650 件 ・ 関連度順
概要と使いどころ
Parse PDFs, Office files, images, and HTML into Markdown/structured outputs with MinerU. Use when SciForge or Codex needs document parsing/OCR for scientific papers, supplementary files, PDFs, scanned documents, tables, formulas, or URL/local-file parsing through MinerU standard or Agent lightweight APIs.
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。
Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.
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 a customer health scoring model — define signals, weights, thresholds, and action triggers. Use when asked to "build health scoring", "how do we predict churn", "what signals indicate a customer is at risk", or "design our health model".
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 a type scale and hierarchy — sizes, weights, line-heights, and named tokens. Use when asked to "design a type scale", "set up type hierarchy", or "define typography tokens".
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。
Size, colour, weight and position mark what matters most. Use when designing button groups, CTAs, cards, dashboards or any layout with a primary action.
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。
Systematic departures from rational choice theory and their implications for economic analysis and policy. Covers cognitive heuristics (anchoring, availability, representativeness), biases (loss aversion, status quo, overconfidence), prospect theory (reference dependence, probability weighting, diminishing sensitivity), nudge theory and choice architecture, and the integration of psychological findings into economic models. Use when analyzing decision-making under uncertainty, evaluating policy interventions that exploit behavioral patterns, or assessing where standard rational-agent models break down.
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。
Causal reasoning in historical analysis. Covers distinguishing immediate from underlying causes, tracing chains of causation across time scales, identifying unintended consequences, and applying counterfactual reasoning to assess causal significance. Use when analyzing why historical events occurred, evaluating the relative weight of causes, or assessing whether outcomes were inevitable or contingent.
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。
OpenStack Nova compute service skill for deploying, configuring, operating, and troubleshooting cloud compute infrastructure. Covers instance lifecycle management (create/stop/start/reboot/resize/migrate/evacuate), compute scheduling with filters and weights, flavor management, live migration procedures, hypervisor management (KVM/QEMU via libvirt), placement service for resource tracking, CPU pinning, NUMA topology, huge pages, cell mapping, VNC/SPICE console access, and capacity planning. Use when deploying compute via Kolla-Ansible, managing instances, debugging scheduler failures, or planning compute capacity.
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。
Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
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。
Retroductive hypothesis sampling via MCMC: given observations, importance-sample explanations weighted by P(observation|hypothesis) using Gay.jl colored particles as hypothesis candidates.
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 pattern for converting rule-following, checklist, or rubric skills into a fan-out map-reduce ensemble of parallel rigid sub-agents with corroboration-weighted merge; worker model tier and diversity are knobs matched to inference load and stakes, not fixed values. Apply when creating or refactoring a skill or agent that applies 10+ independent criteria in a single pass. Triggers on: 'review against a checklist', 'fan out', 'map reduce review', 'ensemble', 'split the rules', 'apply rubric', or any large ruleset being applied by one agent in one pass. NOT for tight single-pass transforms or rulesets under 5 criteria.
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。
Synthesis step in the multi-angle technical research pipeline. Receives structured outputs from all four research angles (api-state, ecosystem-research, impact-measurement, codebase-auditor), applies cross-angle signal weighting and conflict resolution, and produces a single synthesized Research section. Invoked by the technical-researcher orchestrator after all angle skills complete. Returns content to the orchestrator — does not write to the backlog.
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。
Analyze git repository history to produce a codebase risk profile before code review. Probes repo size, runs 7 parallel git analysis pipelines (hotspots, bug magnets, bus factor, contributor momentum, firefighting frequency, newly added files), cross-references hotspots with bug magnets to identify high-risk files with primary owner annotation, and writes walkthrough/recon-report.md. Use before code review to prioritize reviewer attention, at the start of linear-walkthrough Phase 1 Discovery to weight file coverage, or when onboarding to an unfamiliar codebase.
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。
Strategic owner of Horizon, the VNX future-state layer (roadmap -> tracks -> deliverables) and the plan-first gate. USE THIS when the user wants to plan the next VNX feature, decide what to build next, add something to the roadmap, prioritize or schedule (inplannen) work into now/next/later horizons, break a feature into deliverables, set the routing FLOOR, or run the plan-gate on a feature. The tracks DB (`vnx horizon`, alias `vnx objective`) is the source of truth; the repo ROADMAP.yaml is a generic example, not the SSOT. Plans and gates only: never dispatches, never closes open-items. The plan-gate panel is proportional (0-5 seats, by governance weight) and runs only on an explicit plan-gate step. (Renamed from `pm` 2026-07-05 — `/pm`/`@pm` still resolve via the backward-compat alias in `.claude/skills/pm/SKILL.md`.)
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 alternatives through a weighted multi-criteria decision matrix. Use when the user calls for the *evaluation* of *alternatives*, wants to *compare* things, or asks what the best is from a list of choices.
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 FinOps guidance for cloud, AI, and SaaS technology spend. Covers AI cost management, agentic FinOps, GenAI capacity planning, self-hosted vs managed inference, Anthropic billing, open-weight vendor APIs (DeepSeek, Qwen, Kimi, GLM), AWS (EC2, Bedrock, SageMaker, Savings Plans, CUR), Azure (reservations, AHB, OpenAI PTUs), GCP (Vertex AI, BigQuery), OCI, Kubernetes cost allocation, data platforms (Databricks, Fabric F-SKUs, Snowflake), tagging governance, SaaS management (SAM, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Codex), ITAM, allocation, showback, chargeback, anomaly management, KPIs and benchmarking, waste detection playbooks, workload onboarding and M&A, and GreenOps (cloud carbon, CSRD). Use for any question about technology cost or waste in your estate - expiring commitments or RIs, NAT gateway bills, idle VMs, snapshot sprawl - and for commitment sizing, rightsizing, allocation, SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow.
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 adding a new model or pipeline to diffusers, setting up file structure for a new model, converting a pipeline to modular format, or converting weights for a new version of an already-supported model.
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阿杭减肥系列视频制作入口。用于减肥口播剪辑、续剪和已确认完整交付，选择本系列范围后调用另行安装的剪辑引擎与减肥封面流程。
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Evidence-first marketing audit of a third-party local business (café, roaster, shop, restaurant, hotel, clinic, home service) from a Google Maps, share.google or website link — located DataForSEO rankings, map packs, search volume and seasonality, listings, reviews, backlinks, AI-assistant answers and a site crawl, plus direct checks (real-browser access, page weight, store shipping quotes, email DNS, mobile screenshots) — delivered as an owner-facing report with priced offers, an exact change list and a 12-week plan, optionally localized and bilingual. Use when "audit this business", "full marketing audit of <business>", "prospect audit", "local business audit", "use DataForSEO for the hard SEO data", "go deeper", "rerun it for local <market>", "also in Spanish", "give me the offers, changes and 3 month plan", or a business link arrives with "audit". For auditing your own product across channels use /kai-audit.
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。
Convert Kai workflow data, CSV exports, audit folders, SDR package outputs, and marketing reports into dashboard-ready specs or lightweight static dashboards. Use when "data dashboard", "operator dashboard", "operator room", "HTML operators room", "sales dashboard", "SDR dashboard", "turn this data into a dashboard", "dashboard handoff", "visualize Kai data", or any request to package sourced marketing, sales, audit, or SDR data for a dashboard or presentation surface.
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 高教社杯/CUMCM mathematical modeling work needs lightweight reusable paper assets, national-prize style paragraph templates, section controls, per-question code/plot/test scaffolds, evidence registries, paper fragments, reproduction scripts, or safe reuse of prior contest project structure without copying old problem-specific content.
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 高教社杯/CUMCM mathematical modeling papers need lightweight literature search, citation verification, method/background references, standards or policy sources, Chinese-English source routing, DOI/BibTeX checks, or claim-to-citation mapping without fabricated sources.
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。
Create polished single-file HTML artifacts for plans, reports, explainers, code reviews, lightweight dashboards, prototypes, and one-off editing tools. Use when the user asks for a standalone HTML file, single HTML, HTML artifact, interactive report, shareable local artifact, Alpine.js artifact, custom CSS artifact, copy/export UI, or a browser-openable deliverable instead of 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。
AFFiNE is an open source workspace that combines docs, whiteboards, and lightweight databases in one product. This skill is useful when an agent needs to structure notes, maintain linked knowledge, or automate content workflows around AFFiNE workspaces and self-hosted deployments.
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。