「validate」の検索結果

Generate a spreadsheet of test entries — newly designated names from OFAC, OFSI, and EU sanctions lists plus deliberate variations of those names — to validate that a sanctions screening system catches fresh designations and is tuned to the right fuzziness threshold. Use this whenever the user asks for sanctions list update test data, screening regression test data, screening QA, fuzzy match calibration, or wants to verify their screening lists are current. Trigger even if the user doesn't say 'screening' explicitly — phrases like 'test my sanctions list', 'check our SDN coverage', 'is my list up to date', or 'build me a regression set from the latest designations' should also invoke this skill.

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Takes a skill — a .skill or .zip archive, a bare SKILL.md, a folder, or just a raw idea — and produces a package ready for public distribution on lawve.ai, plus the exact entries for the submission form. Runs a gate first: skills with no plausible legal use, unsafe code, or licensing bars are declined with reasons, and general-purpose skills that could serve legal work are offered a legal adaptation instead. Then applies the standard compliance transforms — structure, metadata, attribution, dependency ladders, orphaned-file repair, limitations disclosure — validates the result, and delivers a zip. Raw ideas are gated first, built (via skill-creator where available), then adapted. Use for "make this Lawve-compliant," "prep this skill for Lawve," "package for lawve.ai," "is this suitable for Lawve," "get my skill ready to publish," or any request to ready a skill for the Lawve catalogue.

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When you want multi-source, multi-step research on a topic — competitor research before a sales call, market research for a new business idea, positioning angles, due diligence on a partnership or podcast guest, tech decision research (which DB, which auth), or any "I need to actually understand X." Combines web search, URL fetch, agent-browser, last30days (Reddit/X/YouTube/HN/web recency), memory, and Notion. Outputs a structured brief with citations, contradictions, gaps, and recommended next steps. Archives every research run to ~/.config/makerskills/deep-research/archive/ so past work is searchable. Triggers on "/deep-research," "research X," "investigate X," "do a deep dive on X," "look into X," "what's actually happening with X," "due diligence on X," "validate this market." Differs from a one-shot web search: this is multi-pass with verification.

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When you want to pressure-test a potential new business, product, or side project against the serial-founder filter. Not "marketing ideas for a product" (that's marketing-skills:marketing-ideas) — this is "should this business exist + can you win it." Runs the idea through a structured framework (problem, audience, wedge, monetization, moat, portfolio fit, distribution, energy fit, opportunity cost), checks domain availability via /domain, optionally triggers /deep-research for market validation, and outputs a viability brief: build / sleep on it / pass. Archives every idea to ~/.config/makerskills/business-brainstorm/archive/ so past work is searchable. Triggers on "/business-brainstorm," "/brainstorm," "new business idea," "should I build X," "pressure test this idea," "validate this idea," "is X a good business," "what about a [type] for [audience]."

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Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.

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Expert blueprint for MOBA games including lane logic (minion wave spawning every 30s), tower aggro priority (hero attacking ally over minion over hero), click-to-move controls (RTS-style raycasting), hero ability systems (QWER cooldowns, mana cost), fog of war (SubViewport projections), and authoritative networking (server validates damage). Use for competitive 5v5 or arena games. Trigger keywords: MOBA, lane_manager, minion_waves, tower_aggro, click_to_move, ability_cooldowns, fog_of_war, comeback_mechanics.

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Score a repository's agentic legibility from repo-visible evidence only. Use when Codex needs to audit how easy a codebase is for coding agents to discover, bootstrap, validate, and navigate, especially for harness-engineering reviews, developer-experience audits, repo cleanup, or before/after comparisons after improving docs, tooling, or architectural constraints.

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。

Expert blueprint for MOBA games including lane logic (minion wave spawning every 30s), tower aggro priority (hero attacking ally over minion over hero), click-to-move controls (RTS-style raycasting), hero ability systems (QWER cooldowns, mana cost), fog of war (SubViewport projections), and authoritative networking (server validates damage). Use for competitive 5v5 or arena games. Trigger keywords: MOBA, lane_manager, minion_waves, tower_aggro, click_to_move, ability_cooldowns, fog_of_war, comeback_mechanics.

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Analyze images, video, speech, motion, products, and websites; route local vision, audio, depth, tracking, matting, identity, and restoration models; auto-edit, replicate, enhance, caption, voice, assemble, annotate, validate, and export editable Timeline Studio projects and videos. Use for reference-video remakes, filter and repeated-shot reconstruction, subject-aware reframing, clip splitting, source-time speed curves, Color Wheels grading, ramps and holds, person or product cutout, person or object outline, authorized face swap, shot and timing reconstruction, timeline markers, chapters, beat cues, review notes and ranges, supplied or web-sourced footage, AI video platform selection, cleanup, highlights, product promotion, website walkthroughs, image-to-video assisted edits, optical-flow editing, depth-assisted relighting, grayscale depth-video conversion, depth/2.5D/transition finishing, AI voiceover, subtitles, short-form production, .timeline automation, or editor evaluation.

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Analyze existing XML sitemaps or generate new ones with industry templates. Validates format, URLs, and structure. Use when user says "sitemap", "generate sitemap", "sitemap issues", or "XML sitemap".

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Detect, validate, and generate Schema.org structured data. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", or "markup".

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Hreflang and international SEO audit, validation, and generation. Detects common mistakes, validates language/region codes, and generates correct hreflang implementations. Use when user says "hreflang", "i18n SEO", "international SEO", "multi-language", "multi-region", or "language tags".

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End-to-end implementation workflow. Use when user wants to implement a feature, fix a bug, or make changes and have everything validated and committed.

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Turn an app idea into validated, App-Store-ready name candidates. Use when the user says "name my app", "what should I call it", "app name ideas", "help me name this app", "is this name available", or needs to pick a brandable, ownable name before reserving it in App Store Connect.

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Clone this Aeon agent into a new GitHub repo - fork, configure skills, validate, and register in the fleet

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Three-mode idea engine - generate collides the week's zeitgeist with what you can ship into scored wedges; validate viability-screens the idea backlog; memo writes evidence-backed startup memos.

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Provides guidance for writing, optimizing, and benchmarking Triton kernels for Intel XPU GPUs (Battlemage/Arc Pro B50) using the Xe-Forge optimization framework. Includes an LLM-driven trial-loop workflow (analyze, validate, benchmark, profile, finalize), XPU-specific patterns (tensor descriptors, GRF mode, tile swizzling), KernelBench fused kernels, and Flash Attention.

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Adversarially review a grooming spec before implementation starts. Finds hidden risks, unvalidated assumptions, and missing dependencies. Standalone entry point for the challenger agent.

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META skill — build the largest possible qualified lead list for any request, end to end. Orchestrates discovery (Prospeo + GetLeads + Blitz + Google Maps + lookalike snowball from /list-expander), AI qualification with live-website verify, uncapped contact pull (GetLeads → Blitz → Prospeo), and provider emails (GetLeads/Blitz/Prospeo) validated with MillionVerifier before sending. Use for any "build me a list", "get me leads for X", "find everyone who...", client list request, or campaign list build. Starts with a 7-line intake brief; outputs to Postgres, Google Sheet, and/or CSV (asks which).

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Comprehensive product research and opportunity analysis for Amazon sellers. Analyzes demand, competition, profit potential, market entry barriers, and validates product ideas. Covers product sourcing, pricing strategy, and go-to-market planning. Use when the user asks about researching a product to sell, validating product ideas, product opportunity analysis, market research for Amazon, competition analysis, profit potential, should I sell this product, product viability, or any general product research questions.

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Validate and constrain untrusted input at the boundary. Use on any handler that accepts external data.

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Get JSON out of the model reliably. Prefer tool_use with a schema over prompted-JSON, validate on receive, retry on parse fail. Use when downstream code will parse the response.

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Use real run evidence, validation failures, source drift, platform drift, and user feedback to plan and, only after explicit approval, apply structural upgrades to an existing skill. Use when the user asks to improve an existing skill from recent runs, recurring failures, outdated sources, excessive bloat, changed platform behavior, or validated workflow feedback. Do not use to create a brand-new skill or to execute the business workflow itself.

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Applies Eric Ries's Lean Startup methodology for building products under extreme uncertainty. Use when iterating toward product/market fit, designing MVPs, deciding whether to pivot or persevere, setting up actionable metrics, or accelerating the Build-Measure-Learn loop. Triggers include 'how do we test this idea fast', 'what should our MVP look like', 'our metrics look good but we're not growing', 'should we pivot', 'we're building features no one uses', 'how do we measure validated learning', 'vanity metrics vs real metrics', 'how to do innovation accounting'. NOT for companies with proven product/market fit scaling a known playbook (use Crossing the Chasm), not for determining Market Type (use Four Steps), not for sales methodology (use SPIN Selling), not for pricing strategy (use Monetizing Innovation).

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。