「diagnose」の検索結果

"Analyze e-commerce performance using GA4 metrics, conversion funnel analysis, and key e-commerce KPIs. Use this skill when the user needs to evaluate online store performance, diagnose conversion drop-offs, set up e-commerce tracking, or create performance dashboards — even if they say 'why are sales down', 'optimize our online store', 'set up GA4 for e-commerce', or 'what metrics should 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。

"Apply Cognitive Load Theory to optimize instructional design by managing intrinsic, extraneous, and germane load within working memory limits. Use this skill when the user needs to diagnose why learners are overwhelmed, redesign training or documentation for better comprehension, evaluate UI/UX information architecture for cognitive burden, or when they ask 'why is this tutorial confusing', 'how to simplify complex instructions', or 'what causes information overload'.".

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 debugging session — reproduce, isolate, diagnose, and fix. Trigger with an error message or stack trace, "this works in staging but not prod", "something broke after the deploy", or when behavior diverges from expected and the cause isn't obvious.

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。

"Optimize Core Web Vitals (LCP, INP, CLS) for better search rankings and user experience. Use this skill when the user needs to diagnose page speed issues, improve Largest Contentful Paint, reduce layout shift, or pass Google's page experience signals — even if they say 'my site is slow', 'Core Web Vitals failing', or 'page speed optimization'.".

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 DuPont Analysis to decompose Return on Equity (ROE) into profitability, efficiency, and leverage components. Use this skill when the user needs to diagnose why ROE is high or low, compare financial performance drivers across companies, or identify which operational lever to pull — even if they say 'why is our ROE declining' or 'how do we improve returns'.".

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。

"Analyze and mitigate the bullwhip effect where demand variability amplifies upstream in supply chains. Use this skill when the user needs to diagnose order variability amplification, quantify the bullwhip ratio, or implement dampening strategies — even if they say 'why are our orders so volatile', 'supply chain variability', or 'demand amplification problem'.".

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。

"Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance optimization'.".

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 bugs with unknown root cause. Spawns fresh-context investigation subagent + parallel /review solve companion, persists hypotheses across iterations, hands off to /fix when root cause found.

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 Codex Desktop sidebar sessions disappear, fail with systemError, or cannot continue after a model_provider switch. Diagnoses and repairs active, non-archived sessions without changing authentication or provider configuration.

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 fixes issues in Atlassian Forge apps. Use this skill whenever a Forge app has errors, crashes, shows blank UI, fails to deploy, doesn't appear after installation, has permission issues, or produces unexpected output. Trigger on any mention of forge logs, forge deploy errors, resolver errors, blank panels, missing scopes, Custom UI not rendering, production vs dev discrepancies, or any Jira/Confluence app that "stopped working". Also trigger when the user asks to debug, troubleshoot, investigate, or fix a Forge app issue — even if they haven't used the word "Forge" but describe a Jira panel or Confluence macro acting up.

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。

Configure, diagnose, or optimize the persistent OpenClaw fleet, native CLI routing, skill loading, run evidence, and context recovery. Use for orchestration operations; product implementation uses its own relevant skills.

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 and fix Flutter layout, overflow, unbounded constraint, scroll, and responsive rendering issues in this repository while preserving the template's existing feature, theme, localization, and shared widget patterns. Use when UI overflows, text clips, widgets disappear, layouts fail on mobile/tablet/desktop, or a feature needs responsive behavior.

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 and fix failed Pharos GitHub Actions, adaptive PR checks, Cloudflare deploys, Pages releases, or scheduled automation. Use when a gate/run failed or the user asks to retrigger and iterate until clear.

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。

Phase 4 of the feature-pipeline suite. Use this after /fp-review has posted a sandbox review comment — it reads the outstanding checkbox items, applies fixes, then watches CI until green (or exits cleanly so you can retry). Trigger phrases: "address review items", "fix review feedback", "resolve review checklist", "watch CI", "poll CI", "check if CI is green", "iterate on PR", "fp-iterate", "Phase 4", "after review comment", "fix the review issues and watch CI". Always bounded — max 10 poll rounds, never loops forever. Exits with a clear status: green (hands off to /fp-drift), still-pending (re-invoke), or failed (diagnose before touching anything).

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。

Implement, migrate, review, and diagnose iOS Liquid Glass interfaces in SwiftUI and UIKit. Use for system chrome, custom glass effects, transitions, accessibility, and version-specific issues in native iOS apps.

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。

Reviews Web Audio API JavaScript/TypeScript code for correctness, thread safety, and deprecated patterns. Use when the user asks to review AudioWorklet code, check an AudioWorkletProcessor, audit Web Audio graph construction, or diagnose crackling/dropouts in a web audio app. Trigger on phrases like "review my Web Audio code", "check my AudioWorklet", "is my AudioWorkletProcessor safe", or "why does my web audio crackle".

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 and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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 whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).

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 6-phase debugging. Build feedback loop first, reproduce deterministically, hypothesize with ranked falsifiable theories, instrument one variable at a time, fix with regression test, cleanup. Use when a bug exists, tests fail unexpectedly, or behavior is wrong and cause is unknown.

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 task needs the judgment of an Automotive Engineer — computing weight transfer and tire friction-circle limits for a braking-while-cornering maneuver to set brake-force distribution, sizing a powertrain and gear ratio against a 0-60 mph acceleration target while checking traction versus power limits, sizing a crumple zone's crush distance against FMVSS 208 occupant-deceleration limits, or checking a powertrain-mount or driveline natural frequency against engine firing-order excitation for NVH isolation. Distinct from an automotive-service-technician (diagnoses and repairs a vehicle already designed) — this role owns the system-level design decisions (structure, powertrain sizing, suspension geometry, restraint/crash structure, NVH) that produce the vehicle the technician later services.

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 native Linux packages (.deb / .rpm / .apk / .pkg.tar.zst) from a PKGBUILD or yap.json project using the yap-mcp Model Context Protocol server. Use this skill whenever the user asks to "build a package", "make a deb/rpm", "cross-compile for arm64/aarch64", "sign a release", generate SBOMs, install a built artifact, or diagnose why a yap build failed. The yap MCP server exposes async builds with buildID polling, validation, dependency-graph inspection, and artifact discovery.

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。

Emergency bug fix pipeline — diagnose, fix, test, commit, push, and PR in 2 iterations max. Speed over perfection.

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。

Scan all repos for failing or stuck GitHub Actions workflows, diagnose issues, and fix them. Use when: 'fix ci', 'ci is broken', 'github actions failing', 'unblock ci', 'clear ci', 'workflows stuck', 'fix all builds', 'ci health check'.

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。

Emergency fix-and-deploy pipeline — diagnose a bug, apply a minimal fix, verify safety, deploy to production, and confirm the service is healthy.

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。