「perception」の検索結果

Applies Gestalt psychology principles to web layout, spacing, grouping, and visual hierarchy. Proximity, similarity, closure, continuity, figure-ground, common fate, Pragnanz — translated into CSS, component structure, and information architecture. Activate on 'gestalt', 'visual grouping', 'proximity spacing', 'visual hierarchy', 'perceptual organization', 'layout psychology', 'whitespace strategy'. NOT for color theory (use color-theory-palette-harmony-expert), not for accessibility auditing (use design-accessibility-auditor).

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Model organizations as BDI systems with filtered perception, discourse-shaped desires, accommodations, and abstraction-level hierarchy. Use for socio-technical coordination and organizational modeling. NOT for mechanistic workflows, individual psychology, or non-social domains.

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。

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

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。

Expert-level computer vision for robotics covering camera models, feature detection, object detection, pose estimation, visual SLAM, and deep learning for perception.

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。

Plan browser, app, screenshot, or UI checks before visual claims. Use when a task includes app shots, visual QA, generated assets, or runtime UI proof.

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 the user asks to "measure how our narrative is landing", "track echo rate against our canon lexicon", or "check how AI answer engines describe our brand"; produces a resonance report — echo rate (overlap of market language with the narrative-registry canon lexicon, method declared), AI-answer perception via tavily.py --answer (proxy-labeled), share-of-voice on a locked competitor panel (reusing share-of-voice-tracker), and resonance signals from bluesky.py / gdelt.py / pageviews.py — every number labeled Measured / proxy / User-provided, feeding the TALE E dimension and the upstream of the E1 evidence-integrity veto. Not for rebuilding share-of-voice machinery — use share-of-voice-tracker; not for own-site GA4/GSC analytics — use performance-monitor; not for scoring TALE profile result — use narrative-quality-auditor; not for adjudicating claims — use offer-claims-registry. 回声率/AI回答感知/份额之声/共鸣信号

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。

Micro-decision analysis. Catalog the border radii, spacing scales, type sizes, and state coverage that most people miss. Use when auditing design systems, evaluating craft quality, or training granular perception.

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The 10-second design audit. Look at any design and name what's working and what's not within seconds. Trains rapid pattern recognition for hierarchy, spacing, type, and color. Use when evaluating designs quickly, giving first-impression feedback, or building perception speed.

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。

Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.

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。

Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.

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。

One sentence - what this skill does and when to invoke it

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。

Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.

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 this skill when a driver has vision in only one eye or significantly reduced vision in one eye. Covers § 391.41 vision standards, monocular driver variance, and depth-perception assessment.

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 this skill when the user asks how to manage a trucking carrier's public reputation — handling a major accident in the news, social media + Glassdoor + Indeed reviews, customer disputes that escalate, employee disputes, regulator perception, and how to communicate during a crisis. Reference industry PR + crisis communication practices.

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。

One sentence - what this skill does and when to invoke it

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。

Maxim action choreography video — engineered for emotional arousal and sales lift through Arousal-Based Persuasion. Uses Mirror Neurons + Arousal-Misattribution + Kinetic Energy Perception to drive emotional peak that transfers to brand preference and purchase intent.

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。

Epistemic logic for multi-agent perceptual systems — Kripke models, vision sets, public announcement as coordination primitive, and satisfiability-as-reconfiguration applied to agents with limited observability. From Charrier, Ouchet, Schwarzentruber's "Big Brother Logic" demonstration paper (ENS Rennes / IRISA).

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Model an organization with BDI-inspired perceptions, expressed goals, accommodations, and levels of agency. Use for socio-technical diagnosis where actors interpret the same situation differently and agreement is partial. NOT for individual AgentSpeak execution, normative conflict algorithms, or ordinary workflow diagrams.

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。

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

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。