1,182 件 ・ 関連度順
概要と使いどころ
Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.
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。
Compare two sessions side-by-side using Agent Monitor data — per-model token usage (input/output/cache_read/cache_write + compaction baselines), pricing engine cost breakdowns, workflow intelligence (complexity scores, tool flow transitions, subagent effectiveness), session metadata (thinking_blocks, turn_count, turn_duration_ms, usage_extras), and full event timelines with all 10+ event 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。
Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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。
React 19 performance patterns and composition architecture for Vite + Cloudflare projects. 50+ rules ranked by impact — eliminating waterfalls, bundle optimisation, re-render prevention, composition over boolean props, server/client boundaries, and React 19 APIs. Use when writing, reviewing, or refactoring React components. Triggers: 'react patterns', 'react review', 'react performance', 'optimise components', 'react best practices', 'composition patterns', 'why is it slow', 'reduce re-renders', 'fix waterfall'.
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。
Build and risk-screen a compact role-based method shortlist for a mathematical-modeling subquestion. Use after problem framing and data profiling, before model code generation, to propose a main candidate, a usable baseline, and at most one conditional fallback without padding the pool.
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 and run minimal reproducible MATLAB or Beita Tianyuan compatible code for the human-approved main method and usable baseline, with compact experiment artifacts and a canonical run summary.
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 and run minimal reproducible Python modeling code for the human-approved main method and usable baseline, saving compact experiment artifacts and a canonical run summary.
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 run risk-targeted robustness, sensitivity, error, and baseline checks for an approved mathematical model, emitting compact machine evidence in lean mode and a final report in submission mode.
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。
Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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。
Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
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。
Extract and maintain global and method-specific mathematical-model assumptions from the problem frame, active method cards, data profile, and risk probes, while leaving necessity and impact judgments to the human modeler.
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。
Review, run, debug, and verify approved Python modeling code against its code plan, data contract, method decision, risk conditions, and experiment outputs, saving one compact JSON review.
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。
Review, run, debug, and verify approved MATLAB or Beita Tianyuan modeling code against its plan, data contract, method decision, compatibility constraints, and experiment outputs, saving one compact JSON review.
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。
Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream work.
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 guidance for the Fabric CLI (`fab`) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and admin. Also estimates the capacity units (CU) an operation will consume and its impact on the capacity before running it. Automatically invoke whenever the user mentions Fabric, Power BI Service, a Fabric or Power BI workspace, a capacity or F SKU, OneLake, `fab`, or asks to create, run, refresh, schedule, deploy or delete anything in Fabric, including preview items such as Plan, Ontology, Graph or Copilot.
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。
Trace relationships between semantic models and downstream reports across Fabric workspaces. Automatically invoke when the user asks to "find downstream reports", "show report lineage", "impact analysis", "what depends on this dataset", "cross-workspace lineage", "which reports are connected", "get model dependencies", or mentions model-to-report dependency tracing.
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 making or fixing real-time VFX in Unity 6.3: explosions, fireballs, projectiles with muzzle, trail and impact, spells, slashes, shockwaves, smoke, sparks, Particle System (Shuriken) or VFX Graph ('VFX Graph explosion', 'particles look bad', 'effect kills fps on mobile', 'too much overdraw'), mesh effects with scrolling textures, an uber VFX shader with LUT ramps, Custom Data and custom vertex streams, flipbooks and VFX textures, pink particles after URP upgrade, VFX budgets and readability.
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 a UE5 task involves Niagara or Chaos destruction: a Niagara fireball, projectile trail, impact explosion, muzzle flash, spell, smoke, sparks, debris, Niagara fluids or flipbooks; "Niagara is too expensive", stat NiagaraOverview, Effect Types, pooling, Data Channels, lightweight emitters, VFX scalability; judging an effect's readability, value, color, timing, white blowout or strobing; or a wall that shatters, geometry collection fracture, Dataflow, damage thresholds, floating chunks, debris cost, Chaos Visual Debugger.
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 making or fixing particle effects and game feel in Godot 4.7: GPUParticles3D or GPUParticles2D explosion, fireball projectile with trail, impact, sparks, smoke, shockwave, ribbon trails, sub-emitters, particle collision, color curve does nothing, particles pop out or vanish, effect too heavy on mobile, CPUParticles fallback for Compatibility or web, overdraw, hit-stop, freeze frame, screen shake, juice.
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 one interior photo, uploaded or generated on Scenario, should become a room a visitor walks into in a web browser and plays with: props lifted out of the photo and rebuilt as 3D objects to grab, throw, and hear land, inside a Gaussian splat world, or restyled variants of one room. Keywords: image-blaster, walkable room, photo to 3D room, clean plate, object cut-out, splat world, physics props, grab and throw, impact sounds, heightfield collision.
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。
Query journal indexing/inclusion status on CNKI - check which databases include a journal (北大核心, CSSCI, CSCD, SCI, EI, etc.), get impact factors and evaluation data. Use when user asks about a journal's level, indexing, or ranking.
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。
Assess the impact of a proposed change on dependencies and shared infrastructure. Use before touching unfamiliar or consequential systems.
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。
Triage technical SEO findings from Ahrefs Site Audit (and similar crawlers) by SEO impact, not just severity. Use this skill when reviewing crawl results, prioritizing technical fixes, scoping a technical SEO sprint, or after running any site-wide crawl. Triggers on site audit results, technical fix list, crawl errors, technical SEO triage, prioritize technical issues, what should we fix first, broken links, redirect chains. Also triggers when a long list of crawler issues is creating decision paralysis.
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。
Systematic content refresh discipline. Quarterly audits, refresh prioritization (which pieces, when, how deep), refresh-vs-merge-vs-delete decisions, the lifecycle that distinguishes intentional refresh from set-and-forget decay. Builds on the refresh sections of pillar-content-architecture and editorial-qa with a program-level discipline. Triggers on content refresh, content decay, content audit, refresh prioritization, content lifecycle, refresh strategy, traffic decay, ranking drop, content freshness, evergreen content, content maintenance. Also triggers when traffic is eroding silently across an aging content library, when teams cannot decide which pieces to refresh, or when refresh work is happening but the impact is unclear.
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。