「observation」の検索結果

Expert SwiftUI guidance for macOS apps — covers writing and reviewing code with runtime-level understanding of observation, concurrency, performance, and platform integration. Use when user asks to "build a SwiftUI view", "review my SwiftUI code", "fix observation issues", "bridge AppKit", "architect state management", "debug view updates", or works on any macOS SwiftUI project — building features, reviewing existing code, debugging view updates or performance, fixing concurrency issues, architecting state management, bridging AppKit, or making design decisions. Do NOT use for iOS/iPadOS-only SwiftUI, UIKit bridging, or basic Swift language questions. Focuses on non-obvious traps and deep patterns, not basics.

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通过 ClinicalTrials.gov API v2 实时检索官方临床试验注册数据。触发词包括 ClinicalTrials.gov、临床试验、NCT、试验注册、招募状态、phase 1/2/3、RCT lookup、interventional trial、observational study。专注实时单次查询(≤1000 条/请求),适合"某试验最新状态/招募信息/主次要终点"场景。批量历史分析请用 aact-bulk-trials,文献检索用 pubmed-search/europepmc。

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Web サーバ（nginx/apache、Linux 中心）の Phase 1 設定セキュリティレビュー。 target_profile.yaml で対象を確定し、MANIFEST.txt + manifest_attestation.txt で証跡 integrity を検証。 SSH 直接または証跡受領モードで 9 観点 (OS/リソース/ログ/ネットワーク/サービス/認証/監視/バックアップ/証明書) を read-only 走査。6 階層ガード (allowed/conditional/conditional_sensitive/ask-first/exceptional_sensitive_read/forbidden)、 3 軸補助 (Exploitability/Blast Radius/Service Criticality) の重大度判定、 observation_status による未確認の独立管理、evidence_dir/raw_evidence_store 分離、自己レビュー必須。 Windows/IIS は v1 hard fail。 Use when: Web サーバの設定セキュリティレビュー、Phase 1 監査、nginx/apache 構成監査、 EOL ランタイム調査、ログローテーション不備調査、ドキュメントルート漏えい調査。

Structured taste breakdowns of real products. The format for making taste observations legible and shareable. Use when analyzing products, writing case studies, documenting design observations, or teaching through example.

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Guides agents through creating and safely sizing a Replay Vision scanner: choosing the scanner type (monitor/classifier/scorer/summarizer), shaping the RecordingsQuery that selects sessions, and — crucially — estimating observation volume and checking the org's monthly quota before creating, so a broad scanner doesn't exhaust the budget on its first scheduled sweep. TRIGGER when: user asks to create, set up, or configure a Replay Vision scanner, OR when you are about to call vision-scanners-create, OR when widening an existing scanner's query or sampling_rate via vision-scanners-update. DO NOT TRIGGER when: only reading scanners or observations, deleting a scanner, or running an existing scanner against a single session on demand (vision-scanners-scan-session).

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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。

蓄積された observations を分析し、SKILL.md への改善提案を生成する。 Observe → Inspect → Amend → Evaluate ループの Inspect/Amend フェーズ。 `--apply` フラグで承認後の適用も可能。

Extract study sites, observational variables, coordinates, climate-forcing sources, and cited data sources from ecosystem-modeling papers, PDFs, supplements, or article text, then plan or create EcoSIM simulation workflows and run folders for those sites. Use when Codex is asked to read a paper for EcoSIM feasibility, including requests phrased as "read paper for ecosim feasibility"; read a paper for sites/data availability; reproduce a published site simulation in EcoSIM; compare EcoSIM outputs to paper observations; or convert paper-derived AmeriFlux, FLUXNET, US NLDAS-domain, non-US ERA5, crop, peatland, forest, grassland, wetland, or unmanaged field-site information into EcoSIM inputs and runnable cases.

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Sort AmeriFlux BASE-BADM BIF variables from Excel or CSV files into observation, management-event, static metadata, and plotting-ready target time-series tables. Use when Codex is asked to sort AmeriFlux BIF variables into time series; organize BADM/BIF variables by date; pivot AMF *_BIF_*.xlsx files by GROUP_ID; output total LAI, leaf mass per area, above-ground biomass, canopy height, fruit yield, or total yield time series; write MATLAB readers for BIF time-series plotting; or prepare dated AmeriFlux site observations and management records for EcoSIM workflows.

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AEM as a Cloud Service Java/OSGi best practices, guardrails, and legacy-to-cloud pattern transformations. Use for Cloud Service–correct bundles, deprecated APIs, schedulers, ResourceChangeListener, replication, Replicator, JCR observation (javax.jcr.observation.EventListener), OSGi Event Admin (org.osgi.service.event.EventHandler), DAM AssetManager, BPA-style fixes, HTL (Sightly) Cloud SDK lint warnings (data-sly-test redundant constant value comparison), or any time you need the detailed pattern reference modules under this skill.

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Slackの認証やメッセージ操作、OpenClawの受信・返信を検証するスキル。APIで確かめた結果と実際の画面操作を区別し、証拠と後片付けを確認します。

• Slackの認証やアクセス権の確認
• メッセージの作成・編集・削除テスト
• OpenClawの受信・返信の検証

書きたいテーマを質問で掘り下げ、印象的な一文や体験、比喩、考えかけの断片を集めます。構成は決めず、文章の素材を一つのMarkdownファイルに蓄積します。

• 記事の構成を決める前の素材集め
• 体験や観察を文章の断片にしたいとき
• 考えの軸になる比喩や言葉探し

SwiftUIの画面づくりで、状態管理、部品の分割、画面遷移、描画性能を整えるスキル。iOS・macOS向けの実装やレビューを具体例で支えます。

• SwiftUIの状態管理を選びたいとき
• NavigationStackの遷移設計
• 一覧の描画性能を改善したいとき

顧客や取引先が参加するチャットで、エージェントの応答条件と送信内容を定めるスキル。呼びかけへの応答、内部情報の保護、承認用の下書きへの切り分けを扱います。

• 顧客グループでの応答ルール設定
• 取引先への返信から内部情報を守りたいとき
• 価格や契約の回答を承認用下書きに分ける

Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). (gstack)

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Turn a Caveman optimization observation into an operator-chosen candidate with a paired baseline evaluation. Use when asked to inspect or evaluate a Caveman optimization report. Needs explicit approval.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when the user asks to "search memory", "what do you remember about X", "check claude-mem", "mem search", "find past observations", "what did we do last session", or wants prior-session context about a project, decision, file, or task. Searches the user's Claude-Mem (cmem.ai) memory.

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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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Explain how claude-mem captures observations, when memory injection kicks in, and where data lives. Use when the user asks "how does claude-mem work?" or "what is this thing doing?".

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QA the omo Codex Light edition (lazycodex / packages/omo-codex) itself, in strict isolation so ONLY our plugin is exercised, never the user's real ~/.codex. The first-party method drives the real `codex app-server` against an isolated CODEX_HOME plus a LOCAL mock model (no real API call), and proves a plugin hook fired by asserting hook/started + hook/completed notifications. Also: isolated install verification, per-component hook probes, a tmux TUI smoke, and runtime log observation (RUST_LOG / logs SQLite / /debug-config). Ships tested helper scripts each with a --self-test. Use whenever someone changes anything under packages/omo-codex or wants to QA, smoke-test, verify, or debug the Codex plugin, its hooks/components, the installer/config.toml, the app-server flow, or the Codex TUI. Triggers: codex qa, qa codex, codex-qa, test codex plugin, verify codex hook, codex app-server, lazycodex qa, isolated CODEX_HOME, prove codex hook fired, codex tui test.

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Supports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a specified future observation time, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality ("turned" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.

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Formulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.

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Supports geospatial research workflows for remote sensing, vector and raster GIS, spatial statistics, terrain and network analysis, and machine learning for Earth observation. Use when processing satellite imagery, aligning coordinate systems and raster grids, accessing STAC catalogs, analyzing geospatial time series, or implementing scientific GIS workflows in Python, R, Julia, JavaScript, C++, Java, Go, or Rust.

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

日本語の概要は準備中です。原文の説明を表示しています。