「observe」の検索結果

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`, 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`), 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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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。

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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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。

Use this when fulfilling claude-mem observer jobs on Grok Bot: reply only skip_summary or one full observation XML, never prose.

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Use this when setting up claude-mem on Grok Bot: local worker plus CMEM Pro observer (default), optional host-login observer, or remote cmem.ai. No Cursor required.

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Detonate malware samples in Cuckoo Sandbox to observe runtime behavior — process creation, file system and registry changes, network communications, and API calls — and generate behavioral reports for classification and IOC extraction. Use when a sample has passed static triage and needs dynamic/behavioral analysis, when mapping a full infection chain, or when building YARA/behavioral signatures from observed sandbox activity.

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Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents, LlamaIndex, Pydantic AI, CrewAI, and others); choose between a native integration and manual instrumentation; set span types, tags, or metadata; or send DeepEval-SDK traces to Confident AI's Observatory. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill), or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill). This skill is purely DeepEval-SDK instrumentation — producing well-formed traces, not running evals.

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Apply the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to assumptions. This is a reasoning review, not a deterministic vault health check or repair workflow.

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Observe Before Editing

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Use when a task needs an app on the user's own screen — a native desktop application (instrument control software, Origin, GraphPad, ImageJ, a reference manager, a spreadsheet), or a desktop browser window that needs window-level control. Read before the first `computer` call. Covers the observe-act-observe loop, why element indexes go stale, which action to prefer for which control, what "unverified" means and why it must never be reported as success, and the recovery for each error code. Not for page-level web automation (use the browser connector) and not for anything that can be done from the shell.

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Use under time pressure when the situation is still changing and you must act before certainty — cycle Observe→Orient→Decide→Act on ~70% confidence, then re-observe.

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Use when asked 公开课怎么准备, 帮我写说课稿, 优质课比赛, 评优课, 教研课, 汇报课, 青年教师赛课, 试讲, or prepare an observed lesson in a Chinese school. Produces a 公开课 preparation pack in Chinese: a 说课稿 (说教材, 说学情, 说教学目标, 说重难点, 说教法学法, 说教学过程, 说板书) timed to the required length, the lesson's highlights (亮点) and how observers will see them, a rehearsal and 磨课 plan, contingency plans, and answers to likely 评课 questions.

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Expert guidance for simulating RoadRunner scenarios via the MATLAB programmatic API and Simulink co-simulation. Use when the user wants to run a simulation, step through a simulation, control actors during co-simulation, add observers, attach sensors, retrieve simulation logs, or read/write scenario variables. Covers simulateScenario, createSimulation, ScenarioSimulation set/get, ActorSimulation getAttribute/setAttribute, addObserver, SensorSimulation, Simulink co-sim blocks, and publishActorBehavior. NOT for project setup, scene building, scenario authoring, or trajectory export.

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A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum

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embeddedskills 的薄编排层，用于在当前 workspace 中发现工程、选择 build/flash/debug/observe 后端、串联 .embeddedskills/state.json，并聚合底层 skill 的结果。 当用户明确输入以下命令之一："一键构建烧录"、"自动诊断"、"串起 build -> flash -> debug -> observe" 或显式调用 /workflow 时触发。

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Observe Before Editing

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Step 1 of debugging: observe the symptom carefully before jumping to conclusions.

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Design service mesh observability — distributed tracing, service-level metrics, and dashboards. Use when asked to "add service mesh observability", "set up distributed tracing in the mesh", or "build service-level dashboards".

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Log a bias-log / vipassana observation to the memex store the moment a reasoning pattern surfaces, mid-session or at close. Use when you catch a bias in your own reasoning (anchoring, confirmation, trust-the-summary), when a pace tell or verification gap appears, or when meditate/observe produces a reflection worth persisting across sessions. Primary path pipes the body to `memex add` over stdin (`--title` required) then runs the `meditate-vipassana` extractor to make it queryable; secondary path appends a numbered bullet to `docs/OBSERVATIONS.md`.

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Logs the outcome of a skill execution to observations/<skill-name>/runs.md. Trigger on: "log this run", "skill worked", "skill failed", "this didn't work", "log the outcome", "record this", "note that", or after any skill completes with a clear success, partial, or failure outcome. Creates the observations file if it does not exist, then appends an entry with date, task description, skill used, outcome, what worked, what failed, and any error messages observed.

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Profiles a threat actor by aggregating observed ATT&CK techniques into a TTP profile and comparing it against known-group technique sets to estimate similarity and candidate attribution. Activates for requests to profile a threat actor, compare observed TTPs to known groups, or estimate attribution from technique overlap.

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