「observation」の検索結果

Nexscope Ozon Russia category drill-down product list by Russian category path. Returns all products in a category with complete metrics: sales, revenue, price, rating, stock, turnover, lost revenue, supporting multi-dimensional numeric filters, sorting, and currency conversion. Use for category bestseller mining, blue-ocean insight discovery, category ranking analysis, brand landscape observation. Trigger when the user mentions Ozon category drill-down, Ozon category products, Ozon blue-ocean mining, Ozon category bestsellers, Ozon category ranking, Ozon subcategory structure, Ozon niche SKUs, Nexscope category, Ozon category drill-down, Russian marketplace niche, Ozon niche mining, Ozon subcategory bestseller. Also trigger when the intent is to view all products and their sales/price/ranking performance under an Ozon category path, even without explicitly mentioning Nexscope.

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Diagnose and fix mini-program failures through reproducible, evidence-driven root-cause analysis across source, state, asynchronous timing, caches, mock data, build versions, platform behavior, devices, cloud calls, and external services. Use when users report white screens, crashes, stale or jumping values, missing data, intermittent behavior, permission failures, performance stalls, device-only defects, API or cloud-function errors, source-versus-build mismatches, or regressions whose cause is not yet established. Freezes the observation environment, ranks competing hypotheses, runs discriminating checks, creates a failing regression test before repair, covers analogous states, and never hides a known failure with longer waits, swallowed errors, forced success, or cosmetic patches.

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。

Kolu's project-wide default for filesystem monitoring is `@parcel/watcher`. Reach for this skill when adding or modifying any code that watches files or directories — recursive subtree watching, single-file observation, fs.watch alternatives, chokidar replacement, inotify/FSEvents/watchman backend selection, ignore globs, watcher debouncing, or refcounted shared subscriptions. Covers backend dispatch, the watchman invocation path, ignore handling, post-install reconciliation, and the failure modes Kolu's logger surfaces.

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。

PMU observation pattern for agent health monitoring. Runs patrol loops, detects stalled agents, sends nudges, and escalates persistent failures to the mayor. Never modifies agent work or resolves conflicts.

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。

Following what organisms do — behavior, metamorphosis, host-plant relationships, social dynamics, and the long-form ethograms that reveal how species actually live. Covers the vocabulary of ethology, the structure of a life cycle, the classic categories of species interaction, the discipline of recording behavior without premature interpretation, and the gap between a single observation and a statistically useful record. Use when the task is to understand or record what a species does rather than what it is.

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。

Foundational concepts in nutrition science — macronutrients, micronutrients, energy balance, Atwater factors, reference intakes (DRI/RDA/AI/UL), food composition tables, and the methods of human nutrition research. Grounds the rest of the department in a shared vocabulary and in the measurement limits of the field, including controlled-trial history, observational-study limits, and the biochemical basis for calorie accounting. Use when a question asks what something "is" nutritionally, how energy and nutrients are measured, or what the reference numbers mean.

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。

Security hygiene for GSD's self-modifying skill and agent system. Use this skill whenever: creating, editing, or deleting skill files (.claude/skills/, .claude/commands/), modifying agent definitions (.claude/agents/), working with YAML configuration or chipset files, handling JSONL observation data (.planning/patterns/), processing community-contributed skills or chipsets, any file path operations that could involve user input, or when installing/updating project-claude configuration. Also activates for discussions about skill-creator security, trust models, or content hygiene.

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。

Celestial coordinate systems and sky positioning. Covers horizon (altitude-azimuth), equatorial (right ascension-declination), ecliptic, and galactic systems; epoch and precession; coordinate transformations; planisphere use; and practical sky-locating from any latitude and date. Use when locating objects, planning observations, converting catalog coordinates, or teaching the geometry of the sky.

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。

Bisimulation game for resilient skill dispersal across AI agents with GF(3) conservation and observational bridge types.

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。

Bridge active inference theory with robot control using K-Scale's JAX/MuJoCo stack. Use when connecting predictive coding to locomotion policies, mapping KL divergence minimization to RL training, applying mean field approximation to robotics state estimation, or implementing sim2real as inference about future observations.

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 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。

Relay agent results without corrupting exact counts, failure reasons, source scope or status. Use when receiving agent output or handing results to another actor; distinguish observations from attributed conclusions and reference complete artifacts.

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。

Describe images, screenshots, diagrams and charts from visual inspection. Preserve visible text, counts, labels, directions and uncertainty; distinguish observation from interpretation. Use for image summaries, screenshot explanations and diagram descriptions.

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。

Use when debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.

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。

Investigate pytest failures without treating tests or implementation as automatic authority. Use when diagnosing regressions, debugging test errors, or deciding whether to fix a test or code; trace the contract, product, fixture, and observation before choosing an evidence-backed correction.

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Use when facing unknowns, debugging without a clear cause, or making architecture decisions — enforces hypothesis-driven scientific reasoning through observation, hypothesis formulation, prediction, experiment design, and evidence-based conclusion. Use when previous attempts have failed or the problem space involves uncertainty.

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。

Scientific validation protocol for verifying fixes work through observation, not assumption. Use when claiming a bug fix, code change, refactoring, or implementation is complete — enforces reproduce-broken-state then define-success-criteria then apply-fix then verify-outcome. Success means observing intended behavior, not absence of errors.

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。

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.

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。

Generate a topic-focused briefing in HTML from public news sources. Use when user asks for a briefing / observation / digest / 简报 / 观察 on any subject — region (Middle East, ASEAN, India), industry (semiconductors, EV supply chain, AI), policy issue (AI regulation, critical minerals, cross-border payments), institution (Fed, ECB, IMF), or theme. Output is a single self-contained HTML file with blue-color "TOPIC BRIEF" branding, optimized for both browser viewing and direct copy-paste into 微信公众号 / WeChat Official Account editor. Walks through 5 steps with one user confirmation midway. Do NOT use this skill for short summaries (<1000 字), single-piece news commentary, or already-defined report templates.

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。

Nature-style hydrology manuscript reviewer skill for testing streamflow, runoff, groundwater, water storage, drought, flood, hydroclimate and water-resource claims against water-balance closure, hydrologic signatures, model structure and observational constraints.

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。

Cross-disciplinary polar Earth-system manuscript review orchestrator for Arctic, Antarctic and Southern Ocean research. Routes claims to existing domain reviewer skills and applies polar-specific evidence gates for sparse observations, cryosphere variables, product dependence, conservation budgets, coupled attribution, paleoclimate archives and regional research responsibility.

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。

Nature-style remote-sensing manuscript reviewer skill for evaluating Earth-observation products, retrievals, machine-learning maps, validation design, uncertainty propagation, spatial transferability and claim-scope calibration.

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。

Nature-style atmospheric science manuscript reviewer skill for testing observation, reanalysis, model, aerosol-cloud-radiation, extreme-event, circulation-mechanism and attribution claims against their physical evidence chain.

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。

Generate structured handoff materials for offline human tasks (interviews, observations, outreach). Creates actionable briefs with phone-friendly capture templates.

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。