「ast」の検索結果

Capacity planning — forecasting demand, sizing infrastructure, and avoiding both outages and waste — use when scaling systems.

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Idiomatic Astro: islands architecture, content collections, SSR/SSG choices, and performance-first builds. Use when writing, reviewing, or structuring Astro sites.

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Search Ticketmaster events and seats with pricing. Returns Buy-now links to Ticketmaster checkout; it cannot complete a purchase itself.

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Audit demand forecasting models and inventory optimization logic -- forecast accuracy (MAPE, bias), safety stock calculations, reorder point strategies (EOQ, ROP, min/max), ABC/XYZ classification, demand signal pipelines, and multi-location network optimization..

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Analyze demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining pipelines.

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Analyze food supply chain systems for waste reduction opportunities including shelf life prediction models, FIFO and FEFO inventory rotation enforcement, demand forecasting accuracy and bias, donation logistics workflows, cold chain temperature monitoring, and sustainability reporting against EPA Food Recovery Hierarchy and UN SDG 12.3 targets.

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Manage Buzzsprout podcast hosting: episodes and players via its JSON API. Trigger phrases: buzzsprout, podcast episodes, publish episode, podcast hosting.

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Minimal personal narrated-video pipeline — a topic becomes a talking-head-free explainer MP4 (1080p or 4K) via script → Azure TTS (SSML) → Remotion. Use when the user wants a quick narrated video from a topic without the full video-podcast-maker machinery (no extra skills, no thumbnails/shorts/publish matrix). Do NOT trigger for heavy production needs — use video-podcast-maker for those.

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Turn one approved master still into a character's whole animated sprite set - one image-to-video clip per action (idle, walk, run, attack, jump, hurt, cast and so on), all from that still, generated through generate2dmedia route_media.py (the xAI API when a key is configured, else the local Grok CLI) - then gate each take, key it with a soft matte, register it to the master, pick loops or retime actions, finish (HD by default, pixel on request) and package fixed-canvas frames, alpha WebM, packed MP4 for iPhone and a PNG fallback with engine metadata and a JS runtime. Also processes a supplied clip. Use for character and creature animation, fluid motion and any video-to-sprite work. Not for FX, icon or prop sheets (generate2dsprite), maps or plate loops (generate2dmap), code-drawn animation (codeart2d, only on request) or calling a video API by itself (generate2dmedia).

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Smallest personal narrated-explainer-video pipeline (spoken narration over visuals, not an audio podcast), fully tool-agnostic and autonomous by default — topic → research ∥ asset collection → script → TTS → video → 4K render ∥ publish info + cover. The skill defines the pipeline logic and self-verified checkpoints; any TTS backend and any video tool (Remotion, HyperFrames, CapCut, ...) work, and how much human oversight to apply is set by the working project's AGENTS.md/CLAUDE.md, not here. Use when the user wants a quick personal narrated video with minimal steps, whether or not they name the tool stack. Do NOT trigger for audio-only podcasts, written episodic content, or heavy multi-format production.

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Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.

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Use when configuring or troubleshooting Salesforce Enterprise Territory Management (ETM): territory models, territory types, territory hierarchies, account assignment rules, opportunity territory assignment, and forecast by territory. Trigger keywords: territory model, territory hierarchy, ETM, assign accounts to territories, territory forecast, territory activation. NOT for choosing the coverage model first — use admin/territory-design-requirements. NOT for role hierarchy — use admin/sharing-and-visibility. NOT for Field Service ServiceTerritory — use admin/fsl-service-territory-setup. Trigger keywords: Territory2, Territory2Model, Territory2Rule, territory2Model-meta.xml, ObjectTerritory2Association, UserTerritory2Association, territory assignment rule, run assignment rules, territory access level, Manage Territories permission.

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Orchestrate AI coding agents (Claude Code, Codex, OpenCode) in isolated sandboxes using the @ai-hero/sandcastle SDK. Use when the user needs to (1) run agents AFK in Docker/Podman containers, (2) build multi-agent pipelines with plan-execute-review patterns, (3) run parallel agents on separate worktrees, (4) create iterative agent loops with maxIterations, (5) extract structured output from agent runs, (6) set up sandcastle in a new or existing project, or (7) write prompt files with template args and shell expressions.

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Comprehensive astrology expert covering natal charts, transits, houses, aspects, and astrological traditions from Western to Vedic

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Expert in French pastry techniques, from croissants to éclairs, macarons to tarte tatin

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Learn project rules from accumulated fix patches in .unikit/code/patches/. Analyzes past mistakes, extracts prevention points, classifies each as a coding rule (→ RULES.md) or a per-skill workflow rule (→ skill-context), and proposes them for approval. Use when the user wants to turn past fixes into rules — "evolve", "evolve the rules", "learn from past mistakes", "learn from the fixes", "analyze the patches", "what rules should we add from recent fixes". Best run after several /unikit-fix sessions have left patches behind. This derives rules from accumulated patch history — to add a single rule by hand use /unikit-rules, and to promote mature RULES.md entries into the knowledge base use /unikit-memory migrate-rules.

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塔罗牌占星：把「抽三张塔罗牌」与「西方占星本命盘」合为一次解读。 用户带着一个具体问题来 → 抽三张牌（可复现的 seed）→ 若愿意提供生日，则用脚本 确定性地排出星座与星盘 → 用黄金黎明的塔罗↔占星对应把两者接起来（牌落在黄道哪一旬， 你的哪颗行星正站在那里）→ 给出一张交叉对照表、对所问之事的审慎取向与可执行建议。 塔罗层：牌阵位置语义、牌与牌的组合叙事、元素尊贵、正逆位、直觉对话、治疗性解读。 占星层：本命盘、相位与宫位、必然尊贵、行运盘推运、比较盘/组合盘（合盘）、太阳弧推运。 当用户说：塔罗、塔罗牌、抽牌、抽三张牌、占卜、算一卦（西洋）、牌阵、正逆位、 星盘、本命盘、星座、上升星座、月亮星座、占星、行运、流年、合盘、比较盘、 太阳弧、推运、我该不该……、这段关系会怎样、我适不适合换工作 时使用。 English triggers: "tarot", "tarot reading", "draw three cards", "what do the cards say", "spread", "reversed card", "natal chart", "birth chart", "astrology", "rising sign", "transits", "synastry", "composite chart", "solar arc", "progressions", "should I take this job", "what does my chart say". 内置离线脚本：抽牌洗牌（可复现 seed）、本命盘（行星/四轴/宫位/相位/元素统计）、 必然尊贵、牌↔盘共鸣、元素数字统计、行运盘、比较盘+组合盘、太阳弧+次限推运。 边界：要**周公解梦**请用 `zhougong-dream-interpretation`；要**四柱八字**请用 `bazi`； 要**中式黄历择日/测吉凶**请用 `zhanshi-jixiong`。本技能只做西洋塔罗与西洋占星。 即使用户只是随口说「帮我抽张牌看看」「我最近水逆吗」，也应主动使用本技能。

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Write a short, fact-checked reply that answers a colleague's question in one meaning only, ready to copy and paste. The input is their pasted message. Check the claims against the code first, use the project glossary terms, and print only the reply in chat. Read-only — no writes to code, the issue tracker, GitHub, chat or any MCP. Use on "/cortex-communication:answer-buzzkill", "help me answer", "reply to this question", or when the user pastes a colleague's question and asks for an answer in the same manner.

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View, add, and execute Aavegotchi Baazaar listings on Base mainnet (8453). Buy with GHST directly or buy with USDC using swapAndBuy*. Safety-first: dryRun defaults true (simulate with cast call; only broadcast with cast send when dryRun=false / DRY_RUN=0).

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Design, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.

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Plan and implement infrastructure, containers, CI/CD, deployment safety, observability, and rollback changes. Use for Docker, IaC, cloud runtime, Kubernetes, pipeline, deployment, or operational reliability work. Does not read dotenv files or approve/release deployments.

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Inspect reviewed Grafana infrastructure evidence for host or fleet CPU usage, memory load, disk space, and system load. Use for server-health checks, capacity symptoms, resource saturation, disk pressure, cloud or local host comparisons, and bounded infrastructure triage.

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Italian geographic and property data through Openapi - geocoding/reverse geocoding, zip codes (CAP), municipalities/provinces/regions with ISTAT codes, Italian cadastre (catasto) property and owner lookups, visure catastali/ipotecarie, real estate valuations.

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Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).

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