「cleaning」の検索結果

Cleaning up the skill ecosystem: auditing the agent roster for overlap and inactivity, proposing merges and sunset plans. Propose-only. Not for ecosystem strategy (Darwin) or code YAGNI (Void).

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Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.

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。

Data governance strategy, quality validation rules, and data dictionary management for vehicle insurance platform. Use when defining data quality standards, implementing validation rules, managing field mappings, resolving data conflicts, or establishing data governance processes. Covers data cleaning standards, quality metrics, and mapping 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。

Clean and standardize vehicle insurance CSV/Excel data. Use when handling missing values, fixing data formats, removing duplicates, or standardizing fields. Mentions "clean data", "handle nulls", "standardize", "duplicates", or "normalize".

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。

Process vehicle insurance Excel data using Pandas - file handling, data cleaning, merging, validation. Use when processing Excel/CSV files, handling data imports, implementing business rules (negative premiums, zero commissions), debugging data pipelines, or optimizing Pandas performance. Keywords: data_processor.py, Excel, CSV, Pandas, merge, deduplication, date normalization.

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 data cleaning and transformation pipeline — missing values, outliers, and deduplication. Use when asked to "clean this dataset", "handle missing values", or "deduplicate this data".

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 refactoring patterns and techniques for improving code quality without changing behavior. Use for cleaning up legacy code, reducing complexity, or improving maintainability.

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。

Reviews an existing conjoint study for threats to inference and returns severity-ranked findings on design integrity, estimation, measurement error, external validity, and interpretation, including the guardrail against reading an AMCE as majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation or interpretation. New designs go to conjoint-design, reshaping to conjoint-cleaning.

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。

Turns a raw Qualtrics conjoint export into an analysis-ready long-format dataset, covering export settings, identifying the implementation, package or manual wide-to-long reshaping, choice-profile alignment, ratings, reference categories, pilot exclusion, and subgroup merges. Use when the user has a conjoint CSV to clean, reshape, or debug, cannot align choices with profiles, or asks what the data must look like before AMCE estimation. Design goes to conjoint-design, validity review to conjoint-diagnostics.

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 auditing CRM data quality, finding duplicate records, identifying stale leads, cleaning pipeline data, or improving data hygiene. Triggers: 'clean my CRM', 'audit CRM data', 'find duplicates', 'CRM hygiene', 'data quality check', 'stale leads', 'pipeline 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。

Icarus Figures produces publication-grade figures for scientific papers — reproducible matplotlib/seaborn data figures and original TikZ method/framework/architecture figures, held to a four-axis quality bar (depth · elegance · unimpeachable · visible-gap) and exported as editable PDF+PNG+SVG with a runnable critique gate. Use this whenever a figure is destined for a paper, journal, conference submission, thesis, or report — making a plot/chart publication- or journal-ready, building a method/architecture/framework diagram or 技术路线图 (the hero figure a paper opens with), a schematic or workflow figure, or cleaning up a matplotlib/seaborn/TikZ figure that currently looks like a homework plot. Trigger even when the user never says "publication": if the figure goes into a written scientific document, this skill applies.

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。

Read a CONTINUE_HERE.md continuation file at session start and resume from where the prior session left off. Covers detecting the file, assessing freshness, parsing the structured handoff, confirming the resumption plan with the user, and cleaning up after consumption. Optionally configures a SessionStart hook and CLAUDE.md instruction for automatic pickup. Use at the start of a session when a continuation file exists, when bootstrapping after an interrupted session, or when setting up automatic continuation 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。

Purify water from wild sources using boiling, filtration, and chemical methods. Covers source assessment and priority ranking, sediment pre-filtering, method selection (boiling, chemical, UV, filter), altitude-adjusted boiling procedure, chemical treatment dosages, and safe storage practices. Use when needing drinking water in a wilderness setting, when available water sources are of unknown quality, in an emergency survival situation where dehydration is a risk, or when making water safe for cooking or wound cleaning.

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。

Maintain the 8 essential garden hand tools through sharpening, handle care, rust prevention, and seasonal storage. Covers bypass secateurs, hori-hori, hand fork, trowel, pruning saw, sharpening stone, watering can, and soil rake. Use after each garden session for quick cleaning, monthly during the growing season for sharpening and oiling, at end of season for winter storage preparation, before spring for pre-season readiness checks, or whenever a tool feels dull, stiff, or shows rust.

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, track, switch, sync, and clean up Git branches. Covers naming conventions, safe branch switching with stash, upstream synchronization, and pruning merged branches. Use when starting work on a new feature or bug fix, switching between tasks on different branches, keeping a feature branch up to date with main, or cleaning up branches after merging pull requests.

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 writing, editing, cleaning, or refactoring Tailwind classes, components, or configuration. Not for read-only audits of Tailwind code, or general CSS without Tailwind.

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。

Build AI data cleaning systems. TRIGGERS - Use when user needs help with ai-data-cleaner related 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。

数据清洗师（净凡）。专注于数据质量评估、清洗、标准化和预处理，确保数据可靠可用。

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 when users want to build, edit, or design pages and layouts on Divi 5 WordPress sites. Triggers include creating landing pages, hero sections, testimonial cards, pricing tables, feature grids, blog listing pages with post loops and pagination; adding or modifying sections on existing pages by page ID; setting up design systems with tokens and presets; theme builder templates for headers, footers, and post/page body layouts; mega menus and site navigation; saving to or loading from the Divi library; auditing or cleaning up presets. Handles animations, hover effects, responsive design, and dynamic content. Also covers special effects like WebGL shader backgrounds and advanced CSS animations via the DiviOps Design Library plugin. Do NOT use for custom PHP/plugins, child theme development, standalone CSS authoring, SQL queries, WooCommerce configuration, SEO setup, standalone Three.js projects without WordPress, or non-Divi builders like Elementor.

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。

Deletes merged git branches (local and remote) and flags stale unmerged branches for manual review. Use when user mentions "cleanup branches", "delete merged branches", "prune old branches", "remove stale branches", "branch cleanup", or runs /cleanup-branches command.

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 for hands-on Pulumi CLI work: running deployments, fixing broken stacks, and managing infrastructure state. Handles: recovering from stuck or interrupted `pulumi up` with pending operations, cleaning orphaned resources from state after out-of-band cloud deletions, protecting critical resources from accidental `pulumi destroy`, moving resources between stacks without recreating them, targeting specific resources during deployment, migrating between backends (local file to Pulumi Cloud, S3), stack lifecycle management, state export/import/repair, CI/CD pipeline setup, and importing existing cloud resources. Use this skill — not the language-specific Pulumi skills — whenever the user's question is about operating, troubleshooting, or recovering Pulumi infrastructure rather than writing program code.

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 creating or managing picklist fields: choosing between Global Value Sets and object-local picklists, configuring controlling and dependent field relationships, managing picklist values, and replacing values in existing data records. NOT for why the API or an integration ignores a dependency matrix - use admin/field-dependency-and-controlling. NOT for cleaning up degraded, unrestricted or orphaned picklist values - use admin/picklist-field-integrity-issues. NOT for record type picklist filtering - use admin/record-types-and-page-layouts. Keywords: GlobalValueSet, StandardValueSet, __gvs suffix, customValue, valueSetName, valueSetDefinition, restricted picklist, GlobalValueSetTranslation, OpportunityStage, CaseStatus.

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 designing, reviewing, or troubleshooting Salesforce duplicate prevention, matching rules, duplicate rules, and merge governance. Covers cleaning up duplicates that already exist at ordinary volume: merge sequencing, survivorship, stewardship ownership. Also covers the deployable MatchingRule / DuplicateRule metadata, the Alert-vs-Block action pair, DuplicateRecordSet reporting, and the Apex DMLOptions.DuplicateRuleHeader.allowSave bypass. Triggers: 'duplicate rule', 'matching rule', 'merge strategy', 'dedupe', 'survivorship', 'D&B', 'same contact twice', 'clean up duplicate records', 'clean up duplicate accounts', 'DUPLICATES_DETECTED', 'allowSave', 'FindDuplicates'. NOT for cleanup at volume — hundreds of thousands of records, batch merge jobs, DemandTools/Cloudingo — use data/large-scale-deduplication. NOT for which field values and child records the platform keeps in a merge - use data/record-merge-implications.

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 linters and formatters to fix code style issues. Use when cleaning up style violations, formatting code, or after implementation to ensure code meets project standards.

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。