'spans' 검색 결과

Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.

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• Improve a prompt using failed production traces
• Fix responses that use the wrong output format
• Review unsupported answers in retrieval-based responses

Create shareable links that open specific Arize resources, including traces, datasets, and evaluators. Use IDs from logs or exported data, with suitable time ranges, to direct teammates to the relevant view.

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• Share a trace with teammates
• Open an Arize view from IDs in logs
• Link directly to a dataset or evaluator

Navigate the Composio SDK monorepo, a repository containing multiple packages, and follow its shared development rules. Use it to decide where changes belong, handle generated files and release metadata, and prepare branches and pull requests.

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• Planning changes across multiple packages
• Deciding where code belongs
• Checking generated-file and release metadata rules

Evaluate a strategic choice through multiple executive roles, each forming an independent view before discussion. Produce a board memo with recommendations, votes, dissent, risks, and success or stop conditions for the founder's decision.

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• Reviewing a pricing change across business functions
• Comparing fundraising with spending cuts
• Gathering independent views from executive roles

Track work across sessions with persistent issues, dependencies, and progress notes. Use the bd command-line tool to find unblocked work, claim issues, record completion, and recover task context after conversation compaction.

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• Track feature work across multiple sessions
• Find work that has no unresolved blockers
• Recover task context after conversation compaction

Explore relationships between functions and files using a prebuilt code graph, a map of code dependencies. Trace bugs, inspect callers and execution paths, and assess the impact of refactoring or proposed changes.

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• Explore callers and dependencies in unfamiliar code
• Trace a bug across multiple files
• Assess the impact of renaming or removing code

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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• Trace execution steps in a Python AI application
• Select tracing integrations for an existing framework
• Add manual tracing with @observe

Keep context for work that spans multiple sessions without belonging to a specific project phase. The skill manages lightweight persistent threads, letting you create, list, resume, check, and close them as work progresses.

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• Keeping context for work across sessions
• Recording work outside a specific phase
• Listing open or resolved threads

Add Sentry error reporting and performance monitoring to services, scheduled jobs, and database operations. Follow examples for capturing exceptions with useful context and measuring execution time through spans, which record individual operations for investigation.

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• Adding error reporting to an API
• Recording failures and execution time for scheduled jobs
• Measuring database and asynchronous operations

Design, review, and implement Gantt charts for project schedules. Assess source data, task dependencies, and resource needs, then define suitable views, editing behavior, rendering tools, accessibility, exports, and tests—or recommend a clearer alternative.

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• Choose between a Gantt chart, Kanban board, or timeline
• Map project-management exports into schedule data
• Review dependencies, critical path, and baseline variance

Coordinate AltLLM Portal CLI tasks across wallet login, API key management, credit balances, billing history, and crypto top-ups. This umbrella skill routes each part of a broader workflow to the relevant specialized skill.

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• Log in with a wallet and create an API key
• Review credit balance, transactions, and usage together
• Create a payment link and check its status

Coordinate Cloud Claw workflows from launching an agent deployment to managing its virtual machine. It routes work to focused skills for setup, starting and stopping machines, viewing logs, renewing usage, and accessing dashboards through local altllm commands.

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• Launch an agent deployment and manage it afterward
• Inspect, start, or stop an existing virtual machine
• Review logs and access a deployment dashboard

Turn a multi-file development task or technology migration into a specification with acceptance criteria, then launch background implementation and verification. The workflow clarifies missing requirements, reviews the specification, and tracks progress and blockers through linked jobs.

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• Implementing a feature across multiple files
• Migrating a framework or programming language
• Replacing authentication, storage, or API layers

Plan complex coding changes by exploring existing code, clarifying requirements, and tracing effects across the system. Produce a PLAN.md with recommended changes, affected files, and verification steps, then implement after the user approves.

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• Planning a feature that spans multiple files
• Comparing architectural approaches
• Clarifying requirements before coding

Review explicitly selected tests or bounded pull request changes without running or editing code. Receive a follow-up decision for each test, A–F quality bands where assessable, evidence-based notes, and concrete suggestions for improvement.

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• Reviewing a named test method
• Assessing every test in explicitly selected files
• Preparing per-test feedback for pull request changes

How to pick the owning team and the priority for an inbound support ticket, including what to do when a ticket spans two teams. Use when triaging a ticket.

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Add OpenTelemetry tracing to a new flow or hot path, frontend or backend. Use when building a user-facing flow, a cross-service call, a queue or background job, or code on a latency-sensitive path; when adding spans, span attributes, or trace propagation; or when a trace in Tempo/Datadog is missing, disconnected, or opaque.

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持久战略：面对长期、复杂、短期不可能速胜的任务，客观评估态势，划分战略防御 / 相持 / 反攻三阶段，判定当前所处阶段，设置阶段转换条件，并在困难阶段仍给出局部进攻点。当任务跨多个会话或多轮迭代、资源暂时不足、或出现"想放弃"与"一把梭"两种情绪时触发；任务能在当前会话完成时不触发。 English: Protracted strategy. For long-horizon work that cannot be won quickly, assess the situation objectively, divide the effort into defense / stalemate / counter-offensive stages, name the current stage and its exit condition, and always identify a local offensive even while accumulating. Trigger when work spans many sessions or iterations, resources are temporarily short, or you notice either defeatism or a rush to finish; skip when the task fits in the current session.

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Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.

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Map the full ecosystem of touchpoints, channels, and relationships across a service. Use when the experience spans more than one product. For one persona's linear journey use `journey-map` (design-research); for backstage operations use `service-blueprint`.

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Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why is it slow", "reduce latency", "set up observability", "CloudWatch dashboard", "how much does my agent cost", "cost optimization", "logs not showing up", "logs missing", "spans not found", "eval failing", "eval error", "dev traces", "local traces", "agentcore dev traces", "traces to CloudWatch". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.

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Guides migration, provisioning, search, log-analytics, trace-analytics, and Agentic AI Assistant workflows for Amazon OpenSearch Service and Serverless across six capabilities — migration (Solr/ES/self-managed into AOS/AOSS, schema/query translation, sizing, cutover); provisioning (domain + AOSS lifecycle, upgrades, FGAC, monitoring); search (vector / semantic / hybrid / RAG with Bedrock); log-analytics (PPL, OSI, anomaly detection, Dashboards); trace-analytics (OTel spans, service maps, Data Prepper); ai-assistant (natural language data exploration, incident investigation, root cause analysis). Triggers on OpenSearch, AOS, AOSS, Elasticsearch, Solr, vector/k-NN/semantic/hybrid search, RAG, log analytics, PPL, trace analytics, ISM, FAISS, HNSW, Migration Assistant, UltraWarm, OR1, query my data, analyze logs, investigate errors, root cause analysis.

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Route broad or ambiguous AgentKit SEO work to the right module while keeping context scoped. Use when a request spans multiple surfaces, asks for overall digital-presence strategy, involves provider or install architecture, needs agent-context planning, or the correct platform skill is unclear.

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Designs how an organization is structured — reporting lines, team boundaries, spans and layers, role definition, and workforce planning against the strategy. Use this to structure a new team, restructure an existing one, resolve unclear ownership between teams, plan headcount, or diagnose why a team underperforms for structural rather than individual reasons.

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