“observe”的搜索结果

Use for anything related to EAS Observe - adding `expo-observe` to an Expo project (AppMetricsRoot/ObserveRoot HOC, markInteractive and ObserveInteractiveMarker, the useObserve hook, the Expo Router / React Navigation integrations for per-route metrics, user-defined events via `Observe.logEvent`, error reporting via ObserveErrorBoundary and `Observe.reportError`, native crash reporting, source map upload for symbolicated stack traces, and runtime config such as sampleRate and dispatchInDebug), querying via the EAS CLI (`eas observe:metrics-summary`, `observe:metrics`, `observe:routes`, `observe:events`, `observe:session`, `observe:versions`, `observe:errors`, `observe:event`), interpreting the resulting metrics (cold/warm launch, TTR, TTI, navigation cold/warm TTR, update download, and the TTI frameRate/device/network params for triaging slow startups), or shipping an Observe integration inside a third-party package.

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This skill should be used when user asks to "query OpenObserve", "create OpenObserve dashboard", "edit OpenObserve panel", "fetch OpenObserve logs", "run OpenObserve search", "list OpenObserve streams", "ingest into OpenObserve", or works with OpenObserve Cloud / self-hosted via REST API. Covers auth, search/SQL, streams, dashboards (CRUD + per-panel ops), the v8 panel JSON schema, and known pitfalls (re-aggregation, hash concurrency, microsecond timestamps).

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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

スキル使用後の observation（結果・問題・フィードバック）を記録する。 スキルの改善サイクル（Observe → Inspect → Amend → Evaluate）の起点。 使用後に `/skill-observe <スキル名> <結果> [問題/フィードバック]` で呼び出す。

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Set up claude-mem for Grok Bot with a local worker and CMEM Pro service for recording observations. The skill also covers an optional host-login setup and remote cmem.ai connection, without requiring Cursor.

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• Install claude-mem for Grok Bot
• Set up a local worker with CMEM Pro
• Choose the optional host-login observer

Set up claude-mem in Cursor with a local or remote worker, the process that runs its work. Follow configuration steps for remote inference through cmem.ai or a local observer using the host's login.

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• Installing claude-mem with a local worker in Cursor
• Connecting Cursor to a remote claude-mem worker
• Configuring host-login or cmem.ai inference

Observe malware in an isolated Cuckoo Sandbox environment and review its processes, network traffic, and system changes. Use the findings to trace infection stages, identify persistence mechanisms, and extract indicators of compromise for behavioral reports.

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• Investigating suspicious files after static analysis
• Tracing multi-stage infections and persistence mechanisms
• Extracting indicators of compromise from observed behavior

当用户想使用 HiAgent 观测(observe)能力（API Token、Trace Spans 查询）时，务必使用这个技能给出可直接执行的 cli-anything-hiagent observe 命令与排障步骤。尤其适用于用户提到 agent-harness、cli_anything、--json、workspace-id、custom-app-id、VOLC_ACCESSKEY/VOLC_SECRETKEY、observe token、observe trace，以及希望用命令行(cli-anything-hiagent / python -m cli_anything.hiagent_sdk)。trace-ai-process / trace-ai-history / alert-ai-process / RuleID / IsStream

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Daily wisdom review applying Charlie Munger's mental models to your work and thinking. Use when asked to review decisions, analyze thinking patterns, detect biases, apply mental models, do a "Munger review", or run the Munger Observer. Triggers on scheduled daily reviews or manual requests like "run munger observer", "review my thinking", "check for blind spots", or "apply mental models".

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Reconstruct an incident timeline from recorded screens and conversations, supported by available logs and monitoring. Organize symptoms, changes, response actions, and recovery evidence while distinguishing observed facts from suspected causes.

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• Reconstructing events around a system incident
• Reviewing response actions and recovery evidence
• Separating observed facts from suspected causes

オブザーバーの向き(facing)、入力側/出力側の定義、connects配置の仕様。observer使用時に必ず読む。

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Format completed work in Grok Bot's claude-mem workflow as searchable XML records with a title, facts, file paths, and narrative. Return a skip marker for idle or initialization states, or when no tool results exist.

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• Recording completed work as searchable observations
• Distinguishing idle states from work worth recording
• Standardizing output for the claude-mem generator loop

Audit page loading and defer offscreen images, videos, iframes, and heavy components until needed. Use measurements and page context to identify suitable changes while keeping images needed for the initial view loaded with priority.

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• Investigate slow initial page loads
• Defer images below the initial viewport
• Delay offscreen videos, iframes, and heavy components

Review long pages and dashboards to defer heavy components until they approach the visible screen area. Identify unnecessary initial JavaScript, introduce visibility-based loading, and preserve layout space so scrolling content is ready when needed.

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• Defer charts located far down a dashboard
• Review loading behavior on long landing pages
• Load embeds and related products as they approach the screen

Check whether a code change works by running the app and using its real interface. Exercise the intended flow and nearby error cases, capture outputs or screenshots, and report observed behavior with a clear verdict.

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• Checking changed behavior before committing code
• Probing invalid inputs and repeated operations
• Preparing verification reports with captured evidence

Create and maintain repository instructions for AI agents in AGENTS.md. Check rules against project evidence, preserve existing requirements during revisions, and record observed mistakes so future agents receive concise, relevant guidance for working in the codebase.

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• Creating or consolidating repository instructions for AI agents
• Updating and auditing rules after project changes
• Recording recurring or costly agent mistakes

Recover hidden malware settings and communication parameters in an isolated CTF environment. Document how values are decoded, preserve supporting evidence, and connect recovered fields to the sample’s observed behavior.

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• Recovering hidden malware configuration fields
• Decoding command server addresses and beacon settings
• Separating loaders from staged payloads

Test a local web app in a real browser, checking the page structure, console messages, and network activity. Follow controls observed on the page and record evidence showing whether the tested flow passes or fails.

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• Verify a web app flow in a real browser
• Check page state, console messages, and network activity
• Record evidence of test results

Build and run evaluations for AI applications using Phoenix. Start with observed failures, create checks in code or use a language model as a judge, run experiments, and validate automated judgments against human labels.

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• Turning observed failures into evaluation criteria
• Building code-based or language-model evaluators
• Evaluating retrieval and answer faithfulness in RAG

Investigate suspicious mobile apps by examining permissions, network traffic, and runtime behavior. Use observed evidence to identify signs of data theft, SMS interception, credential stealing, or communication with attacker control servers, then classify the suspected malware.

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• Triaging suspicious APK or IPA samples
• Investigating possible data leaks from mobile apps
• Checking for SMS interception and credential theft

Helps build or improve reusable OpenCLI website commands in JavaScript. It uses observed page behavior, captured requests, and existing adapters to guide implementation, then checks definitions and tests real command behavior and failure handling.

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• Turning repeated website tasks into OpenCLI commands
• Improving an existing website adapter
• Investigating API requests before implementation

Build a computer-control loop where Cua Driver observes and acts, while TypeSafe Jev chooses from predefined actions. Validate each choice, execute one action at a time, and verify the result before continuing.

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• Build a Jev integration for browser automation
• Create action candidates for native desktop apps
• Reject stale or unknown action choices

Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.

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• Audit existing articles for evidence and credibility gaps
• Revise article structure and references for AI answers
• Record citations observed across AI services

Build a daily or weekly worklog from recorded activity and checked outcomes. Separate plans, observed work, and verified completion, then summarize useful results and unfinished tasks with source links and clearly marked gaps or estimates.

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• Preparing a daily work report
• Reviewing weekly outcomes and unfinished tasks
• Summarizing work time using recorded active minutes