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Uninstall or hard-clean OpenClaw Companion, Windows node, native Gateway/MXC, WSL Gateway, and managed llama.cpp/Local AI state for a clean retest. Choose the existing dev CLI uninstall or the reviewed hard-clean procedure, inventory exact targets, and obtain destructive confirmation before acting.

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 MCP Apps with interactive React UIs using the Leanmcp UI SDK. Use this skill when users ask for "MCP with UI", "MCP App", "interactive MCP", "leanmcp UI", "MCP dashboard", or want to create rich visual interfaces for their MCP tools. This skill covers @UIApp decorator, React components with useTool/useResource hooks, ToolDataGrid, ToolForm, ToolButton, and ChatGPT Apps support.

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 cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data fo...

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 working with Lean 4 (.lean files), writing mathematical proofs, seeing "failed to synthesize instance" errors, managing sorry/axiom elimination, or searching mathlib for lemmas - provides build-first workflow, haveI/letI patterns, compiler-guided repair, and LSP integration

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You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.

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Consolidates redundant documentation while preserving all valuable content; keeps a doc set truthful after things change. Modes: (1) POST-CHANGE GOVERNANCE — when code, scripts, config, env vars, ports, paths, deployment, auth, tests, or a documented procedure changed; also "which docs are now wrong", "check docs for inconsistencies", "过时的命令/路径", "文档同步". Use it even when the user mentions only the change and never the docs — updating them is part of the change, not optional cleanup. (2) CONSOLIDATION — "clean up docs", "documentation bloat", "reduce documentation sprawl", "consolidate documentation", "too many doc files", "merge redundant docs", "merge these docs", "文档太多/太乱", multiple files covering the same topic, or documentation exceeding 500 lines across multiple files on similar topics. Finds stale copies of a fact changed in only one place, values that should never have been persisted (counts, totals, "N items", restated summaries), rival definitions that drift apart, and orphaned cross-references.

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Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. Invoke it on your own initiative the moment you finish writing, editing, or refactoring non-trivial production code, before presenting or committing — don't wait to be asked. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-guard).

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 control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowClean), margin/boundary event removal, signal-drift detection, dead-cell exclusion, CyTOF Gaussian/DNA/event-length checks, instrument calibration/standardization (MESF, CS&T, peak-2), and batch-level outlier flagging. Use when assessing acquisition quality, choosing a cleaning tool, ordering QC relative to compensation, deciding margin removal before density-based steps, or flagging problematic samples before clustering or differential analysis.

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This skill should be used when user asks to "clean gone branches", "remove deleted local branches", "prune branches removed from remote", or explicitly invokes "clean-gone-branches".

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 has finished building a mobile app, started Metro with `npm run dev`, and wants the running app monitored for runtime errors AND silent failures (empty lists, blank screens, swallowed network errors) and fixed autonomously. Accepts a free-text symptom (e.g., `/debug-app "todos not appearing on home screen"`) to drive persisted-log diagnostics — injects temporary console.log statements at data-path boundaries, reads the sanitized `.powernative/metro-logs/` log, and cleans up logs after the root cause is fixed. Supports port/platform filtering, configurable clean-cycle and timeout limits, and watch-only `--no-fix` mode. Otherwise polls with a durable byte cursor, fixes inline or routes appropriately, verifies each fix from new output, and exits after the configured clean checks (default 3). Foreground loop — blocks the conversation while running. Run only after the app is loaded.

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Use when modeling hard-surface objects in Blender (sci-fi panels, props, weapons, vehicles, robots, machined or product parts) with booleans and cutters, bevel modifiers, weighted normals, or subdivision with support loops or creases. Also when shading is broken (smears on flat faces, stretching near a cut, pinching), a bevel shrinks or vanishes after a boolean, a boolean empties the mesh or leaves holes, choosing n-gons vs quads, or making a game-ready mid-poly.

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SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontamin

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Turns the raw company-name string on a lead row into the short human form a person would say out loud, so it can be dropped into email copy. Triggers on "clean these company names", "company name variable", "strip the LLC and Inc", "the company names look robotic", "normalize company names for the campaign". Outputs company_clean, one short string per row.

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 the raw first-name field on a lead row into the name a person would actually be greeted by, so it can open an email. Triggers on "clean these first names", "first name variable", "the greeting says Hi DR MATTHEW", "strip the titles off the names", "some rows have the whole name in the first-name column", "normalize first names for the campaign". Outputs first_name_clean, one short string per row.

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。

Organize messy conference LaTeX template .zip files into clean Overleaf-ready structure. Extracts, analyzes, cleans up, and generates README with submission requirements. Triggers on "organize LaTeX template", "clean up template", "prepare Overleaf template", "整理LaTeX模板".

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 Microsoft Orleans systems from each primitive's purpose and failure model. USE FOR: grains, digital twins, state versus databases, transactions, messaging, streams, timers, reminders, Durable Jobs, stateless workers, grain services, startup, and hosting. DO NOT USE FOR: other actor stacks, batches, relational-only CRUD, or advice without an Orleans decision. INVOKES: inspect version and topology, choose the primitive, implement, and validate.

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Removes unwanted visual elements from videos using AI-powered inpainting via Volcengine LAS. Video watermark removal, subtitle removal, logo removal, and text overlay removal — erases and cleans up watermarks, hardcoded subtitles, logos, text, and other fixed-region artifacts and obstructions from video footage. Video inpainting, video repair, video restoration, and video cleanup. Supports specifying exact bounding boxes for targeted removal and erasure. Handles videos up to 4 hours and 30GB. Use this skill when the user wants to remove watermarks from videos, erase hardcoded subtitles/captions, delete logo or text overlays, clean up video footage by removing unwanted objects, repair or restore videos by removing obstructions, do video inpainting on fixed regions, or remove visual artifacts from video content.

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Pull request lifecycle: commit, codex review, sync, review, fix, status, cleanup, and PR mining. Use when user wants to commit changes, get a second-opinion code review from Codex, push changes, create a PR, check PR status, fix review comments, clean up branches after merge, or mine tribal knowledge from PR reviews. Use for "commit my changes", "codex review", "push my changes", "create a PR", "pr status", "fix PR comments", "clean up branches", "mine PRs", or "address feedback".

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 performing maintenance or diagnostics on remote Linux systems. Triggers on "remote server", "Linux maintenance", "Ubuntu cleanup", "Debian", "disk space", "apt cleanup", "journal vacuum", "snap cleanup", "system diagnostics". Provides structured three-phase checklists with quantification.

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 up after (or abandon) a Rails upgrade. Drop NextRails.next? and NextRails.current? branches and retire dual-boot scaffolding (Gemfile.next, Gemfile.next.lock, conditional Gemfile groups), keeping either the next or the current version. Trigger when the user says they are done with the upgrade, want to clean up dual-boot, want to drop NextRails branches, want to finish the upgrade, want to abandon or revert the upgrade attempt, want to roll back to the current Rails version, or want to pause this upgrade hop. Based on FastRuby.io's "Finishing an Upgrade" methodology, extended with an abandon/pause path.

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 a local FF&E CSV schedule by normalizing casing, dimensions, units, language, materials, and formatting. Use when asked to clean, fix, or standardize product 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。

Cleans up CRM data from HubSpot, Salesforce, Pipedrive, Zoho, Close, Attio, or any CRM CSV export. Finds duplicate contacts and companies with fuzzy matching (not just exact email), normalizes emails, phones to E.164, names, domains, states, and countries, flags stale, invalid, role, and test records, finds broken contact-company associations, and produces a reviewable merge plan plus import-ready files without touching the live CRM. Use this skill whenever the user wants to dedupe, find or merge duplicate contacts, merge companies or accounts, clean up a CRM, prep a CRM migration or re-import, or says the CRM export is a mess. Also use it when they mention only one symptom, like "the same person is in here three times", "half our leads are junk", or "our company records don't match".

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 preprocess datasets by handling missing values, removing duplicates, correcting types, resolving outliers, and enforcing validation schemas. Use when the user requests data cleaning or provides relevant inputs for this workflow.

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 or review lean canvas deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for lean canvas planning or 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。