「cause」の検索結果

Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, upload SQLs to LinkedIn, LinkedIn Conversions API, LinkedIn CAPI, Meta conversion leads, CRM to ad platform, gclid, li_fat_id, msclkid, value-based bidding, optimize for pipeline not form fills, low match rate, conversion lag.

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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

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Analyze a set of related tickets to identify the underlying root cause.

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Abductive reasoning about causes. Takes a phenomenon, separates observations from interpretations, then spawns diagnosticians applying different reasoning lenses (technical, human-factors, process, incentive-structure, environmental, temporal, measurement-artifact, statistical) in isolation. Orchestrator evaluates candidate causes for fit-against-evidence, calibrates confidence honestly, and reports leading candidate(s) with distinguishing evidence needed. Produces feedback only — no code, no tickets, no artifacts.

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Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.

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Mandatory 3-phase explore-reason-act workflow before code modification. Enforces read-before-write, root cause hypothesis formulation, minimal change application. Triggered by "explore" ("탐색"), "reason" ("추론"), "read before write", "root cause" ("근본 원인") keywords.

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Systematic root-cause debugging that produces a diagnostic report (mk:fix applies the fix; mk:build-fix owns compile failures). Use for 'debug this', 'why is this broken', 'root cause'.

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Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

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Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.

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 systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.

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Investigate and fix failing CI. Takes a PR reference, one or more CI run/job URLs, or freeform text describing where the failures are, then enumerates failing workflows/jobs/steps, determines the root cause (often one shared cause behind several red checks), proposes a fix plan, applies code changes, commits, pushes, watches the new runs, and repeats until checks pass. Use when the user says "fix CI", "CI is red", "checks are failing", "the build is broken on my PR", "fix the failing jobs", or pastes a failing CI run URL. Do NOT use for authoring new workflows, or for read-only diagnosis with no fix intent — use gha-log-reader for that.

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Use when tracing the root cause of a resolved incident or recurring bug — 5-whys chain, fishbone categorisation, contributing-factors split — even if the user says 'why does this keep breaking?'.

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Symptom → root-cause lookup table for nuxt-crouton's recurring failure modes — exact error strings mapped to their known cause, fix, and evidence issue. Use when you hit an error you don't recognize in this repo, before debugging from first principles. Trigger phrases and symptoms: "Cannot resolve entry module .nuxt/hub/db/schema.entry.ts", "Could not load '@fyit/crouton'", "No migrations present", "No schema files found", "$setup.t is not a function", "does not provide an export named 'Map'", "no such table: translations_ui", "collection pages 500 in production", "the build was green but the app is broken", "is this a known issue?".

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Disciplined diagnosis loop for hard bugs, regressions, flaky failures, and performance problems with unclear cause. Use for root-cause debugging after a concrete symptom exists; do not use for routine implementation or speculative cleanup.

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インシデント発生後の振り返りと根本原因分析を体系的に実施するスキル。 ログファイル解析や技術デバッグが必要な場合は log-debugger を使用すること。 本スキルはインシデント管理プロセス（タイムライン構築、影響評価、 是正措置計画、再発防止策）に特化する。ログデータの分析ではなく、 組織的な振り返りプロセスと是正措置の策定が主目的である。 Use when conducting post-incident reviews without log analysis, creating corrective action plans, or performing organizational root cause analysis focused on process improvement. NOT for log file debugging. Triggers: "post-incident review", "corrective action plan", "incident report作成", "再発防止策", "RCAレポート", "是正措置", "インシデント振り返り"

Internal audit support skill aligned with IIA (Institute of Internal Auditors) International Standards. Provides risk-based audit planning, audit program development, workpaper documentation, finding development (Condition/Criteria/Cause/Effect), and Corrective Action Request (CAR) tracking. Use when: planning annual/quarterly audits, creating risk assessment matrices, developing audit programs and test procedures, documenting audit workpapers, writing audit findings and reports, tracking corrective actions and follow-ups, preparing for external audits (SOX, ISO). Triggers: "internal audit", "audit plan", "audit program", "audit workpaper", "audit finding", "risk assessment", "CAR tracking", "corrective action", "IIA Standards", "COSO framework", "監査計画", "監査プログラム", "監査調書", "是正措置", "リスク評価".

Problem solving and analysis: Structured thinking processes for complex problems. Divide & Conquer, Root Cause Analysis, SWOT, Pareto, and decision heuristics.

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Drafts one knowledge article or known-error article from a resolved incident or ticket thread: symptoms as reported, environment, cause exactly as the resolver stated it, resolution steps as performed, workaround with its limits, verification, things tried without effect, related records, keywords and proposed metadata, every statement traced to a ticket note or marked UNKNOWN, with a redaction log and a question list for the knowledge owner. Never decides a root cause, adds a step or publishes. Use when the user asks to "write a KB article from this ticket", "turn this resolved incident into a knowledge article", "draft a known error record for this", "document the fix we just did" or "make this thread into a how-to for the service desk". Do not use for a procedure the team will execute (runbook, operating procedure, playbook), use runbook-drafter instead; for a post-incident review, use incident-postmortem-drafter. Drafts for human review; never approves, authorises or signs off.

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Turns reported data problems (messages, tickets, meeting notes, complaints) into one DRAFT issue log: one row per issue with the symptom as reported, dataset and fields named, reproduction steps, affected reports, first seen date, reporters, a suggested owner, and blank severity, root cause and status cells for the owner. Never asserts a root cause, severity or fix. Use when the user asks to "log these data issues", "turn these complaints about the numbers into an issue list", "build the data quality backlog", "which reports are hit by this data problem" or "write up the reproduction steps for this data bug". Do not use for exploring or profiling a dataset, use dataset-insight-pack instead; for an IT incident write-up, use incident-postmortem-drafter; for tracking fix actions once assigned, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.

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Generate Zod v4 TypeScript schemas from OpenAPI (openapi.yaml / openapi.json) definitions. This skill exists because npm packages like openapi-zod-client do not support Zod v4 — Claude reads the YAML/JSON directly and writes the conversion. Use this skill whenever the user wants to convert OpenAPI/Swagger definitions to Zod schemas, generate TypeScript types from API specs, or create validation schemas from openapi.yaml. Trigger on keywords: 'openapi', 'swagger', 'zod', 'API schema', 'yaml to types', 'schema generation', 'validation generation'. OpenAPI定義からZodスキーマを生成するスキル。openapi.yamlから型を作りたい、APIスキーマをTypeScriptの型にしたい、Zodバリデーションを作りたい、といったリクエストで使う。「openapi」「swagger」「zod」「APIスキーマ」「yamlから型」「スキーマ生成」「バリデーション生成」などのキーワードが出たら積極的にこのスキルを使うこと。

Resolve complex or high-impact problems when the cause, solution path, requirements, or proof boundary is genuinely uncertain. Use when work needs evidence-led diagnosis, consequential design or implementation, or a completion audit; do not trigger merely because work has multiple steps, or for simple facts, fully specified routine changes, or pure creative generation.

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Investigate the root cause of a recurring or systemic defect across a series of incidents, not a single one. Use on the third recurrence, or when someone asks 'why again?', 'five whys', 'fix the root'. Skip for one or two ordinary cases, an already proven simple cause, or a one-off external outage. Triggers: /five-whys, five whys, fix the root.

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Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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Apply when debugging. Trace each symptom to its root cause and fix it there; reproduce first, ask why until you reach it, resist nil-check guards that silence crashes.

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。