「apt」の検索結果

Skill for the Adapters area of paddock. 108 symbols across 22 files.

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Write and maintain DESIGN.md + PRODUCT.md — captures visual decisions and interaction patterns so design tasks stay consistent across sessions without re-scanning past work.

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Maintains repo-local action contracts and harness repositories where product CLI and MCP adapters stay thin over core libraries. Use when adopting or improving steward.yaml actions, capability-level adoption evidence, cold-start proof loops, probes, benchmarks, CLI/MCP/core parity, adapter refactors, packages/core boundaries, or sibling harness layout; use repo-quality-system-lifecycle first for general app/library/tool stewardship baselines.

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Best practices for Remotion - Video creation in React. 29 domain-specific rules covering 3D, animations, audio, captions, charts, transitions, and more. Use when building video in React with Remotion — animations, audio, captions, charts, or transitions.

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Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser session.

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Cloudflare Turnstileをprojectへend-to-endで導入する。codebaseを調査し、Cloudflare APIでwidgetを作成し、適切なformへ埋め込み、既存backendへ正式なserver-side siteverifyを接続して検証する。Turnstile追加、CAPTCHA設定、botからのform保護、Turnstile連携修正を依頼されたときに使用する。developers.cloudflare.com/turnstile/spinの中核手順へAIDD固有の安全規則を重ねたSkill。

吉里吉里Z (krkrz) の SDL3 / WINVER ビルドを REPL 経由でエージェントから駆動するためのリファレンス。krkrz を起動して TJS スクリプトを評価・検証・デバッグする、startup.tjs を介さず明示的に処理を開始する、入力イベント (キー/マウス) を注入する、画面をキャプチャして目視確認する、Elements ダイアログを観測・操作する、例外やダイアログ表示をコンソールで観測する、コアデモ全シーンを自動巡回してキャプチャで表示確認する (-demotest / -demotestcap)、といった場面で使う。**外部エージェントは console(CONIN$) に打てないので -replfile ファイルチャネルが本命**。起動フラグ (-repl / -replfile / -nostartup / -loglevel / -display / -ignoremouse)、ファイルチャネルのプロトコル、Agent API (入力注入 / captureScreen / dialogs / dialogClick)、ドットコマンド (.cap/.dlg/.click/.mem 等)、REPL 駆動時の挙動変更 (例外で即終了しない / inform と例外ダイアログがコンソールに出る) を網羅。TJS2 言語仕様そのものは skill `tjs2`、本体クラス API は skill `krkrz` を参照。

Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".

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Conducts authorized wireless network penetration tests to assess the security of WiFi infrastructure by testing for weak encryption protocols, captive portal bypasses, evil twin attacks, WPA2/WPA3 handshake capture, rogue access point detection, and client-side attacks. The tester evaluates wireless authentication, network segmentation, and the effectiveness of wireless intrusion detection systems. Activates for requests involving wireless pentest, WiFi security assessment, WPA2/WPA3 testing, or rogue access point detection.

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Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.

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Knowledge base from NIST SP 800-160 Vol 1 (Engineering Trustworthy Secure Systems) and Vol 2 (Developing Cyber-Resilient Systems). Use for systems security engineering (SSE): the three SSE framework contexts (problem/solution/trustworthiness), the 30+ design principles for trustworthy secure design, trustworthiness and assurance/assurance cases, security across the ISO/IEC/IEEE 15288 life-cycle process groups, and the Vol 2 cyber-resiliency engineering framework — 4 goals (anticipate/withstand/recover/adapt), objectives, 14 techniques, implementation approaches, and design principles for the APT/adverse-conditions threat model. Bridges systems engineering and the NIST security cluster (CSF/SSDF/RMF). Does not reproduce ISO/IEC/IEEE 15288 process text, nor cover SP 800-53 control catalogs in depth.

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Audit and repair a kohya-style LoRA dataset before training. Use when the user asks to check, validate, inspect, fix, clean, 体检, or 修 a dataset, captions, or tags. Inspect image count, repeats and step budget, resolution and aspect buckets, duplicates, corrupt or non-RGB images, missing or empty captions, trigger consistency, and tag-frequency hygiene. Return a structural PASS, WARN, or FAIL report and apply only confirmed fix_dataset.py actions, dry-run first with displaced originals quarantined.

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ユーザーの明示依頼により、dotfiles本体を中央engineで保全commit、統合、adapter再生成、submodule整合、pushまで同期する。

進行中の方針変更やキャンペーン再開で、短期の判断・仮説を自動capture / recall / triageする。長期記憶や日記には使わない。

実験やキャンペーンで次の判断を変える短期メモを capture / recall / triage する。方針変更やキャンペーン再開時に使い、単なる進捗・日記・過去経緯の検索には使わない。長期記憶は /ai-ltm、感想は /ai-diary。ユーザーが /field-notes と入力したら使う。

- Verify whether an image or video is authentic, original and correctly captioned — provenance checks, error level analysis, noise and JPEG compression analysis, clone and copy-move detection, lighting and shadow consistency, C2PA… Use when fact-checking a photo or video, checking for a deepfake or AI-generated image, spotting manipulation, or testing whether footage is recycled or miscaptioned

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Split one source post into tier-sized drafts (teaser, main social post, longread, technical dev-log) with cross-links downward and a canonical link back to the repository. Draft-first: publishes nothing itself. Triggers: "/episode", "/adapt", "adapt this post", "make an episode".

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Scan recent agent sessions for content-worthy moments and capture them as drafts in a publishing funnel, publishing nothing. Zero-token detector, then a taste judge, then capture. Triggers: "/content-mine", "mine sessions for content", "what in our sessions deserves a post".

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 command-only adapters for `eval` run mode using strict stdin/stdout JSON contracts.

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Use when the user asks to "turn this idea into posts for every platform", "write the X thread / LinkedIn post / 小红书 note", or "spec the carousel slides"; turns one idea into N platform-native ready-to-paste packages — post/thread, caption, Threads text, 小红书 note, link post with first-comment placement — each adapted per the dated platform norm card (never verbatim cross-posting), every product claim held to approved claims-ledger wording or flagged [needs source], every variant tagged {formula | hook family | CTA type | signal optimized}, plus a carousel slide-spec mode (intro/content/outro slide roles, per-element char budgets, 小红书 3:4 / IG 4:5 / LinkedIn document PDF artboards, text-safe-zone + alt-text checklist). Not for creator deliverable briefs — use brief-generator; not for repurposing existing assets with paid boost — use content-amplifier. 社媒文案/一稿多发改写/小红书笔记/轮播图脚本

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Use when the user asks to "grow my email list", "design a lead magnet / signup incentive", "set up double opt-in", or "plan a referral / recommendation loop"; produces a list-growth plan — acquisition channels, lead-magnet / incentive concepts, a compliant double-opt-in capture-flow spec, referral-loop mechanics, and subscriber-growth / cost-per-opt-in targets (labeled Estimated) — that feeds SEND-S (consent quality captured at acquisition) and SEND-N (lifecycle entry). Not for the signup page/popup UX itself — use landing-optimizer; not for recording the opt-in — use consent-registry; not for the confirmation-email copy — use email-creative-builder. 邮件列表增长/lead magnet/双重确认/推荐环

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Use when the user asks to "codify our brand voice", "define naming rules for our products and tiers", or "write the tone-of-voice guide with banned phrases"; produces the brand-level voice canon (register, tone spectrum, banned-phrase list, few-shot examples drawn only from the brand's own published material) and the naming tax (product / feature / tier naming rules plus approved and banned terms) that seeds the narrative-registry canon and that every channel's voice adaptation points up to. Not for per-platform voice adaptation — use channel-registry's voice-dossier; not for finished copy or blog posts — use content-writer; not for the message hierarchy itself — use message-system-architect; not for claim adjudication — use offer-claims-registry. 品牌语气/词汇表/命名税/禁用词/品牌语言规范

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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. Also triggers on: 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.

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Detects long-lived objects (caches, listeners, singletons) that capture an entire enclosing scope via closures/environment capture, keeping large arrays or buffers alive, and proposes copying only the needed fields.

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。