「cost」の検索結果

This skill should be used when a model gets read-write control over its own live context window instead of a harness-scheduled compaction policy: the context exposed as an editable file the model rewrites with code tools, model-driven eviction and in-place updates, the harness invariants that keep self-editing safe (pinned prefix, edit gate, edit receipts, budget readouts, rollback on overflow), the prefix-cache cost of mid-context edits, and steering or training the model's own context-editing strategy. Route note content and fixed-threshold summarization to context-compression, cache-stable layout under harness control to context-optimization, file offloading to filesystem-context, and loop governance to self-improvement-loops.

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。

This skill should be used for improving context efficiency: context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality.

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。

This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-optimization.

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。

Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

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。

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

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。

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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。

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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。

Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).

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。

Read the Convex deployment's 72h insights (read limits, OCC contention), root-cause each event in code, report evidence-backed perf/cost findings with fixes.

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。

Evaluate feature investments using revenue impact, cost structure, ROI, and strategy. Use when deciding whether a feature deserves investment.

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。

When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,' 'conversion rate,' 'CAC,' 'LTV,' 'NRR,' 'GTM dashboard,' 'magic number,' 'pipeline velocity,' or 'funnel metrics.' This skill covers GTM measurement from metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences. Do NOT use for technical implementation, code review, or software architecture.

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。

When the user wants to price an AI product, choose a charge metric, design pricing tiers, or optimize margins. Also use when the user mentions 'AI pricing,' 'usage-based pricing,' 'consumption pricing,' 'outcome pricing,' 'BYOK,' 'bring your own key,' 'per-seat pricing,' 'pricing tiers,' 'AI margins,' 'cost per token,' or 'pricing model.' This skill covers pricing strategy, packaging, and margin management for AI-native products. Do NOT use for technical implementation, code review, or software architecture.

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 a live Three.js landscape that stays quiet behind a subject — a noise heightfield on a polar grid so resolution follows the lens, ground coloured by slope and moisture rather than by texture, instanced GPU grass whose wind costs nothing on the CPU, scattered stones, a gradient sky dome, a star field you can actually see, and a time-of-day system that cross-fades instead of cutting. Use for hero backdrops, product stages, scroll worlds, or any scene where a building, object, or figure has to sit in a place rather than float on a gradient.

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 a cursor trail whose spacing stays constant at any hand speed, by emitting motes per unit of distance travelled rather than on a timer, so a flick draws the same continuous ribbon as a crawl instead of breaking into scattered dots. Covers sub-segment placement, the ring-buffer ordering trap, the idle breath a distance emitter needs, anchoring the trail to the screen in a 3-D scene, scaling scatter against the plane it hangs on, coasting instead of stopping dead, touch and reduced-motion fallbacks, and why moving the emitter to a DOM overlay to raise its z-index costs more than it buys. Use for cursor wisps, pointer sparks, embers, magic trails, comet tails, plankton, dust, or any mote trail that must stay legible however fast the hand moves.

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 sharp, physically coherent high-resolution materials for 3D rendering with appropriate PBR maps, texel density, UV direction, mipmaps, anisotropic filtering, and progressive asset delivery. Use for detailed wood, stone, fabric, foliage, and architectural close-ups without excessive download or GPU memory costs.

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。

Score a game's combat skills and spells out of 10 with blind judges, then raise the weakest to a bar, for timing and readability (does a skill read as itself, does the blow land when it lands) and for art (detail, light, trails, impact, art direction, judged against a reference such as Diablo IV). Covers filming casts on a stopped, stepped clock, contact strips and close-ups, anonymised two-judge rounds, the levers that moved scores (hero props made from a painted concept with image-to-3D, own schedules and keyed moves, painted additive textures, camera-facing ribbons, light outside the body), the traps that cost points, asset and load budgets, and shipping each skill as its own version. Use when the user asks to score, review, improve, polish or rebuild combat skills, spells or attacks; says a skill "doesn't make sense", "looks bad" or should look like another game; names a target score; or starts a "next skill" thread. Project specifics (review hooks, harness settings, where each skill's code lives, scores so far) go in an optional local references/<project>.md next to this skill.

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。

Launch and navigate the ccboard TUI/Web dashboard for Claude Code. Use when monitoring token usage, tracking costs, browsing sessions, or checking MCP server status across projects.

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。

Extracts full item detail from any open eBay item URL, returning JSON with url, itemNumber, title, subTitle, categories, price, priceWithCurrency, currency, wasPrice, available, availableText, sold, image, images, seller, sellerUrl, sellerFeedbackCount, sellerPositiveRating, itemLocation, brand, type, mpn, upc, ean, condition, shippingCost, shipping, whyToBuy, and itemSpecifics (full label/value map). Works across all eBay regional TLDs. Use when user mentions eBay item page, eBay listing detail, eBay product page, eBay itm, ebay.com/itm, eBay item scraper, extract eBay item details, eBay item specifics, scrape eBay item, eBay sold count, eBay availability, eBay brand MPN UPC EAN, eBay item location, eBay seller feedback, eBay wasPrice, eBay hi-res images, eBay itemNumber, eBay per-item enrichment. Also applies to enriching item URLs from a listing, monitoring price and stock, competitive product research, brand catalog audits, and pulling full field set from an individual eBay listing.

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。

Shifts Layer 3 model selection to cost-optimized alternatives when economy mode is active.

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。

Squad's command catalog and interactive menu. Invoke via /squad (slash command) or natural language ("squad commands", "what can squad do", "show me squad options"). Presents categorized operations (Install & Upgrade, Team Management, Issues & PRs, Plugins & Skills, Model & Cost, Sessions & State) as an interactive picker. Routes to the right squad CLI command or the Squad coordinator agent.

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。

Compare options with weighted scoring, pros and cons, pre-mortems, opportunity costs, and ICE prioritization. Use when a user wants to reason through a choice, expose assumptions, or rank alternatives.

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。

Hunting skill for graphql vulnerabilities. Built from 12 public bug bounty reports across IDOR via node() / GID, mutation IDOR including AI/LLM features, cross-tenant IDOR, SSRF via argument, batching-DoS, query-cost-bypass, SQLi via argument, broken-object-level-authz, auth-bypass via unscoped mutations, and PII exposure from missing field-level authz. Use when hunting graphql on any target.

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 English text must be parsed without a human to resolve ambiguity — tool descriptions, error messages, inter-agent instructions, system prompts, status reports — and misreading has a real cost, or when text reads as dense, hedged, or easy to misparse. Triggers: disambiguate, STE100 rewrite, apply Simplified Technical English, plain-language rewrite, controlled-language rewrite, rewrite so an agent cannot misread this. Not for creative or marketing copy.

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 a TRES Finance dashboard ledger URL (Transactions tab) with a precise filter set — date range, wallets, assets, tags, status, amount, and more — so it can be shared with a customer or reused. Use whenever the user asks for a "ledger link", "dashboard link", "filter URL", or "send <customer> a link to <some filtered view>". Scoped to the Transactions tab only (not Accounting, Roll Forward, Pivot Tables, Cost Basis, or Trial Balances).

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。