.x. Covers @Aspect, pointcut expressions, @Around/@Before/@After advice, custom annotations, logging, security, caching, and transaction patterns. Use for cross-cutting concerns.
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spring-batchSkillMonitoring & ops
Spring Batch for batch processing in Spring Boot 3.x. Covers Job, Step, ItemReader/Processor/Writer, chunk processing, job parameters, restart, skip/retry, partitioning, and monitoring.
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spring-cloud-basicsSkillMonitoring & ops
Spring Cloud patterns for microservices in Spring Boot 3.x. Covers Service Discovery, Config Server, API Gateway, Circuit Breaker, Load Balancing, and Distributed Tracing.
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spring-schedulingSkillMonitoring & ops
Spring Scheduling and Async for Spring Boot 3.x. Covers @Scheduled, @Async, cron expressions, ThreadPoolTaskExecutor, task monitoring, distributed scheduling with ShedLock, and error handling.
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add-llm-evalsSkillMonitoring & ops
Use this when adding evaluation to an LLM/agent app - measuring output quality (correctness, faithfulness, relevance, safety) rather than just watching traces. Trigger on "add evals", "test my prompt", "is my RAG accurate", "catch regressions", "score outputs", or setting up an eval suite in CI. Cov
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add-llm-fallbacksSkillMonitoring & ops
Use this to make an LLM app resilient to provider failures, rate limits, timeouts, and outages. Trigger on "handle LLM API errors", "add retries/fallbacks", "the app breaks when OpenAI is down", "rate limit errors", "make my LLM calls reliable", "timeout handling". Add retries, timeouts, and model/p
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add-llm-guardrailsSkillMonitoring & ops
Use this to add safety and security guardrails to an LLM/agent app - blocking prompt injection, PII leakage, jailbreaks, toxic output, off-topic responses, or invalid structured output. Trigger on "add guardrails", "prevent prompt injection", "stop PII leaks", "validate the model's output", "make th
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check-answer-consistencySkillMonitoring & ops
Use this to get a cheap, reference-free signal that an LLM answer might be made up, by sampling the same prompt a few times and measuring agreement. Trigger on "is this answer reliable", "flag low-confidence answers", "cheap hallucination check", "confidence score without a ground truth", "self-cons
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choose-observability-stackSkillMonitoring & ops
Use this to recommend an LLM observability / evaluation tool or stack for a specific situation. Trigger on "which observability tool should I use", "compare Langfuse vs Phoenix vs LangSmith", "what's the best LLM monitoring for us", or picking an eval/tracing/gateway tool given constraints (self-hos
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collect-user-feedbackSkillMonitoring & ops
Use this to capture user feedback on LLM outputs (thumbs up/down, edits, corrections, implicit signals) and feed it back into observability and evals. Trigger on "add thumbs up/down", "collect feedback on responses", "how do I know if users like the answers", "improve from real usage", "human feedba
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compare-llm-modelsSkillMonitoring & ops
Use this to pick or switch the LLM behind a feature, based on evidence instead of hype or the newest release. Trigger on "which model should I use", "is GPT/Claude/Gemini/Llama better for this", "should I switch models", "can a cheaper model do this", "compare models for my use case". Evaluate on YO
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debug-agent-from-tracesSkillMonitoring & ops
Use this to diagnose WHY an LLM agent or chain produced a wrong, empty, slow, or expensive result, by reading its observability trace. Trigger on "my agent gave the wrong answer", "the chain returned nothing", "why is this so slow/expensive", "debug this trace/run", or when a trace tree is available
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detect-hallucinationsSkillMonitoring & ops
Use this to detect when an LLM is making things up, so you can flag or block confident-but-wrong answers before users see them. Trigger on "detect hallucinations", "is the model making this up", "flag unreliable answers", "hallucination check", "confidence scoring for LLM output", or hardening a RAG
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estimate-llm-costSkillMonitoring & ops
Use this to estimate what an LLM call or feature will cost, and to compare models on price, before or after shipping. Trigger on "how much will this cost", "estimate my OpenAI/Anthropic bill", "is a cheaper model worth it", "cost of this prompt", "project my LLM spend". Ships a runnable, tested calc
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eval-driven-developmentSkillMonitoring & ops
Use this to build or change an LLM feature the reliable way, by writing evals first and iterating against them, instead of tweaking prompts by vibes. Trigger on "how do I improve this prompt", "my changes keep breaking other things", "how do I know if this is better", "iterate on my agent", or any p
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fit-auditSkillMonitoring & ops
Rank this catalog's skills by what each would have saved this specific adopter, every top pick citing a real incident from their own history, and for a team judge each skill against the incumbent stack as well. Use when someone is considering the catalog and has not run setup, when the user asks whi
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instrument-llm-observabilitySkillMonitoring & ops
Use this when adding tracing/observability to an LLM or AI-agent application - capturing prompts, tool calls, token usage, latency, and cost per step. Trigger whenever someone wants to "add tracing", "instrument", "monitor", "see what my agent is doing", or debug an LLM app in production. Prefer ven
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Master Agent OrchestrationSkillMonitoring & ops
Guide for the Master Agent to orchestrate, monitor, and merge tasks across the 3-tier multi-agent architecture
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measure-agent-task-successSkillMonitoring & ops
Use this to measure whether an AI agent actually completed its task end to end, not just whether individual LLM calls looked fine. Trigger on "is my agent working", "measure agent success rate", "evaluate my agent", "how good is my agent", "agent completion rate", or evaluating a multi-step/tool-usi
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monitor-rag-qualitySkillMonitoring & ops
Use this to measure and monitor the quality of a RAG (retrieval-augmented generation) pipeline - whether it retrieves the right context and answers faithfully. Trigger on "my RAG gives wrong answers", "is my retrieval any good", "the chatbot makes things up", "evaluate my RAG", "improve RAG accuracy
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optimize-promptsSkillMonitoring & ops
Use this to improve a prompt systematically instead of hand-tweaking it by feel. Trigger on "optimize my prompt", "make this prompt better", "the prompt isn't working well", "auto-tune my prompt", "few-shot example selection", or when prompt quality has plateaued. Optimize against an eval set with a
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red-team-llm-appSkillMonitoring & ops
Use this to adversarially test an LLM/agent app before attackers do - prompt injection, jailbreaks, data exfiltration, tool misuse, and unsafe output. Trigger on "red team my LLM", "test for prompt injection", "is my agent secure", "jailbreak testing", "security review of my AI app", especially befo
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redact-pii-for-tracingSkillMonitoring & ops
Use this when adding LLM observability to an app that handles sensitive data (finance, healthcare, PII) and you must NOT ship raw prompts/PII to a third-party tracing backend. Trigger on "redact traces", "PII in observability", "can we self-host tracing for compliance", "GDPR/HIPAA/SOC2 and LLM logg
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reduce-llm-costSkillMonitoring & ops
Use this to cut the cost of an LLM app using observability data. Trigger on "my OpenAI/Anthropic bill is too high", "reduce token usage", "the app is expensive", "optimize LLM cost", "why am I spending so much on the API". Find the expensive spans first (measure), then apply the cheapest wins. Don't
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