「labels」の検索結果

Use this when creating, modifying, reviewing, or saving charts, figures, plots, or visual summaries — including matplotlib/seaborn code, EDA figures, report figures, dashboards, or any task involving Japanese chart labels, color palettes, or figure styling.

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Tags harness-decomposed curriculum items (KUDs, LTs, criteria) with a school's developmental band metadata while preserving source voice and labels. Supply the band schema; the skill derives mapping rules from it.

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Nexscope Ozon product report search: filters Ozon products by multi-dimensional metrics including sales, revenue, sales/revenue growth rate, cart conversion rate, order conversion rate, price, rating, review count, QA count, variant count, page views, gross margin, return/cancellation rate, ad spend share, weight/volume, listing time, brand, seller, fulfillment method, and labels. Returns each product's SKU, title, price (RUB), sales, revenue, lost revenue, conversion rate, rating, reviews, brand, seller, fulfillment method, listing days/months, and complete product report fields. Use for Ozon product selection, competitor product analysis, best-seller mining, price/conversion band filtering. Trigger when the user mentions Ozon product report, Ozon product selection, Ozon product filtering, Ozon product analysis, Ozon best-selling products, Ozon competitor product analysis, Ozon product report, Ozon product screener, filter Ozon products by sales, Ozon best-seller mining, Nexscope Ozon product report. Also trigger when the intent is to filter Ozon products by multiple metrics and view product-level reports, even without explicitly mentioning Nexscope.

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Returns the frozen gate constants — question, header, option labels and descriptions, digest field-list, and preview content — for every catalog gate. Invoked by the team lead fresh on arrival at each gate and on pulse re-emissions, in the same turn as the render. The transport contract, three renders, partition rule, and authoring rubric live in swarm:workflow-rules.

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React Flow (@xyflow/react) for workflow visualization with custom nodes and edges. Use when building graph visualizations, creating custom workflow nodes, implementing edge labels, or controlling viewport. Triggers on ReactFlow, @xyflow/react, Handle, NodeProps, EdgeProps, useReactFlow, fitView.

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Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.

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。

Interactive demo: Generate pseudo-labels from a strong teacher model, build a cheap student function, and evaluate accuracy. Showcases pseudo-labeling and teacher-student distillation.

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EasyPost — shipping labels, rate comparison, package tracking, address verification, and insurance.

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Audit UX copy in a product or codebase — find passive errors, generic labels, missing states. Use when asked to "audit our UX copy", "our error messages are bad", or "review the microcopy".

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Write accessibility fixes for specific WCAG failures — ARIA, focus management, keyboard patterns. Use when asked to "fix this accessibility issue", "add ARIA labels", or "make this keyboard navigable".

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Show where the user is and what their actions affect — breadcrumbs, regions, scope labels. Use when designing deep navigation or multi-section layouts.

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。

Creates GitHub Pull Requests with automated validation and task tracking. Use when user wants to create PR, open pull request, submit for review, or check if ready for PR. Analyzes commits, validates task completion, generates Conventional Commits title and description, suggests labels. NOTE - for merging existing PRs, use github-pr-merge instead.

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Extract text visible in an image (OCR) with a local vision LLM. Wraps kuzco's tool_llm_image_extract_text() ellmer tool. Use when the task needs reading text from screenshots, documents, signs, labels, or any image containing writing.

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。

Guides writing and editing Mermaid diagrams in GitHub-rendered Markdown so they remain readable across themes and preserve placeholder labels.

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Describe images, screenshots, diagrams and charts from visual inspection. Preserve visible text, counts, labels, directions and uncertainty; distinguish observation from interpretation. Use for image summaries, screenshot explanations and diagram descriptions.

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Transitions a GitHub milestone from planning to active execution. Use when the team is ready to begin a sprint or release cycle — bulk-transitions open issue labels from status:needs-grooming to status:in-progress, updates GitHub Projects V2 Status to In Progress, and confirms before applying changes. Requires milestone number as argument. Use after /group-items-to-milestone.

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Use this skill when a human needs to review a Deepline Play result and hand feedback, labels, or approval back to the agent for revision, evaluation, comparison, or bounded iteration. Triggers on “put this run in a Sheet,” “review these results,” “read my feedback,” “make this a standing rule,” “never regress on this case,” “compare these runs,” “keep improving,” or “add the revised run to the same spreadsheet.” Skip ordinary Play authoring or execution with no human review loop, generic spreadsheet work, and one-off CSV export.

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Runs several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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Runs several language models as independent coders on one labeling or discovery task and treats disagreement as data. Covers diverse panel assembly, independent voting, consensus rules, Cohen’s and Fleiss kappa, Krippendorff’s alpha, correlated-error limits on validity claims, human validation, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, how to combine model labels, or what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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Audits a manuscript's citations for fabricated or nonexistent sources, DOIs that resolve to a different work, mismatches between in-text citations and the reference list, APA 7 or journal style, and whether each source supports the claim it is attached to. Checks entries against Crossref, OpenAlex, and DataCite and labels anything it could not verify NOT CHECKED. Use to check citations, references, a .bib file, or DOIs, or when a source may be AI-invented. Figures and tables go to figure-table-audit.

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Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.

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, search, and manage Confluence Cloud pages, spaces, blog posts, comments, attachments, and labels. Use this whenever the user wants to find a page, read a doc, search the wiki with CQL, create or update a page, add a comment, list pages in a space, pull an attachment, or ask "what does the wiki say about X" — even if they don't say "API". Also use it for any *.atlassian.net/wiki URL, or a CQL string when the context is wiki content rather than tickets. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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Read and manage Linear issues, projects, cycles, teams, comments, and labels. Use this whenever the user wants to list their issues, search issues, create or update an issue, move an issue between states, add a comment, check a project or cycle, look up a team, or ask "what's on my plate in Linear" — even if they don't say "API" or "GraphQL". Also use it for any linear.app URL or an issue identifier like "ENG-123". Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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Create user-centered, accessible interface copy (microcopy) for digital products including buttons, labels, error messages, notifications, forms, onboarding, empty states, success messages, and help text. Use when writing or editing any text that appears in apps, websites, or software interfaces, designing conversational flows, establishing voice and tone guidelines, auditing product content for consistency and usability, reviewing UI strings, or improving existing interface copy. Applies UX writing best practices based on four quality standards — purposeful, concise, conversational, and clear. Includes accessibility guidelines, research-backed benchmarks (sentence length, comprehension rates, reading levels), expanded error patterns, tone adaptation frameworks, and comprehensive reference materials.

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。