「improve」の検索結果

End-to-end SEO operations for B2B SaaS organic visibility. Use this skill when you need keyword research, technical SEO audits, content optimization, link building strategy, international expansion, AI/AEO optimization, schema markup, Core Web Vitals improvements, and organic traffic growth planning. Also triggers on: SEO, keyword research, can we rank for this keyword, which keywords are actually worth it, qualify these keywords, check the SERPs, keyword shortlist, too many keywords, technical SEO, link building, content optimization, international SEO, hreflang, x-default, multilingual site, translated pages not ranking, our German site gets no traffic, language switcher, auto-redirect by country, machine translation SEO, content parity, ccTLD vs subfolder, AI search, AEO, GEO, Core Web Vitals, schema markup, organic traffic, G2 listing, Capterra, TrustRadius, review-platform category page, best software roundup, listicle placement, directory submissions, do we need a Google Business Profile in [country], local SEO with no office, Google Business Profile eligibility, virtual office address SEO, coworking address business profile, business profile suspended, NAP consistency, local citations for SaaS, LocalBusiness schema, server log analysis, log file analysis, what is Googlebot crawling, is Googlebot hitting errors, verify Googlebot, fake Googlebot, crawl budget, do we have a crawl budget problem, Crawl Stats report, new pages not getting crawled.

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。

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.

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。

Provide constructive, evidence-based research mentoring feedback on student manuscripts, proposals, analyses, code, figures, and presentations. Use for advisor-style review that teaches the next improvement without impersonating an advisor or making unsupported scientific judgments.

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 Emergency Management Director — activating and staffing an EOC for a developing hazard, sequencing a disaster declaration and FEMA Public Assistance/Individual Assistance request, sizing shelter and resource needs against real population and capacity numbers, designing or critiquing a tabletop exercise, or writing an after-action report and improvement plan.

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Use when a task needs the judgment of a Cutting/Slicing Machine Operator — calculating a portion weight overweight margin from measured weight variability to minimize giveaway while staying compliant, verifying a thickness setting against a new product lot's actual density rather than reusing a prior lot's setting, diagnosing declining cut quality as a blade-condition issue rather than compensating with thickness/speed, or adapting cut pattern to irregular input to improve yield.

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 a Biofuels/Biodiesel Technology and Product Development Manager — deciding whether a lab/bench-scale process is ready to scale up, evaluating feedstock flexibility tradeoffs in process design, pricing a technology's value against regulatory credit pathways (RFS RIN, LCFS CI score), or managing IP strategy for a novel conversion process. Distinct from biofuels-production-manager, which runs an existing operating facility — this role owns R&D and commercialization of new or improved process technology.

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。

Transform vague inputs into precision-optimized AI prompts for Claude, ChatGPT, Gemini, or other LLMs. Use when user mentions "optimize prompt", "improve prompt", "lyra", "prompt engineering", or needs help crafting effective AI prompts.

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 high-quality, secure, and performance-optimized Containerfiles (Dockerfiles) following best practices for multi-architecture builds, OpenShift/Kubernetes compatibility, and BuildKit cache optimization. Use when the user wants to: (1) create a new Containerfile or Dockerfile for any project (Python, Rust, Go, Node.js, .NET, or any language), (2) containerize an application with multi-stage builds, (3) optimize an existing Containerfile for security, performance, or image size, (4) review or improve container image build practices, (5) set up BuildKit cache mounts for package managers, (6) create OpenShift-compatible container images with non-root users and arbitrary UID support, (7) write a .dockerignore file, or (8) apply OCI LABEL standards.

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。

Craft high-quality natural-language image prompts for any modern text-to-image or image-edit model that accepts flowing English. Trigger when the user wants help writing, rewriting, improving, or translating an English natural-language image prompt — including "write me an image prompt", "improve this image prompt", "describe this scene for an image model", or "convert these tags into a natural language prompt". Do NOT trigger for requests that are purely about dispatching to an image API, choosing samplers/schedulers, picking LoRAs, or setting up ControlNet — those belong to a runtime skill.

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。

Write professional press releases in Traditional Chinese (zh-TW) following inverted pyramid structure, 5W1H framework, and Taiwan media conventions. Use when the user wants to: (1) write a press release or news announcement, (2) draft corporate communications for media dissemination, (3) craft a lede or headline for a news release, (4) review or improve press release structure and newsworthiness, (5) write any 新聞稿 or 媒體通知, or (6) format announcements following journalistic standards.

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。

Scout Rule — identify the top 3 highest-impact improvement opportunities in files you're already touching. Analyzes entire file content, not just changed lines. Focuses on pre-existing code quality, not PR bugs. Use when preparing a PR, during code review, or after completing a 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。

Prepares Go applications to work effectively with AI coding agents. Use when setting up a new Go project or retrofitting an existing one to ensure: structured JSON logging (slog/Zap/ZeroLog/Logrus), machine-readable command output, thorough golangci-lint configuration, non-interactive CLI design with --yes flags, structured error handling with meaningful exit codes, proper context.Context propagation, graceful shutdown, health check endpoints, and a standardized Makefile. Triggers when a user asks to make their Go app "agent-ready," "AI-friendly," wants to improve agent tooling/observability in a Go project, or needs to audit an existing Go project against agent-readiness best 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。

Audit organizational communications — channel effectiveness, message clarity, overload, and actionable improvement plans.

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。

Strengthen defensive security operations — detection engineering, hardening, monitoring, and continuous defense improvement.

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。

Computes development quality and velocity metrics from git history and tracks improvement over time. Measures fix ratios, rework hotspots, test coverage, DORA metrics, and code churn.

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 MRO parts inventory systems for field service optimization -- truck stock composition, first-time fix rate improvement, reorder point calculation, safety stock sizing, obsolescence detection, and demand forecasting..

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 retention and engagement features using the Hook Model. Use when the user says "improve retention", "reduce churn", "make it habit-forming", "engagement design", "hook model", "trigger action reward", "users aren't coming back", "increase DAU/MAU", or wants to design features that bring users back repeatedly - even if they don't explicitly say "Hook 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。

Rate any product, feature, or experience on the 11-star scale (Brian Chesky's Airbnb thought experiment). Use when user says "rate this experience", "11-star", "star rating", "experience audit", "how good is this", "experience rating", "product audit", "quality assessment", or wants to evaluate product quality and identify improvement paths. Also trigger when user wants to benchmark a feature, assess where a product stands, or map out what "great" looks like - even if they don't explicitly say "11-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。

Analyze flow metrics to identify delivery bottlenecks and improve throughput. Use when the user says "flow metrics", "why is delivery slow", "cycle time analysis", "WIP analysis", "throughput review", "what's our lead time", "DORA metrics", "where is work getting stuck", "Kanban metrics", or wants to diagnose delivery performance using data - even if they don't explicitly say "flow metrics review". Based on "Making Work Visible" by Dominica DeGrandis (WIP limits, flow blockers, time thieves) and "Accelerate" by Forsgren, Humble & Kim (DORA metrics, delivery performance indicators).

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 and improve product-market fit. Use when the user says "product-market fit", "PMF", "do we have PMF", "why aren't users retaining", "we're not growing", "users aren't sticking", "PMF pyramid", "find our target customer", "are we building for the right person", or wants to diagnose why a product isn't growing or retaining - even if they don't explicitly say "product-market fit".

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。

Critique a design for usability, hierarchy, and accessibility. Use when the user says "critique this design", "review this screen", "what's wrong with this UI", "design feedback", "UX review", "how can I improve this", "review my mockup", "is this good UX", or shares a design and wants structured feedback - even if they don't explicitly say "design critique".

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 why a landing page, homepage, pricing page, or signup form is not converting, and produce a ranked fix list with a test plan. Runs a structured audit across motivation, value clarity, friction, anxiety, and CTA hierarchy rather than giving generic design opinions. Use when the user says "why isn't this page converting", "improve our conversion rate", "CRO", "conversion audit", "this page isn't working", "high traffic low signups", "people bounce", "review our landing page", or shares a URL and asks what is wrong with it. For rewriting the words only, see copy-review. For writing a new page from scratch, see landing-page-writer. For the statistics of running the test afterwards, see growth-experiment.

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 clean and consistent APIs. Use when the user says "design an API", "API design review", "design these endpoints", "REST API for X", "GraphQL schema", "API contract", "how should this endpoint work", "review my API design", or wants to create or improve an API interface - even if they don't explicitly say "API design".

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 implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

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。