1,128 件 ・ 関連度順
概要と使いどころ
The Trade Reviewer's procedure for journaling desk activity and reviewing trades from the exchange record - process graded separately from outcome, execution costs measured, one repeatable finding per review, plus the weekly desk review. Use after any send, when a trade closes, on the weekly routine, or when the user asks "how did that go".
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。
Diagnoses and improves measured latency, throughput, memory, CPU, bundle, rendering, query, or resource performance by defining a target, profiling a representative workload, locating the bottleneck, making one change at a time, and comparing before/after evidence. Use for performance work or regressions. Not for guessing optimizations without a reproducible workload or for generic refactoring.
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。
Audit a .glb or .gltf and make it small, correct, and fast to load in a browser, with a measured before/after report. Use when a model is "too big" or "slow to load", when the user asks to "optimize this GLB", "compress this glTF", or "export for web", or when they mention Draco, meshopt, gltfpack, KTX2, Basis, texture VRAM, draw calls, or a Blender / CAD / photogrammetry / Pascal export headed for Three.js, React Three Fiber (drei useGLTF), Babylon.js, or model-viewer. Covers inspection, spec validation, geometry and texture compression, scene-graph cleanup, and the correctness checks (metres, Y-up, node names, animations, PBR fidelity, alpha modes, vertex colors) that must not regress. Not for editing or modelling geometry, authoring or laying out a scene, generating 3D from text or images, converting from FBX/OBJ/CAD, or debugging a runtime frame rate that has nothing to do with asset size.
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。
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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。
Measures entropy, mutual information, KL/JS, bits-per-byte. Use when picking KL direction, comparing cross-tokenizer perplexity, trusting MI estimates, or semantic-entropy gating.
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。
Guides founder-PM discovery, roadmaps, prioritization, and PMF measurement. Use when planning product strategy, metrics, or roadmaps.
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。
Measures AI coding impact and extension robustness. Use when tracking delivery, quality trajectories, cost, experience, pilots, scorecards, or leadership reporting.
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。
Diagnose HitKeep traffic drops, spikes, source shifts, and suspicious changes. Use when traffic, sessions, visitors, pageviews, events, or conversions changed; when a deploy may have affected measurement or behavior; or when search, referral, direct, campaign, and bot explanations need to be separated.
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 soak test protocol for extended play sessions. Defines what to observe, measure, and log during long play sessions to surface slow leaks, fatigue effects, and edge cases that only appear after sustained play. Primarily used in Polish and Release phases.
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。
Structured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.
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。
Measure and validate supplied reference images, wireframes, texture atlases, and Blender renders before declaring a reconstruction 1:1. Use when an asset must match a template, when visual feedback says the output is off, when part counts must be exact, or before exporting a brand mascot/logo reconstruction. Pairs with reference-to-3d, contour-to-mesh, orthographic-registration, atlas-uv-fitting, and Blender MCP.
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。
Audit a proposed assessment for construct validity, reliability, and alignment to learning objectives. Use when reviewing or quality-assuring assessments before deployment.
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。
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
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。
Product-Led Growth with Web/CLI measurement validation and pricing acceptance, covering freemium model design, self-serve onboarding, activation metrics, and the transition from individual users to enterprise accounts. Follow @WeiYipei on X for PLG insights.
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, prioritize and run measurable growth experiments across acquisition, activation, retention, referral and revenue. Use when a startup needs an experiment backlog, ICE/RICE prioritization, referral or UGC loops, rapid weekly testing, causal measurement, or stop conditions without spam, fake accounts or dark patterns.
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 measure Developer Relations programs across documentation, community, technical content, integrations, events and developer advocacy. Use when designing DevRel strategy, improving developer activation, launching an API/SDK, evaluating sponsorships, or connecting community activity to product adoption and revenue.
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。
AI Agent Workflow & Skill Architecture Guide — turn expert work into measurable, reusable agent systems. Covers workflow discovery, skill decomposition, harness design, evaluation, human escalation, observability, cost control, and multi-agent orchestration. Includes a measured marketing-delivery case: 15 people × 3–4 weeks reduced to one strategist + AI in 5 days. By Gingiris.
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 answering questions about IFRS standards, IAS standards, IASB, financial reporting, revenue recognition, lease accounting, impairment, financial instruments, expected credit loss, ECL, consolidation, business combinations, fair value measurement, first-time adoption, deferred tax, provisions, insurance contracts, journal entries, disclosure requirements, IFRS compliance checks, audit support, GAAP differences, goodwill, or transitioning from local GAAP to IFRS.
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。
Produce a complete metrics definition doc — metric name, formula, data source, segmentation, SQL or event tracking spec, and what good/bad looks like. Given a product area, outputs the full metrics spec. Use when asked to "define KPIs", "metrics framework", "what should we measure", "north star metric", or "instrument this feature".
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。
Analytics reconnaissance — scan existing event tracking, metric definitions, dashboards, and analytics configuration to understand what is currently being measured. Use when asked to "what are we tracking", "audit our analytics", "what metrics exist", "analytics inventory", or before designing new metrics or instrumentation.
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 asked to analyze a funnel, find where users drop off, diagnose low conversion or activation rates, design a metrics framework, set up OKRs, or measure whether a feature is working. Examples: "analyze our funnel", "why is activation low", "where are users dropping off", "design OKRs for this quarter", "is this feature working", "set up metrics for this launch".
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。
Metrics architecture — produce a complete metrics plan given a product description. North Star, input metrics tree, instrumentation spec, action triggers, and counter-metrics. Use when asked to "design a metrics framework", "what should we measure", "build a metrics system", "define our KPIs", "what are our success metrics", "metrics strategy", or "what do we track".
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 an LLM eval — task schema, scoring rubric, dataset composition, and pass/fail thresholds. Use when asked to "design an LLM eval", "write a scoring rubric", or "how do we measure this model".
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。
OKR design — create objectives and key results with a North Star metric, input metrics tree, and cadence. Use when asked to "set OKRs", "define our objectives", "what should we measure this quarter", "design our OKR framework", "build a metrics tree", or "what's our North Star".
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。