「quality」の検索結果

Suede Labs agent-team orchestrator: split complex work into coordinated lanes with explicit file ownership, WIP collision detection, quality gates, escalation thresholds, rollback plans, and handoffs that prove what shipped. Use when one shared change needs safe parallel ownership across builders and reviewers, when a lane map must be resolved before anyone opens a file, or when running a repeatable public-repository contribution program with issue scoring, atomic task leases, isolated worktrees, and explicit publication authority. NOT FOR: one repo change a bundled DAG can run end to end (use suede-graph-flo-xr); findings-only review of a diff (use suede-code-review) or an A-F ship grade (use suede-code-grader); CI, branch protection, or merge-gate wiring (use suede-ci-gate); branch and worktree setup on a stale mirror (a private Suede Labs companion, not in this pack).

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QA test a live website with Firecrawl browser and scrape evidence. Use when the user wants exploratory QA, form testing, navigation/link checks, responsive checks, performance observations, bug reports, or a pre-launch quality 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。

Review UI designs and implementations for accessibility, consistency, usability, and visual quality. Use when asked to review a design, audit accessibility, check UI consistency, compare implementation against mockups, or evaluate a user interface.

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。

Review code for quality, security, performance, and maintainability. Use when reviewing PRs, auditing a codebase, or refactoring. Covers TypeScript, Node.js, infrastructure-as-code, and full-stack web apps.

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, review, and improve skills for Pi agents. A skill is a folder with a SKILL.md that teaches an agent specialized workflows, domain knowledge, or tool integrations. Use when asked to create a new skill, improve an existing skill, review a skill for quality, scaffold a skill from a workflow, or convert documentation into a skill. Also triggers on "make a skill for", "build a skill", "skill for [topic]", "teach the agent to", or "package this workflow as a skill".

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。

Perform a comprehensive DRY (Don't Repeat Yourself) code review on a codebase. Identifies duplicated code, repeated logic, redundant patterns, and opportunities for abstraction. Use this skill whenever the user asks to review code for duplication, reduce repetition, apply DRY principles, refactor for reusability, find copy-pasted code, deduplicate logic, or audit code quality with a focus on redundancy. Also trigger when users say things like "clean up my code", "find repeated patterns", "too much boilerplate", "reduce code duplication", or "refactor for maintainability". Works on any language or framework.

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。

Review code for quality, correctness, and fit. Use when the user wants judgment on code that already exists — their own changes, a teammate's patch, a PR, branch, commit, diff, staged changes, or one or more files to look over. Activate on requests like review, look over, sanity check, critique, code review, or 'is this good?' The key signal is that the user wants evaluation of existing code and its tradeoffs, not implementation, debugging, or explanation. This skill works independently, but when plans, specs, task artifacts, or prior discussion exist, use them to understand why the code exists before judging it.

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 wants to know if something works — the answer requires running code, not analyzing it. Output is a verdict backed by evidence: passed, failed, or broken. Primary triggers: 'run the tests', 'does X still work after my change?', 'did the merge break anything?', 'verify the fix worked', 'check if the endpoint returns X', 'confirm nothing regressed', 'run tests/unit/test_foo.py', 'let me know the results', 'make sure my changes didn't break anything'. Hard stops — do NOT use for: reviewing test code for quality/coverage gaps, debugging why test infrastructure/databases/seed scripts are misbehaving, writing or fixing tests, diagnosing root causes of unexpected behavior. The deciding question: is the user asking for the result of executing something, or asking for help understanding/analyzing/improving something? If it's the latter, use diagnose or review-code instead.

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。

The single config quality skill. Two modes: (1) Default — review/wire pending self-improvement proposals. (2) Audit — analyzes skills, rules, CLAUDE.md, project refs, hooks, and agents against the actual codebase to find inaccuracies, trigger overlaps, stale references, and weak rules, then fixes them. Trigger on 'self-evolve', 'evolve', 'config health check', 'audit config', 'check claude setup', 'apply this rule', 'reorganize rules', 'ok' (when proposals are pending). NOT for improving individual skills (use skill-creator), diagnosing code bugs (use diagnose), or fixing broken code (use fix).

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Comprehensive clinical laboratory data harmonization for multi-source healthcare analytics. Convert between US conventional and SI units, standardize numeric formats, and clean data quality issues. This skill should be used when you need to harmonize lab values from different sources, convert units for clinical analysis, fix formatting inconsistencies (scientific notation, decimal separators, whitespace), or prepare lab panels for research.

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 a model is served online (FastAPI, BentoML) or scored in batch, when changing how a model artifact is loaded or versioned, or when adding drift, quality or prediction monitoring and a rollout plan for a new model

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Use when adding or changing a scheduled data or ML pipeline — Dagster assets, Prefect flows, Airflow DAGs or a cron'd entrypoint — idempotent partitioned stages, retries, backfills, quality gates, and testing the pipeline without the scheduler

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Use when writing tests for data or ML code — what to test at each layer (transforms, schemas, properties, pipeline smoke, model-quality thresholds, determinism and parity), how to build small synthetic fixtures, and what not to test

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Use when you are about to hand off any number, table, chart or analysis someone will act on (a metric query, a dbt mart, a report, an experiment readout) — the pre-delivery checklist for data quality, calculation, reasonableness and presentation, and the pitfalls that produce plausible wrong numbers

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 code reviewer. Analyze PR changes for correctness, security, performance, and quality. Returns findings as JSON. CRITICAL: this skill is costly, don't use it unless user explicitly requested to use it.

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Review code changes for correctness, security, performance, and code quality. Use when the user asks to review a diff, review code changes, review commits, or perform a code review. Input can be: (1) a text diff pasted directly, (2) one or more git commit hashes to extract the diff from, or (3) a git range like abc123..def456. The user may also provide task description or requirements that motivated the change.

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 this skill on every programming task — writing code, fixing bugs, refactoring, explaining code, running tools, or answering technical questions. Apply token-saving and quality-preserving practices throughout. Activate even when the user doesn't explicitly ask for efficiency — this skill is always relevant for software development work. Do not skip this skill for "simple" tasks; the rules apply at every 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。

Apply senior software engineering standards when writing, editing, or refactoring any code. Use this skill whenever the user asks to write a function, build a feature, fix a bug, design a module, or improve existing code — even if they don't mention quality explicitly. The goal is production-grade code: readable, correct, maintainable, secure, and failure-aware. Always active for any coding task, at any 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。

Encode Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great. From the creator of Sonner (13M+ weekly npm downloads), Vaul, animations.dev, and Linear's web team. Use when user wants to polish UI, audit animations, review component interactions, add micro-feedback, or elevate motion 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。

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web 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。

Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk 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。

Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.

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Plan, art-direct, generate, edit, and quality-check tactile minimal-zine bitmap imagery from almost any subject: landscapes, portraits, objects, products, architecture, interiors, existing photographs, moods, article ideas, cultural essays, data, reviews, and social-media card series. Use for 小红书图片, Instagram carousels, poetic posters, editorial covers, photo restyling, visual essays, evidence-aware explainers, or any request for aged paper, photocopy, risograph, letterpress, sparse type, and restrained single-ink character. Routes the subject before choosing composition, preserves identity and factual content when editing, saves reproducible prompts, invokes the runtime-native image generator, and audits the real raster output. Do not use for glossy commercial ads, deterministic UI/vector assets, or exact diagram rendering.

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。

Inspect and diagnose minimal-zine posters, photo restyles, editorial images, and social-card series for subject preservation, factual and text integrity, composition, paper realism, print-process coherence, color signal, anti-AI craft, platform fit, and series continuity. Use when the user asks whether an image has高级感, feels AI-generated, matches Paper Signal or gc-minimal-zine quality, needs improvement, or should be regenerated. Returns evidence-based findings and a targeted correction prompt; regenerates only when the user also asks for a fix.

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。