「porting」の検索結果

Runs several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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。

Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

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 topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.

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。

Audit a methods section against a 45-item checklist drawn from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT. Use before submission or archiving, or to document a pre-analysis-plan deviation.

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。

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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。

Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy 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。

Guide for using and supporting the AGENTS.md standard in VS Code. Use this when asked about AGENTS.md, custom instructions, or repo-level AI agent configuration.

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。

Intelligent financial management skill for Claude Code that provides comprehensive PocketSmith API integration with AI-powered analysis, transaction categorization, rule management, tax intelligence, and scenario planning. Use when working with PocketSmith data for (1) Transaction categorization and rule management, (2) Financial analysis and reporting, (3) Australian tax compliance (ATO) and deduction tracking, (4) Scenario analysis and forecasting, (5) PocketSmith setup health checks, (6) Budget optimization and spending insights.

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 operating ProjectClownfish cluster workflows in this repo: reviewing GitHub Actions run artifacts, tuning worker/applicator guardrails, importing ghcrawl clusters, dispatching canaries, scaling batches, or deciding whether autonomous write-mode cluster cleanup is safe to ramp.

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。

Safely close out completed Incan work after a PR is merged by verifying merge status, syncing the base branch, removing task-owned local worktrees/assets, deleting merged local/remote branches, pruning refs, and reporting dirty or ambiguous leftovers. Use when the user says /closeout, asks to clean up after a merged PR, or wants local RFC/issue branch/worktree cleanup.

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。

发现缺陷需要记录时使用。适用于功能测试、回归测试、探索式测试发现的所有缺陷。融合 Cem Kaner 缺陷报告、ISTQB 缺陷生命周期、严重度优先级矩阵。

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。

测试覆盖率收集和报告时使用。适用于覆盖率目标设定、CI 集成、PR 评论、趋势跟踪。

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。

Migrates Odoo QWeb HTML and PDF reports between major versions while preserving data semantics, access behavior, paper geometry, and intended visual appearance. Use when porting reports across Odoo versions, adapting report templates or assets after an upgrade, or diagnosing visual regressions in migrated PDFs.

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-knowledge for stella, the self-hosted AI assistant. Use when users ask about configuration, setup, onboarding, providers, models, agents, channels, webhooks, memory, schedules, workflows, goals, workers, skills, plugins, session compaction, notifications, self-update, multi-user behavior, or how stella works. Also use for changing models, setting up channels or webhooks, configuring providers, updating stella, onboarding, switching agents, reporting bugs, and goal status or acceptance.

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 before committing, opening a PR, or reporting a task done — when you want ground-truth confirmation there are no conflict markers, live-looking credentials, debug leftovers, untracked TODOs, or oversized files, without spending a model turn re-reading every file yourself.

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 before publishing documentation, merging a docs PR, or reporting a Markdown-heavy repo's navigation as complete — when you want ground-truth confirmation that internal links, heading anchors, and directory links resolve, without opening every file yourself.

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 GitHub Backup when an agent needs to mirror repositories, release assets, and gists into local storage on a schedule, instead of manually exporting GitHub content repo by repo.

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。

audioFlux is a deep learning tool library for audio and music analysis and feature extraction, supporting dozens of time-frequency transforms and hundreds of feature combinations for classification, separation, MIR, and ASR tasks.

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。

CRISP Execute — Sprint execution loop with change request management, security gates, product gates, and stakeholder reporting. Use after Phase S (Spec) is complete and crisp-state.json shows ready_for_execute: true. Triggers on "start sprint", "begin build", "execute", "sprint 1", "run sprints", or when handed off from crisp-orchestrator.

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。

Blender MCP expert for scene inspection, Python scripting, GLTF export, and material/animation extraction. Activate when: (1) using Blender MCP tools (get_scene_info, execute_python, screenshot, etc.), (2) writing Blender Python scripts for extraction or manipulation, (3) exporting scenes to GLTF/GLB for web (Three.js, R3F), (4) debugging material or texture export losses, (5) optimizing GLB files with gltf-transform, (6) using asset integrations (PolyHaven, Sketchfab, Hyper3D Rodin, Hunyuan3D). Covers critical export gotchas, material mapping survival, texture optimization pipeline, headless CLI patterns, and known 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。

Use when the user asks to create a custom post type, build a portfolio, set up case studies, add team members, events, or podcast episodes, or says 'scaffold a CPT'. Creates the post type plus supporting taxonomies, an ACF field group, sample entries, and a builder-specific single template suggestion.

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。

Add or edit junior.guru interview stories and participant photos under src/jg/coop/web/docs/stories and src/jg/coop/images/avatars-participants. Use for importing an interview from Google Docs, formatting story Markdown, or preparing its portrait.

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。