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概要と使いどころ
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.
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Estima, confirma e acompanha o gasto com geradores pagos. Use quando o usuário disser "quanto vai custar", "orçamento", "quanto gastei", "controlar custo", "está caro", antes de qualquer lote de geração, ou ao fechar campanha. Com APIs no centro, custo deixa de ser detalhe e vira restrição de projeto.
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Use when a change is small and already understood - at most three files, such as a typo, a config value, a rename, or a one-line bugfix - and a plan file would cost more than the change
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Analisa custo AWS e identifica desperdício em EC2, RDS, S3, EKS, EBS, Elastic IP, NAT Gateway e tráfego. Use quando o usuário disser "custo", "fatura", "está caro", "FinOps", "reduzir gasto AWS", "Cost Explorer", "otimizar custo". Quase todo leitura — seguro por natureza.
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When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' 'audience targeting,' 'Google Ads,' 'Facebook ads,' 'LinkedIn ads,' 'ad budget,' 'cost per click,' 'ad spend,' or 'should I run ads.' Use this for campaign strategy, audience targeting, bidding, and optimization. For bulk ad creative generation and iteration, see ad-creative. For landing page optimization, see page-cro.
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Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".
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Explain Ponytail's intended tradeoffs and measurement boundary. It does not estimate code, cost, or speed savings for the current repository. One-shot display, not a persistent mode. Trigger: ponytail-gain, "ponytail gain", "what does ponytail save", "show ponytail impact", or "ponytail scoreboard".
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。
Guides generic vs trait object decisions in Rust. Use when choosing between static and dynamic dispatch, designing traits, handling E0277/E0038, or deciding between enum and dyn Trait.
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Show ponytail's measured impact as a compact scoreboard: less code, less cost, more speed, from the benchmark medians. One-shot display, not a persistent mode, and not a per-repo number. Trigger: /ponytail-gain, "ponytail gain", "what does ponytail save", "show ponytail impact", "ponytail scoreboard".
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Promote project-specific memories into strict, small global memory only when they are cross-project, stable, useful, safe, and worth the global context cost. Load when the user says make this global, remember across projects, save this globally, or promote this learning.
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Plan which model tier handles which work BEFORE execution begins — a high-cognition model deeply understands the problem, lays the foundations, then emits a modular plan assigning each module the cheapest tier that can safely execute it, with escalation tripwires and one-way-door protection. Advisory only: it announces "next module → tier X / model Y" at each boundary and the HUMAN switches models — harnesses like Cursor cannot switch mid-run. Load when the user asks which model to use, wants a model plan, model tiers, model-tier routing, assign models to tasks or modules, says "cheap model got stuck", "which model for this task", "cost-efficient model choice", or when implementation-plan / problem-to-plan need a model: tier column. NOT dynamic-routing (plan-path selection after failure) — this skill assigns cognition tiers to work.
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Decompose an unknown quantity into 3-5 estimable factors and produce a defensible order-of-magnitude answer without needing precise data. Load when the user needs to size something without data — market size, resource requirements, effort estimates, user numbers, costs — or when a decision is blocked by "we don't know the numbers". Also triggers on "ballpark this", "rough estimate", "how big is this market", "how long would this take", "how many users", or when deep-thinking diagnoses a sizing/estimation frame. The goal is not precision — it is a defensible answer that enables a decision to be made. Based on Enrico Fermi's estimation method.
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You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.
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You know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.
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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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Use this skill when the user asks how to design or improve a Preventive Maintenance (PM) program for a fleet — mileage vs time intervals, A/B/C/D inspection levels, ATA standards, common PM tasks, scheduling software, cost tracking, and how PM affects roadside inspections + CSA scores. Reference ATA Equipment Maintenance Council standards.
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Use this skill when the user asks about trucking rate negotiation — cost-plus pricing, market rate benchmarking via DAT/Truckstop, fuel surcharge math, how to negotiate with brokers/shippers, when to walk away, contract pricing vs spot rates. Reference DAT Rate Analytics + freight market reports.
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Use this skill when the user asks about weigh-station bypass services — Drivewyze, PrePass, BestPass, NORPASS, OOIDA toll savings — how they work, eligibility criteria, costs, and how they tie into CSA scores. Reference each provider's coverage area + pricing.
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Use this skill when the user asks about truck purchase financing, leasing options, factoring, fuel surcharge math, lease-purchase agreements, the total-cost-of-ownership (TCO) comparison between buying + leasing, and how owner-operators finance their first truck. Cover Section 179 + Bonus Depreciation tax treatment.
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 this skill to evaluate document retention software options. Covers compliance features, ease of use, integration, and cost.
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Use when a carrier asks how to improve MPG, lower fuel cost per mile, manage fuel surcharge, set up a fuel-purchase strategy (in-network vs out-of-network), train drivers on fuel-efficient driving, spec new tractors for fuel economy, or benchmark fleet MPG against industry averages.
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Use this skill to evaluate whether to switch from annual MVR pulls to continuous monitoring. Covers cost-benefit, intervention timing, and major providers.
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 this skill when the user asks about driver ergonomics — back/neck/shoulder injury prevention, seat adjustment + lumbar support, entering and exiting cab safely, load handling techniques, vibration reduction, stretching protocols, and how ergonomics affects workers compensation costs in trucking. Reference OSHA + ATA ergonomics research.
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 a carrier asks about electric trucks — Tesla Semi, Freightliner eCascadia, Volvo VNR Electric, Peterbilt 579EV, range, charging, total cost of ownership, regulatory pushes (CARB ACT, ACF), incentives, infrastructure, depot vs en-route charging, and which routes/applications are EV-ready today.
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。