「bun」の検索結果

Think and work like an expert Computer Vision Scientist. Use when a task calls for Computer Vision Scientist judgment. Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train–test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class failure modes.

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。

Think and work like an expert Biological Oceanographer. Use when a task calls for Biological Oceanographer judgment. Reasons from light-nutrient-grazing coupling, the microbial loop, and size-structured export through CTD/MOCNESS sampling, 14C and O2/Ar production with 234Th export flux, imaging and flow cytometry enumeration, and SILVA/PR2 metabarcoding while treating spatial patchiness, diel-migration tow aliasing, CDOM-biased chlorophyll algorithms, and eDNA-detection-as-abundance as first-class failure modes.

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。

Think and work like an expert Biochemist. Use when a task calls for Biochemist judgment. Reasons from thermodynamics, enzyme mechanisms, and binding energetics; designs orthogonal purification and assay readouts while controlling oxidation, aggregation, coupled assays, and activity-vs-abundance confounds.

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。

Thesis report synthesis — turn a thesis's markdown artifacts into its letter-size thesis-report.html, then OPTIMIZE it against real page renders. Four-step loop — pack (deterministic Python bundle of all sources + report/metrics.json), author (the LLM writes report/content.html — narrative, KPI tiles, badges, timeline, tables and citations are LLM judgment), assemble (deterministic validation, template injection, Q50 pins, Q47 overflow gate), render + optimize (Chrome headless per-page PNGs — the LLM reads them and iterates until every page is visually clean). Spec 046 Q46–Q50.

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。

Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.

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。

Calculates species richness, diversity, and turnover using the Hill number framework with iNEXT coverage-based rarefaction/extrapolation, asymptotic diversity estimation, and beta diversity partitioning (betapart turnover vs nestedness). Compares assemblages using coverage-standardized rather than size-standardized rarefaction. Use when quantifying biodiversity from species abundance or incidence data, comparing diversity across sites, or constructing rarefaction curves. Not for clinical 16S microbiome alpha/beta diversity (see microbiome/diversity-analysis).

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。

Workflow for amplicon microbiome analysis including denoising, taxonomy assignment, diversity analysis, and differential abundance.

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。

Render Markdown, HTML, or PDF paid-advertising reports from a validated Claude Ads JSON run bundle. Use for ads report, client report, audit PDF, executive audience reporting, or exporting prior audit and plan results.

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。

Consolidated Galyarder Framework Product intelligence bundle.

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 preparing a Formax code handoff: selecting files, generating repomix bundles, and writing a high-quality prompt for WebGPT or another coding agent with clear constraints and validation scope.

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。

Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.

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 when the user says "/council", "council", "deliberate", "multi-perspective decision", "트레이드오프 분석", "위원회 소집", "여러 관점으로 검토", or wants deep multi-perspective deliberation with tradeoff mapping. Combines tribunal (structured adversarial review), agent-council (external LLM opinions), dev-scan (community sentiment), and step-back (meta-level review) into a unified decision-making committee. Uses Agent Teams for real peer-to-peer debate with iterative step-back moderation loop.

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。

Babylon.js is an open-source 3D engine for the browser that renders with WebGL or WebGPU and includes a scene graph, PBR materials, glTF loading, Havok physics, a GUI layer and WebXR. Use when a user asks to build a 3D scene, game, product configurator or VR/AR experience with Babylon.js, load a glTF/GLB model, add physics or in-scene UI, switch to WebGPU, or shrink a Babylon.js bundle.

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。

World-class application logging - structured logs, correlation IDs, log aggregation, and the battle scars from debugging production without proper logsUse when "log, logging, logger, debug, trace, audit, structured log, correlation id, request id, log level, winston, pino, bunyan, log4j, logging, observability, debugging, monitoring, tracing, structured-logs, correlation, aggregation" mentioned.

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。

World-class frontend engineering - React philosophy, performance, accessibility, and production-grade interfacesUse when "frontend, react, vue, svelte, next.js, nuxt, component, state management, redux, zustand, client side, spa, ssr, hydration, bundle size, web vitals, accessibility, a11y, responsive, css, tailwind, frontend, react, typescript, performance, accessibility, components, state, architecture" mentioned.

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。

Query visual scientific figure references, show candidates for human preference selection, export selected reference bundles, and guide plotting agents from human-selected visual examples to code 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。

Angular Best Practices workflow skill. Use this skill when the user needs Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Angular Best Practices workflow skill. Use this skill when the user needs Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

AI/ML Workflow Bundle workflow skill. Use this skill when the user needs AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

AI/ML Workflow Bundle workflow skill. Use this skill when the user needs AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Triage the deferred-work ledger for the bmad-loop orchestrator: verify every selected open entry against the actual codebase and return a machine-readable partition (bundles, already-resolved, blocked, skip, human decisions). Also migrates legacy pre-DW-format ledgers when invoked with --migrate. Automation-only — invoked by bmad-loop sweep runs, not by humans.

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。

Exports a Claude.ai workspace — its custom instructions and its uploaded knowledge files — into a bundle that can be re-imported elsewhere. Use for "back up my project instructions", "export my project knowledge", "copy this workspace to a new one", "migrate my project to another account", or "recreate this setup somewhere else". Operates on the Claude.ai workspace you are talking in, and reads nothing from disk.

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 an opt-in deep codebase audit with persisted findings. Confirm cost before dispatch; filing and telemetry need separate approval.

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 an opt-in deep codebase audit with persisted findings. Confirm cost before dispatch; filing and telemetry need separate approval.

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。