「ast」の検索結果

"Build ARIMA models for time series forecasting with trend and seasonality decomposition. Use this skill when the user needs to forecast future values from historical sequential data, test for stationarity, or select ARIMA parameters — even if they say 'time series forecast', 'predict next month sales', or 'ARIMA model'.".

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Use when doing structural code search and rewriting - ast-grep linting, refactoring, multi-language patterns

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。

Hunts for Kerberoasting and related ticket attacks by analyzing Kerberos service ticket request events (4769) for weak encryption types, abnormal request volume per account, and non-machine accounts requesting many SPNs. Activates for requests to hunt Kerberoasting, detect RC4 ticket requests, or find SPN-targeting credential attacks.

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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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Use when a task needs the judgment of a Brewmaster — setting or correcting mash pH before conversion, calibrating yeast pitch rates from wort gravity, deciding whether a diacetyl (VDK) reading clears a batch for cold crash or requires a diacetyl rest, investigating a contamination or off-flavor signal and scoping a recall, or specifying dissolved-oxygen and pasteurization controls to hit a TTB-compliant ABV label.

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Creates Pulumi infrastructure-as-code projects in Go, configures OIDC authentication, integrates with Pulumi ESC for centralized secrets and configuration management, and builds multi-language component resources. Use when setting up Pulumi Go projects, writing infrastructure code with Go, configuring OIDC for Pulumi, using Pulumi ESC with Go, automating cloud infrastructure with Golang, creating reusable Pulumi components in Go, or working with pulumi-go-provider. Also use when the user mentions Pulumi with Go/Golang, AWS/Azure/GCP infrastructure in Go, or Go-based ComponentResource patterns.

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Creates Pulumi infrastructure-as-code projects in Python, defines cloud resources (AWS, Azure, GCP), configures ESC environments for secrets management, sets up OIDC authentication for secure deployments, and builds multi-language component resources. Use when creating Pulumi Python projects, writing infrastructure code, configuring cloud providers, managing secrets with Pulumi ESC, setting up OIDC for Pulumi, automating infrastructure deployments with Python, creating reusable Pulumi components in Python, or configuring Python toolchains (pip, poetry, uv) for Pulumi. Also use when the user mentions pyproject.toml with Pulumi, component_provider_host, or Python virtual environments for infrastructure code.

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Scaffolds Pulumi TypeScript infrastructure-as-code projects, writes IaC code with proper resource configuration, manages Pulumi ESC environments for centralized secrets and configuration, configures OIDC authentication for cloud providers, and builds multi-language component resources. Use when the user asks to create Pulumi TypeScript projects, write Pulumi infrastructure code, set up ESC environments, configure OIDC for Pulumi, implement infrastructure automation with Node.js/TypeScript, create reusable Pulumi components, or work with stack references. Also use when the user mentions Pulumi with TypeScript, AWS/Azure/GCP infrastructure in TypeScript, or PulumiPlugin.yaml for multi-language components.

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Analyze catastrophe modeling systems for natural disaster exposure, PML estimation, and reinsurance optimization. Use when: 'assess cat model', 'evaluate disaster exposure', 'review PML calculations', 'audit reinsurance program', 'check exposure data quality', 'analyze hurricane/earthquake risk models', 'evaluate Oasis or RMS setup'.

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Make game-ready 2D characters, creatures, props, icons and FX as master stills, sprite sheets or animation clips. Plan size, motion and finish; generate the art (image generation is the default, through generate2dmedia route_media.py - the API when a key is configured, else the local Codex or Grok CLI); approve one master still per character; then key, slice, register, scale, palette, QA and export frames for a game engine (Aseprite JSON, Godot SpriteFrames or Sprite3D). HD finish by default, pixel finish on request. Character animation is one image-to-video clip per action from the master (video2dsprite); sheets are for FX, icons and props. Use for sprites, masters, sheets, frame-by-frame animation and packaging frames from any source. Not for maps, tiles or backgrounds (generate2dmap), video clips (video2dsprite), code-drawn art (codeart2d, only on request) or calling an image or video API by itself (generate2dmedia).

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。

Use when the user gives a topic and wants an automated topic-driven narrated explainer, podcast, or knowledge-summary video (Bilibili / YouTube / Xiaohongshu / Douyin / WeChat Channels), or asks to learn visual design patterns from a reference video/image. Trigger when the user mentions creating a knowledge video, narrated explainer, video podcast, or animated infographic-style video from a topic — even if they don't say "video podcast" explicitly. Also trigger when the user wants to regenerate, re-render, rebuild, update, or iterate on a narrated video this skill already produced — e.g. they edited the script/prompt, changed the visuals, or swapped the background music and want the final video remade (reuse the existing videos/{name}/ directory, never start a new project). Do NOT trigger for generic video editing, trimming, format conversion, color grading, or non-narrative video tasks. Produces 4K video via research → script → TTS → Remotion → MP4 + BGM.

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Revenue Intelligence setup: activating the RI app and its CRM Analytics assets, Einstein deal insights, the opportunity-change waterfall, and forecast-accuracy analytics for sales leaders. NOT for configuring the Pipeline Inspection view, its metrics and review cadence - use admin/pipeline-review-design. NOT for forecast types, categories, rollups and quotas - use admin/collaborative-forecasts.

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Comprehensive Mastra framework guide. Teaches how to find current documentation, verify API signatures, and build agents and workflows. Covers documentation lookup strategies (embedded docs, remote docs), core concepts (agents vs workflows, tools, memory, RAG), TypeScript requirements, and common patterns. Use this skill for all Mastra development to ensure you're using current APIs from the installed version or latest documentation.

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Create an implementation plan for a feature — a dependency-ordered, actionable task list for the project. Has four modes: fast (a quick single-pass plan), full (a richer plan that can also create a git branch), ultra (a multi-file bundle — a manifest plus one deeply specified file per phase — for execution by a smaller model), and add (extend an existing plan with more tasks). Pick the mode from the user's wording: "full plan" runs full; "quick plan" or "fast plan" runs fast; "ultra plan", "ultraplan" or "ultra-plan" runs ultra; a plain "create a plan" with no qualifier defaults to fast; "add to the plan" or "extend the plan" runs add. Use whenever the user wants to plan a feature or task, e.g. "create a plan", "create a full plan", "create a quick plan", "run an ultra plan for the inventory system", "plan this feature", "add this to the plan", "extend the plan", "add a phase to the plan". Ultra runs only when the user names it — never because the feature looks large.

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View, create, cancel, bid, and claim Aavegotchi GBM auctions on Base mainnet (8453). Subgraph-first discovery (Goldsky), with onchain verification + execution via Foundry cast. Safety-first: DRY_RUN defaults to 1 (simulate with cast call; only broadcast with cast send when DRY_RUN=0 and explicitly instructed).

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Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL, Stockholm) and re-encode FASTQ quality offsets using Biopython Bio.SeqIO. Use when changing a file format for a downstream tool, fixing FASTQ quality encoding (Phred+33 vs Phred+64 vs Solexa), or when a conversion risks silently dropping annotations or quality scores.

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Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Guides timing of breast surgery for transgender males based on physical and mental health status. Triggers include transgender male considering breast surgery or requesting mastectomy.

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Applies standard breast-screening guidelines for transgender females who have been assessed as having no known increased risk of breast cancer. Trigger phrases include transgender female and assessed as no increased breast cancer risk.

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 content-focused websites with Astro — zero JS by default, islands architecture, multi-framework components, and Markdown/MDX support.

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Premiere Pro 動画編集ワークフローを Claude Code で自動化するスキル集。/cut は Premiere Pro XML の無音・雑音区間をジェットカット、/srt は WAV+Premiere XML から日本語テロップ用 SRT を並列Whisper→LLM意味区切り→全体アライメントSRT の3ステップ(v6)で生成、/srt-fast は改行工程まで3チャンク並列化した高速版、/telop-check は書き出し済みMP4を全編スキャンしてテロップの数字表記・誤字脱字・い抜き・ら抜き・固有名詞ミスを検出する。faster-whisper / ffmpeg / Premiere Pro を要するローカル実行型。日本語トーク動画のショート / ロング編集向け。

Expert-level radio astronomy covering radio telescopes, interferometry, VLBI, pulsars, spectral line observations, fast radio bursts, and aperture synthesis imaging.

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-level astrophysics covering stellar physics, galaxy formation, high-energy astrophysics, observational techniques, and the electromagnetic spectrum across wavelengths.

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。