「ttl」の検索結果

Debug or validate Pulp animations / transitions / scroll behavior using in-process motion fixtures and offline visual analysis. TRIGGER on phrases like "animation is wrong", "transition timing off", "fade too late", "card slides too far", "scroll jumps on restore", "scroll offset wrong on restore", "frame jump in the timeline", "easing looks wonky", "imported Figma motion doesn't match source", "settle time / overshoot / drift / monotonic", "what value does the knob reach at frame N", "record this animation so I can replay it", "this animation is expensive — which one and why", "reduced-motion broken", "scrub a captured fixture". Captures fixtures (.motion.jsonl), and ships assertion helpers (is_monotonic / settling_time_seconds / overshoot / start_delay_seconds / final_value / local_step_outlier_ratio) and the `scroll_geometry` trace for `ScrollView` content-offset / visible-rect / content-size observability. Off by default in prod.

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Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire or the AI annex buyers now attach to it, executing sales sequences, coaching qualification, or deciding what the website's AI chat agent is allowed to say. Also triggers on: outbound, cold outreach, reply handling, reply classification, out-of-office replies, auto-replies counted as replies, our reply rate is inflated, prospect replied remove me, opt-out by reply, is this list ready to send, launch readiness, CRM segment outbound, we emailed a current customer, outbound hit an open opportunity, ownerless leads, prospects engaging our competitors, competitor connections, displacement list, is this personalization creepy, can we mention they visited our site, signal freshness, deal strategy, pipeline, battle card, objection handling, common objections, why do we keep hearing this objection, mine call transcripts for objections, objection register, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, AI security questionnaire, AI annex, AI vendor due diligence, the buyer is asking if we train on their data, do you train on customer data, which model do you use, model provider sub-processor, AI subprocessor disclosure, where does inference run, can the customer turn the AI feature off, AI-CAIQ, CSA AI Controls Matrix, ISO 42001, ISO/IEC 42001, NIST AI RMF, EU AI Act Article 50, are we a provider or a deployer, AI feature disclosure, our model vendor changed and nobody told the buyer, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss, website chatbot, chat widget, AI chat agent, AI SDR, inbound AI SDR, conversational marketing, live chat, what should our chatbot be allowed to say, our chatbot quoted the wrong price, the bot promised a discount, do we have to disclose it is AI, EU AI Act chatbot disclosure, chatbot handoff to sales, chat transcript review, prompt injection on our chatbot, Drift, Qualified, Intercom.

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Use when designing and operating the in-app purchase catalog of a game — SKU design, price-tier ladder, bundles, starter packs, battle pass tiering, A/B price tests, store config (App Store / Google Play / Steam DLC / web / web3), and per-region price localization. Triggers on "IAP catalog", "store SKUs", "pricing tiers", "starter pack", "bundle design", "battle pass tiering", "price test", "price localization", "App Store Connect", "Google Play Console", "Steam DLC", or when handed a monetization strategy with the catalog still open. Produces a catalog spec, per-region price tables, bundle compositions, and a price-test plan. For in-game economy curves see game-balancer.

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Automate Battlenet tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Generates structured summaries of mediation and arbitration proceedings, extracting outcomes, settlement terms, monetary amounts, obligations, releases, and enforcement steps. Trigger when the user needs to document mediation settlements, arbitration awards, or partial ADR resolutions from transcripts, agreements, mediator reports, or awards.

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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).

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Design multi-tier caching architectures for web applications — cache-aside vs write-through vs write-behind, TTL design, cache invalidation, Redis patterns, CDN configuration, browser caching, and stampede prevention. Use when choosing a caching pattern, designing cache invalidation strategies, implementing Redis caching, configuring Cache-Control headers, or preventing cache stampedes. Activate on "cache invalidation", "cache-aside", "write-through", "TTL", "Redis cache", "CDN caching", "cache stampede", "stale data", "browser cache". NOT for database query caching within an ORM, memoization of pure functions, or CPU-level caching.

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[Debugging] Use when analyzing or optimizing performance bottlenecks: database queries, N+1 fan-out, indexing, API latency, memory/GC, concurrency and pool saturation, algorithmic complexity (O(n²)), network/protocol round trips, frontend rendering and Core Web Vitals, caching, and distributed/resilience paths. Calibration constants and domain laws (latency ladder, Little's Law, utilization knee, CWV thresholds, symptom→cause triage) live in references/performance-knowledge.md.

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吉里吉里Z (krkrz) の -replweb HTTP+SSE サーバ (WebServer クラス) にブラウザ UI を載せて、本体アプリの操作/編集/観測パネルをブラウザ側に組み込む方法論。ゲーム本体は 3D 表示やゲーム内 UI に専念させ、編集ツール・インスペクター・ダッシュボード・REPL コンソールをブラウザ (別ウィンドウ/別PC) から使う構成を作るときに読む。プラグインや TJS がサーバへエンドポイントを追加公開する手順 (WebServer.register / serveStatic / broadcast)、ハンドラ呼び出し規約 (req %[method,path,query,body,bytes] → 文字列/octet/整数/辞書)、状態同期パターン (fetch POST + SSE /sub push + throttle + 差分/tick 配信)、既存 REPL コンソール (/events + /cmd) の UI 埋め込み、ブラウザのアプリモード起動 (Chromium --app / -webui)、json.dll 連携、TJS2 由来のハマりどころ (ローカル関数クロージャ不在→クラス化 / ブロックコメントのネスト誤爆 / startup 例外の致命性 / ハンドラは必ずメインスレッド実行) を網羅。エンジン側 WebServer クラスそのものの仕様は krkrz core doc/REPL.md、REPL/Agent 駆動は skill krkrz-repl、Elements ネイティブ UI は skill elements、TJS2 言語は skill tjs2、本体 API は skill krkrz を参照。

ネットワーク品質を総合的に診断し、ボトルネックの特定と根本原因の深堀りまで行うスキル。 接続情報、レイテンシ、ダウンロード速度、HTTP接続タイミング、経路解析を自動実行し、 品質閾値に基づく総合評価を日本語レポートとして出力する。 外部依存なし（OS標準ツールのみ使用）、macOSおよびLinux対応。 Use when diagnosing network quality, troubleshooting slow connections, identifying network bottlenecks, measuring latency/bandwidth/jitter, or generating network health reports. 「ネットワーク診断」「network diagnostics」「ネットワークが遅い」 「latency check」「bandwidth test」「traceroute analysis」

Psychoedukation zu Depression, Angststörungen, PTBS, bipolarer Störung, Schizophrenie, ADHS und Borderline. Wissensvermittlung ohne Diagnose.

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Use when the user asks to "build a B2B cold-outbound sequence", "design reply-triage branching", "plan a domain warmup / sending throttle", or "make my outbound CAN-SPAM / opt-in compliant"; produces a multi-step outbound sequence with reply-triage branches (positive / objection / referral / not-now / opt-out), a warmup + send-throttle ramp schedule, jurisdiction opt-in/CAN-SPAM guardrails (guidance, not legal advice), and a SEND S-dimension read. Not for B2C lifecycle flows — use email-sequence-designer; not for the consent record — use consent-registry; not for computing EQS — use email-quality-auditor. B2B冷启动外联序列/回复分流/域名预热

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Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire, executing sales sequences, or coaching qualification. Also triggers on: outbound, cold outreach, deal strategy, pipeline, battle card, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss.

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TanStack Pacer best practices for execution control in React — debouncing, throttling, rate limiting, queuing, and batching. Use when implementing search inputs, scroll handlers, API rate limits, task queues, bulk operations, or any scenario requiring controlled execution timing with reactive state.

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Use when working with Braintree — braintree payment gateway management including transactions, subscriptions, customers, payment methods, disputes, settlements, and merchant account configuration. Covers transaction success rates, decline analysis, settlement reporting, and fraud tool monitoring.

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Use when working with Aws Dynamodb Deep — aWS DynamoDB deep-dive management covering table configurations, capacity analysis, GSI/LSI health, auto-scaling policies, streams, backups, contributor insights, and TTL settings. Use when performing deep analysis of DynamoDB tables, optimizing capacity and throughput, debugging throttling, or auditing table configurations beyond basic inventory.

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Diagnose MSBuild build performance bottlenecks using binary log analysis. Only activate in MSBuild/.NET build context. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, errors, search, items, properties); falls back to dotnet msbuild binlog replay + grep/cat when the MCP is unavailable.

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One-question-at-a-time design interrogation. Use when: 'grill me', 'interrogate this', 'one question at a time', 'brainstorm', 'let's brainstorm', 'let's discuss', or design review start. Produces ADRs and glossary as decisions settle. Do not use for settled designs — load architecture-mode instead.

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Tests API rate limiting implementations for bypass vulnerabilities by manipulating request headers, IP addresses, HTTP methods, API versions, and encoding schemes to circumvent request throttling controls. The tester identifies rate limit headers, Belirle:s enforcement mechanisms, and attempts bypasses including X-Forwarded-For spoofing, parameter pollution, case variation, and endpoint path manipulation. Maps to OWASP API4:2023 Unrestricted Resource Consumption. Activates for requests invol...

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Stellar smart contract development (Rust, soroban-sdk). Entry point with project setup, contract anatomy, and build/deploy workflow, routing to three companion files in this directory — development.md (storage/TTL, authorization, cross-contract calls, tokens, events, errors, upgrades, fees, troubleshooting), testing.md (unit, fuzz, property, fork, mutation, integration), and security.md (vulnerability classes, checklists, tooling, audits). Use when writing, testing, reviewing, securing, debugging, or shipping Stellar smart contracts, including anything the user calls "Soroban" — Soroban contracts, soroban-sdk, Soroban auth/storage/TTL errors, SEP-41 tokens, or SAC integration from contract code.

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Inngest イベント駆動 Durable Execution プラットフォームリファレンス。 サーバーレス環境での長時間ワークフロー、バックグラウンドジョブ、スケジュールタスク実装。 createFunction, step.run, step.sleep, step.sleepUntil, step.waitForEvent, step.waitForSignal, step.invoke, step.sendEvent, step.fetch, step.ai.infer, step.ai.wrap, inngest.send, serve, connect。 concurrency, throttle, debounce, rate-limit, batching, priority, idempotency, singleton フロー制御。 Realtime（channels, topics, publish, subscribe, useRealtime, subscription tokens）による進捗配信。 Agent Evals, scoring, group.experiment, MCP dev server などエージェント運用機能。 Next.js / Express / Hono / Cloudflare Workers / Vercel / Netlify / DigitalOcean / Render デプロイ対応。

Debrief a finished Otameshi Tenshoku trial from the employer's side: set the observed facts against the completion criteria agreed before the start, keep expectations added afterwards, second-hand or off-the-job observations and the company's own late or missing support apart, settle the hours actually worked separately from any evaluation, and draft feedback to the candidate that states facts, support gaps and what happens next without promising a hire or continuation the company has not decided. Use when a hiring manager, recruiter or small-business owner wants to review a trial, decide what to tell the candidate, or work out the settlement after a shortfall; do not use to judge the candidate's aptitude or predict a hire, to cut pay or demand unpaid rework because of dissatisfaction, to compare candidates, or to send the feedback, pay, or update records on the user's behalf.

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。

Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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。