「labels」の検索結果

Compose multi-panel publication figures with patchwork, cowplot, gridExtra (R), or matplotlib GridSpec/subfigures (Python) including shared axes/legends/guides collection, panel labels in Nature/Cell convention, and journal-spec sizing. Covers patchwork ≥1.2.0 axes='collect' feature, Type-42 font embedding, and the cairo_pdf save path. Use when composing 2+ subpanels into a single figure for journal submission.

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。

Sets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's issue tracker (GitHub or local markdown), triage label vocabulary, and domain doc layout. Run before first use of `to-issues`, `to-prd`, `triage`, `diagnose`, `tdd`, `improve-codebase-architecture`, or `zoom-out` — or if those skills appear to be missing context about the issue tracker, triage labels, or domain docs.

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Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.

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Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.

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Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.

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Coordination guidance for the narrow case of multiple desktop-backed agents active concurrently in the same desktop-backed coordination context. Use ONLY when two or more desktop peers are running at the same time; normal CLI agents and single desktop peers should ignore this skill. Read this BEFORE concurrent desktop-to-desktop handoffs to keep sender labels unique, identity routing cooperative, and material handoffs proven.

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Load this skill whenever the project contains navigation components — primary navigation menus, dropdown menus, mega menus, breadcrumbs, pagination, mobile hamburger menus, or in-page jump navigation. Under no circumstances create navigation without proper landmark roles, keyboard support, and accessible labels. Absolutely always wrap navigation in <nav> with a unique aria-label.

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。

Load this skill whenever writing, editing, or reviewing any web content, copy, headings, labels, error messages, or instructions. Under no circumstances write inaccessible content — every piece of text must be clear, structured, and usable by people with cognitive, visual, and language differences. Absolutely always apply plain language, logical heading hierarchy, and meaningful link text.

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Load this skill whenever the project contains forms, inputs, selects, checkboxes, radio buttons, text areas, or any validation flow. Under no circumstances create a form without visible labels, error identification, and keyboard accessibility. Absolutely always associate every input with a label and provide clear, accessible error messages.

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。

Converts inconsistent job titles into standardized role, department, and seniority labels. Use when the user provides records with free-text titles and wants consistent role family, department, and seniority fields.

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Classifies professional profiles into buyer, influencer, user, decision-maker, founder, recruiter, or other personas. Use when the user provides profiles and wants persona labels with evidence and confidence. Must not infer sensitive personal attributes.

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 implementing typography in Apple-platform apps — SwiftUI text styles, hierarchy, readable sizes, monospaced data, custom fonts, labels, captions, and accessibility.

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Turn a wireframe and everything already agreed into a written spec in traditional agile shape — an initiative broken into epics, epics into user stories with acceptance criteria, and themes as the labels that span them — presented as an interactive drill-down tree the user prunes, edits and annotates in the browser, with a plain-markdown spec generated from it. Use when the user wants a spec, an initiative or epic broken into user stories, acceptance criteria, to formalise what a wireframe or mockup does, or to review and edit a spec visually.

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The catalog of every config-as-data surface in nuxt-crouton — which file controls what, who actually reads it, its drift hazards, and how to change it safely. Use when asking "where is X configured", "who reads this file", "what happens if I edit harness.config.mjs / routing.json / digests.yml / labels.yml / a deploy.config.json / an e2e.manifest.json", when a setting change had no effect (silent no-op), when adding a new config surface, or when a gen-*.mjs --check fails. Trigger phrases: "where do I configure", "what reads this config", "config drift", "the setting didn't do anything", "stale generated doc", "add a new config-as-data surface".

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。

Interactively build a Narrative identity graph workflow from one or more first-party datasets and (optionally) third-party data sources. Confirms each input dataset is mapped to the Rosetta Stone graph edge attribute (mapping it via /generate-rosetta-stone-mappings if not), then composes and submits a workflow that unions every edge source and labels connected components. Use when: "build an identity graph", "generate an identity graph", "create an identity graph", "stitch these datasets into a graph", "make a graph workflow", "label connected components on these datasets", "I want a person graph / household graph / device graph". (narrative-identity)

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Read the Free AI Credits directory — enumerate currently-claimable free AI credit offers, fetch per-offer terms and claim steps, and interpret the site's trust labels — using only static HTTPS GETs.

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Checks an extracted PFD register as a process model and returns findings and unknowns only: connectivity closure, stream numbering, presence of design conditions, equipment duty text, phase and service labels, package and off-sheet scope, boundaries, operating cases, and agreement between the drawing and attached process documents, then holds at gate G2. Creates no design values. Use when the user asks to "check the process model", "verify connectivity" or "is the PFD ready for enrichment" after gate G1 and before enrichment. Do not use for extraction from the drawing, use pfd-intake-and-extraction instead; do not use to propose lines or instruments, use piping-enrichment. Drafts for human review; never approves, authorises or signs off.

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。

Refresh WhatsApp text into an Obsidian vault (data layer, dashboard, group labels, contact notes). Text only, never downloads media, and pulls the recent window the live companion bridge exposes rather than a full multi-year archive. Semi-automatic by design: it drives the live bridge and needs the phone nearby. Triggers: "/whatsapp-sync", "pull whatsapp".

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。

Gmail skill for sending, reading, searching, labelling, and managing email via the gws CLI. Covers messages, threads, labels, drafts, filters, and inbox 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。

Analyze channel and campaign contribution using explicit attribution assumptions, reconciliation across systems, incrementality where possible, and confidence labels.

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。

Detect inconsistencies in ID formatting, date/time display, monetary amounts, and enum/status labels compared to existing patterns in the codebase.

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。

Analyze a script or existing edit, identify visual gaps, and generate supplemental B-roll coverage with clear parent and factual-status labels.

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。

Audits and fixes mobile accessibility across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform, including shared Compose UI and native hosts. Covers TalkBack/VoiceOver labels, roles, state, reading order, touch targets, text scaling, contrast and focus. Use for a11y audits, screen-reader bugs or accessible UI implementation.

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Scheduled health sweep that closes the factory loop - reads CI failures, recent commits, dependency and security advisories, and live queue labels, then files issues and writes an immutable run record. Use for the nightly or weekly monitor routine.

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。