「scale」の検索結果

Spawns full Claude Code peer sessions in separate terminal panes and git worktrees, real sessions rather than subagents, each with a directed task and contract brief, monitored and merged by the spawning lead. Detects the environment and uses herdr first, then tmux, then a native claude background agent. Use when work must persist or run beside the current session, needs a separate worktree or permission settings, should remain user-steerable in a visible pane, or when the user asks to spawn, hand off, or parallelize full sessions. Also spawns Codex peers into those panes. Excludes bounded consults and subagent-scale work.

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 publication figures for social-science manuscripts. Covers chart choice, axes and scales, color and accessibility, legend order, captions that carry the title, and script-based reproducibility. Use when making, revising, or critiquing a figure for a paper, such as a coefficient plot, small multiples, a distribution, a trend, a map, or a network. Finished figure sets go to figure-table-audit for end-stage QA.

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。

OCRs scanned or image-only corpora with vision-language models in three phases. Evaluate selects a system by measured CER or WER against stratified human ground truth. Run builds the production pipeline. Clean corrects raw OCR with LLM and rule-based passes, quality diagnostics, and span-level provenance. Use when choosing an OCR model for a corpus, measuring OCR accuracy, transcribing scans at scale, or correcting raw OCR. Born-digital documents with text layers go to doc-to-markdown.

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。

Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.

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。

Design and format publication-quality figures. Use for chart choice, color, scales, legends, captions, accessibility, and reproducible figure workflows.

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。

Advises on setting up and validating LLM text classification: codebook, learning regime and model choice, prompts, validation against human coding (kappa, F1), hybrid review, measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline. Use to classify, code, or label text at scale; category discovery goes to topic-modeling.

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。

Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.

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。

Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.

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。

Open a ready-to-use local 3D model composition workbench for GLB or GLTF files. Use when the user says "打开模型工作台", asks to place, rotate, scale, arrange, or frame one or more 3D models, or needs a clean composition screenshot before website or Image 2 design. If the user has not supplied a model, request GLB/GLTF files first; after models are available, generate the workspace, start its localhost server, and open it automatically.

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。

Transition from a hands-on "bricklayer" to a high-level "architect" by managing a fleet of autonomous AI agents. Use this when you need to scale engineering output with a small team, handle repetitive migrations/bug fixes, or onboard engineers to complex legacy codebases.

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 context manager specializing in information storage, retrieval, and synchronization across multi-agent systems. Masters state management, version control, and data lifecycle with focus on ensuring consistency, accessibility, and performance at scale.

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 whenever the user wants to find, shortlist, vet, or enrich US recruiting and staffing firms — executive search/retained search, RPO, tech/sales/healthcare recruiting, contingent/contract staffing, and temp staffing. Triggers on "find me an executive search firm for a CFO search", "shortlist three retained-search boutiques in NY focused on tech", "we need RPO support for a 50-engineer hiring push", or "pull contact info for these 8 staffing firm domains", even when described indirectly (need help hiring at scale, executive recruiter for senior roles). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Skip when the user wants to hire someone as their own employee (job-board questions, in-house recruiter hires, "where should I post the role"), individual job-seekers looking for recruiters to represent them, candidate-side career coaching, non-US firms, individual freelance recruiters.

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。

Generate a stage-appropriate marketing plan based on your company's MRR/stage. Uses the marketing-by-stage playbook to tell you exactly what to do (and what NOT to do) at pre-launch, early ($0-10K MRR), growth ($10-100K MRR), or scale ($100K+ MRR). Use when "what should I do for marketing", "growth plan", "marketing plan", "I just raised a round", "marketing strategy", "what's the right marketing for my stage", "GTM strategy", or any request for a stage-appropriate marketing roadmap.

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。

Self-learning SEO content pipeline: dashboard, multi-agent queue, token budgets, research → write → audit → publish. StoryBrand/Cialdini/JTBD-style frameworks; config-driven. Use for content factory, batch articles, or scaled publishing.

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。

Orchestrates Apify actors for large-scale web scraping via the Apify Client SDK. Manages actor runs, dataset exports, and proxy configuration through the Apify API v2.

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。

Agno is a high-performance Python framework for building, running, and managing agentic software at scale. It provides a three-layer architecture — framework, runtime, and control plane — enabling developers to go from agent prototype to production API in roughly 20 lines of code.

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。

Automates image editing workflows via the Adobe Photoshop API (Firefly Services). Supports smart object replacement, action playback, and PSD layer manipulation at scale.

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。

CTO-level architectural advisor for AI-native development. Use this skill whenever you encounter code design decisions, architecture discussions, system resilience questions, or any work touching: "architecture", "design", "scale", "dependencies", "state", "failure", "blast radius", "refactor", "migrate", "optimize", "resilience", "consistency", "observability", "bottleneck", "coupling", "monolith", "microservices", "distributed", "concurrency", "data flow", "system design", or any prompt suggesting code-first thinking when design-first thinking is needed. This skill integrates with Claude Code to review generated code for architectural soundness, define design systems via design.md, and guide teams toward CTO-level thinking. Trigger aggressively on architectural questions—this is where AI adds the most leverage.

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。

Rates any SKILL.md on a 0-100 scale across 10 dimensions with a SHIP / REWORK / SCRAP verdict. Evaluates trigger precision, instruction clarity, output predictability, edge case coverage, anti-hallucination guardrails, developer experience, composability, open-source readiness, wow factor, and real-world utility. No flattery — calibrated against Anthropic's own skill-creator rubric. Use this skill whenever someone says "score this skill", "rate my skill", "is this skill good", "skill review", "skill audit", "roast my SKILL.md", "grade this", "will this skill work", "evaluate my skill", "how good is this", or pastes a SKILL.md and asks for feedback. Also trigger when comparing two skills, benchmarking a skill collection, or asking "what's wrong with this skill" — even without the word "score".

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。

Explainability (XAI) audit for products containing AI components. Five-stage tier-scaled check: risk classification, stakeholder×question matrix, fidelity audit, system card, recourse path. Honest about what was validated functionally vs what needs user testing.

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 working on imp's quantization formats, loaders, or dequant paths - GGUF Q4_0...Q8_0/Q*_K/IQ4/MXFP4, NVFP4 two-level scaling (Modelopt vs compressed-tensors layouts), FP8 E4M3, StorageTier, decode cache, KV-cache dtypes (auto/fp16/fp8/int8/int4/nvfp4/mxfp4), "which quant should I use", "which KV dtype", scale-factor layout, dequant kernel wiring, imp-quantize / AWQ, judging quant quality (PPL). Do NOT use for writing/optimizing GEMM/GEMV kernels (sm120-cuda-expert) or measuring quant perf (benchmark-cuda).

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。

Structures growth-focused DD with revenue quality, customer concentration, technology scalability, and organizational readiness. Use when conducting growth DD, validating revenue sustainability, or assessing scale capability.

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。

Assesses leadership team capacity to scale with organizational design review, key person risk, and executive bench analysis. Use when evaluating management scalability, identifying talent gaps, or assessing organizational readiness.

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。

当用户需要分析企业/组织/产品的竞争格局、评估"做大"是否真的更好、判断某公司的护城河来源是否可持续时调用。典型触发信号：涉及"规模经济""市场占有率""大企业的困境""先发优势""网络效应""赢家通吃"等话题。不适用于纯财务分析(用 value-assessment)或纯心理偏差检查(用 misjudgment-checklist)。

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。