「cta」の検索結果

Critique a rendered screen's affordances — what looks clickable, state visibility, CTA clarity, and action discoverability. Use when reviewing an existing screen. For sizing and positioning targets in new work, use `fitts-law` (interaction-design).

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Map functional, emotional, and social jobs with outcome expectations. Use when reframing decisions around motivation rather than features. For who the user is, use `user-persona`.

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Write interface copy — microcopy, error messages, empty states, and CTAs. Use when the words are the deliverable. For content structure and ownership, use `content-strategy` (ux-strategy).

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Create a library-grade Vue composable that accepts maybe-reactive inputs (MaybeRef / MaybeRefOrGetter) so callers can pass a plain value, ref, or getter. Normalize inputs with toValue()/toRef() inside reactive effects (watch/watchEffect) to keep behavior predictable and reactive. Use this skill when user asks for creating adaptable or reusable composables.

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Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.

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Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 6-stage recon pipeline (seed → asset expansion → enrichment → exposure analysis → convergence → operator-armed active validation) with connector-resilience and stage-vs-gating discipline, asset-graph discipline, severity rubric, confidence upgrade workflows, time budgeting, identity-fabric mapping, breach×identity correlation with per-person identity dossiers, detectability tagging, detection-aware probing, WAF/CDN bypass, vulnerability prioritization, phishing infrastructure planning, bug bounty submission, and client deliverable templates. Use when planning or executing reconnaissance against authorized targets, mapping an organization's external attack surface, investigating a person/entity, or producing client deliverables.

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Organization-grade identity-fabric mapping: tenant/federation fingerprinting and the pre-auth user-ENUMERATION oracle methodology — enumeration and fingerprint only, never credential submission. Covers domain-to-tenant resolution (Microsoft getuserrealm.srf Managed/Federated namespace check, Entra OIDC metadata tenant-GUID extraction, Autodiscover v2), keyless Microsoft tenant-federation mapping (GetFederationInformation SOAP -> sibling-domain discovery, discover-only ROE, FEDERATED_WITH provenance edge held out of attack-path pivoting), Okta org-slug derivation + OIDC fingerprint + governed custom-domain enumeration, ADFS passive/active fingerprint + version inference, Google Workspace MX-correlated detection, generic OIDC (Auth0/Keycloak/Ping Identity/OneLogin/Duo) discovery, SAML metadata (5 paths), Azure AD Seamless-SSO Negotiate-challenge detection, Microsoft Defender for Identity (MDI) sensor-API presence check, the user-enumeration oracle methodology for Microsoft GetCredentialType (IfExistsResult semantics: exists / doesn't-exist / exists-in-federated-tenant / throttled) and Okta /api/v1/authn (errorCode differential), Medium-detectability discipline with a hard 20-candidate-per-tenant cap and admin/role interest-based ranking, and name x confirmed-email-pattern login-candidate synthesis that FAILS CLOSED with zero output when no org pattern is confirmed. Grounded directly in a production ASM implementation's sso_idp.py, tenant_recon.py, and core/email_patterns.py modules. Deepens — does not duplicate — offensive-osint skill's Identity Fabric endpoint reference with the tenant-federation MAP, the oracle WORKFLOW, and the candidate-SYNTHESIS methodology that reference lacks. Use when fingerprinting an organization's identity provider, mapping its tenant/federation boundary, running an authorized pre-auth user-enumeration pass, or synthesizing login candidates from harvested names to feed that oracle — never for password spray, credential submission, or auth bypass.

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Authentication bypass testing playbook. Use when assessing login flows, password reset logic, account recovery, MFA bypass, token predictability, brute-force resistance, and session boundary flaws.

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Create irresistible offers using the Value Equation, bonus stacking, risk-reversing guarantees, and ethical scarcity. Use when the user mentions "grand slam offer", "make my offer more compelling", "what bonuses should I add", "guarantee strategy", "offer naming", or "people say its too expensive". Also trigger when packaging a product for higher perceived value, justifying premium pricing instead of discounting, designing a money-back guarantee, or structuring tiers to maximize conversions. Covers the MAGIC naming formula and starving-crowd targeting. For product positioning, see obviously-awesome. For outbound sales, see predictable-revenue.

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Build a scalable outbound B2B sales machine with specialized roles (SDR, AE, CSM). Use when the user mentions "outbound sales", "Cold Calling 2.0", "cold email sequences", "sales pipeline", "SDR process", "sales development", "build an outbound sales team", or "fill my pipeline". Also trigger when setting up a B2B SaaS sales team from scratch or building a lead-qualification framework to improve close rates. Covers the three lead types (seeds/nets/spears), role specialization, the referral-email method, ANUM qualification, and pipeline math. For offer design, see hundred-million-offers. For persuasion science, see influence-psychology.

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Deep cultural adaptation of translated blog posts. Run after blog-translate completes. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "lokalisieren", "localiser", "adaptar".

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。

Turn a product into ad angles, hooks, scenes, and CTA.

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Assemble product footage, UGC, and b-roll into a conversion-oriented montage with a hook → pain → demo → proof → CTA rhythm. Use for 带货混剪, 产品混剪, 商品短视频, 种草视频, ecommerce montage, product reel, UGC cutdown, or when the user wants selling footage cut into a short that converts.

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When the user wants to design or optimize the funnel that takes web visitors into installing and onboarding the app — including smart app banners, web-to-app deep links, deferred deep links, web onboarding (Stripe-paid web flow before app install), QR codes, "open in app" CTAs, and the trade-off between paying on web vs in-app. Use when the user mentions "web to app", "smart app banner", "Stripe before app", "web paywall before install", "Branch web SDK", "web funnel for app", "AppsFlyer OneLink web", "Universal Links", "App Links", "QR code to app", "open in app", "deferred deep link from web", or "should I sell on web first then push to app". For pure in-app onboarding, see onboarding-optimization. For deep link infra, see attribution-setup.

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Judges whether material for children under six is developmentally plausible — what is typical at each age, why pre-K is not a thinner kindergarten, and which frameworks apply where Common Core does not. Use this to design or review pre-K material, decide whether a task is reasonable for an age, choose the right framework for a pre-K alignment claim, or push back on material that pushes school-age expectations downward.

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Runs performance systems that change behavior — expectations, feedback, review cycles, calibration, and handling underperformance. Use this to design or fix a review process, run calibration, write or coach on feedback, address sustained underperformance, or work out why reviews consume weeks and change nothing.

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Verified prompt templates and structures for Seedance 2.5 / 2.0 video generation, distilled from the highest-performing published cases on goodcase.ai. Use when the user asks to write, revise, or review a Seedance video prompt, or asks how to prompt Seedance for a specific kind of shot (narrative short, ad/commercial, UGC talk-to-camera, dialogue with lip-sync, action, product reveal, retro home video, travel film, pet or animal lead, comedy skit, horror, fantasy or sci-fi spectacle, 3D cartoon, car spot, fashion film, sports, storyboard image to video, and similar). Also use when the user names Seedance, 即梦视频, or Seedance 2.5/2.0 directly.

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Monsters, dragons, world reveals. Each entity gets its own definition block, the shots are cut by timecode, and scale is bought with reference objects and low angles rather than words like massive. Use when the user wants a Seedance 2.5 / 2.0 video prompt for this kind of clip (Epic fantasy and sci-fi spectacle; 中文触发词: 奇幻科幻大场面, 奇幻, 科幻, 巨龙, 怪兽, 史诗), or asks how published cases of this type were prompted. Read references/cases.md before drafting.

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Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.

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This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.

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Monitor portfolio risk, R-multiples, and position limits. Creates hedging strategies, calculates expectancy, and implements stop-losses. Use PROACTIVELY for risk assessment, trade tracking, or portfolio protection.

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Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

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Assemble a submission-ready replication package to the AEA Data and Code Availability Standard (DCAS) / openICPSR / Social Science Reproduction Platform expectations — standard replication README, dataset manifest, computational-requirements capture, a Table/Figure → script:line map, and a confidential-data deposit plan. Use when user says "build the replication package", "prepare the openICPSR deposit", "make the AEA data and code package", "DCAS compliance", "assemble the deposit for the journal", or after a paper is accepted and the journal's data editor needs the package. NOT a numeric verifier — it calls /audit-reproducibility to confirm claims reproduce before packaging.

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微信公众号排版。Use when the user asks to turn an article, Markdown draft, longform post, essay, newsletter, review, or Chinese article into a WeChat Official Account-ready HTML layout with local preview, selectable Claude/OpenAI/Google-inspired styles, and a button to copy rich HTML for pasting into the WeChat editor.

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。