1,190 件 ・ 関連度順
概要と使いどころ
When the user says 'refactor this', 'rename class', or 'move method'. Safely refactors code in any language — finds all callers, updates downstream dependencies, verifies via quality tools.
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。
[Code Quality] Use when you need restructure code without changing behavior using extract method, extract class, rename, move, and inline patterns.
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。
[Code Quality] Use when you need to perform a security review or audit on any scope — application code (OWASP Top 10 2025), secrets exposure, dependency/supply-chain malware, third-party repository vetting before install, infrastructure/config, CI/CD pipeline, AI-agent risks, and host/VPS compromise detection.
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。
[Code Quality] Use when reviewing UI/frontend changes for long-content overflow, responsive multi-screen layout (flex-wrap / row-to-column on small devices), flex-vs-fixed sizing, z-index discipline, SCSS/BEM styling quality, and async UI states & feedback (loading indicator, error surface, empty state).
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。
[Code Quality] Use when reviewing UI code for accessibility, responsiveness, performance, and UX best practices.
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。
[Code Quality] Use when reviewing rationale and change quality for plans, PBIs, commits, diffs, docs, specs, reports, or explicit artifacts.
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。
[Code Quality] Use when you need to prove fix correctness with adversarial code proof traces — a skeptic tries to DISPROVE the fix and trace all related paths first, with confidence scoring and stack-trace-style evidence chains, so confidence is earned by surviving attack, not by confirming it works.
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。
[Quality] Use when you need to research and configure code quality tooling for any tech stack — linters, formatters, static analysis, pre-commit hooks, and CI gates.
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。
[Code Quality] Use when you need to review artifact quality (PBI, user story, test spec, design spec) before handoff. Supports --type={pbi|story|spec-tests|design}.
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 reviewing code before it merges, whether written by an agent or a human.
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 raising code quality or test coverage across a .NET, Java, or Python repository.
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。
医学证据等级排序与编排层 skill - evidence grading orchestrator. 当用户请求"证据等级 / GRADE / 证据排序 / 医学证据评估 / 系统评价排序 / 临床证据排序 / 证据金字塔 / 给 PMID 打分 / Practice Guideline 优先 / Cochrane 优先 / 反向证据交叉验证 / evidence grading / quality of evidence / evidence pyramid / GRADE assessment / Cochrane review ranking / clinical evidence ranking"时,**调用本 skill 进行医学证据自动分级,自动选择最佳信息源**。本 skill 是编排层(L2)·不直接打 NCBI/Europe PMC/ClinicalTrials API,而是编排 6 个底层原子 skill(pubmed-eutils / europepmc-search / clinical-trials-v2 / aact-bulk-trials / bioc-fulltext-fetch / pubtator-entity-search)的召回结果,按证据金字塔(指南 > Meta > RCT > Cohort > Case-Control > Case Report)做 GRADE A/B/C/D 自动评级,自动剔除 editorial/letter/comment。优先使用 BioMCP MCP server 做 fan-out(genomoncology/biomcp);无 BioMCP 时走自建 slow-path 兜底。跨平台无锁(Claude Code / Codex / Gemini CLI)。
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 tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.
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 distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code 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。
Use when the deliverable is authored or edited prose whose wording matters (docs, READMEs, release notes, public copy) or when the user asks to tighten, de-AI, or review writing. Not for chat answers, code review, or explanations of current 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。
Use when work needs deeper thinking, less generic output, repo evidence, tradeoffs, monitoring signals, or stronger quality discipline.
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 after UI implementation for mechanical source checks and a rendered or fresh-eyes review.
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。
METAINFORMANT rules for directory src/metainformant/longread/quality. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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。
METAINFORMANT rules for directory scripts/quality. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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。
METAINFORMANT rules for directory examples/quality. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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。
METAINFORMANT rules for directory config/quality. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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。
METAINFORMANT rules for directory docs/quality. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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。
Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.
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。
Standard procedure for onboarding new software projects: Feature analysis, code quality review, onboarding checklist, and task creation.
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。