「baseline」の検索結果

Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design. This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Proteina-Complexa" as the BioClaw-facing skill label. For sequence design after backbone generation, use proteinmpnn or solublempnn. For QC thresholds, use protein-design-qc.

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 analytics-driven browser test matrices and execute cross-browser tests. Covers BrowserStack/Sauce Labs configuration, Playwright browser channels, common cross-browser CSS/JS divergences, a known-issues documentation log, and progressive enhancement validation. Use when: "cross-browser," "browser matrix," "BrowserStack," "Safari issues," "browser compatibility," "Edge," "works in Chrome but not Safari." Not for: pixel-level baseline strategy and threshold tuning — use visual-testing; device-farm testing of native/hybrid apps — use mobile-testing. Related: visual-testing, playwright-automation, ci-cd-integration, mobile-testing.

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。

Write production-grade Playwright tests in TypeScript: Page Object Model, fixtures, auto-waiting, user-facing locators, parallel execution, CI integration, sharding, and 2025-2026 feature awareness. Includes an explicit "do not" list for AI agents. Use when: "Playwright," "write E2E test," "page object," "new Playwright suite," "Playwright config." Not for: fixing one flaky test at runtime — use test-reliability. Not for: bulk regenerating selectors after a UI refactor — use selector-drift-recovery. Not for: visual baseline creation/management — use visual-testing. Not for: deep WCAG/axe audits — use accessibility-testing. Related: visual-testing, ci-cd-integration, api-testing, test-reliability, selector-drift-recovery, accessibility-testing.

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。

End-to-end substantive-integrity forensic sweep of a research paper (especially autoresearch / AI-Scientist-style output). Orchestrates the whole pipeline: ingest (arxiv-id | pdf | dir → working dir + pdftotext for L0) → /evidence-ledger (artifact manifest + observability level L0/L1/L2 + span-anchored claims.json) → fan out the integrity auditor skills (consistency, citation, baseline, experiment, presentation, proof-derivation, eval-design — each reads the ledger, emits span-anchored findings) + the zero-verdict-weight AIS writing-style track → advisory memos (/adversarial-case-builder + /novelty-duplication-advisory, no verdict weight) → deterministic tools/adjudicate_findings.py (--ledger REQUIRED) → reviewer-ready Integrity Forensics Report. Cross-model (fresh codex per dimension) and reviewer≠adjudicator: the model proposes findings, the deterministic adjudicator decides the verdict. Observability-aware, detect-only, never an opaque AI-text classifier (a separate zero-weight AIS section lists AI writing-style impressions, never moving the verdict). Triggers: "anti-autoresearch", "integrity audit this paper", "forensic review", "audit a submission", "审一篇投稿的诚信".

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。

效能分析與最佳化：量測 baseline、定位瓶頸、優化最高影響項、驗證改善效果。適用於應用程式慢、API 超時、前端 Core Web Vitals 問題。

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。

日本語の業務文書を作成・修正するときに使用する共通品質基準。事実性、確度、論理、簡潔さ、自然な日本語を守る。提案書、報告、技術説明、議事録、メール、Slack、要約、レビューに適用する。創作、広告コピー、コードや構造化データだけの生成には使用しない。

Exploratory data analysis. Use when users upload .csv/.xlsx/.json/.parquet files or request "explore data", "analyze dataset", "EDA", "profile data". Small files get ydata-profiling HTML/JSON reports; large files (over 200MB or 5M rows) get fixed-memory DuckDB/sketch profiling. Also covers near-duplicate row detection, cross-file key overlap ("can these join?"), dataset drift vs a stored baseline, and time-series profiling.

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。

Audit stale provenance-tracked docs on request. Report first; re-scout or re-baseline only for selected docs.

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。

Audit stale provenance-tracked docs on request. Report first; re-scout or re-baseline only for selected docs.

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 an OpenClaw maintainer or owner is reviewing a ClawHub malicious-skill profile proposal PR: checking `proposals/<GHSA-ID>/clawscan.yml`, reading the private vulnerability context without leaking it, running the SkillTrustBench Profile Gate or equivalent local benchmark, updating the accepted baseline, and promoting an accepted candidate into `internal/profiles/clawhub/clawscan.yml`.

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。

Generates a 3-month SEO performance HTML report for any domain using DataForSEO data. Fetches baseline vs current traffic, keyword rankings, top content pages, and competitive landscape, then outputs a report styled with the Infrasity brand design system: #0D0A1A background, #8157F2 purple accent, Instrument Sans + DM Sans + DM Mono typography. Use this skill whenever a user provides a target domain and a list of competitor URLs and asks for an SEO report, performance report, SEO analysis, competitive SEO comparison, traffic report, ranking report, or 3-month SEO summary. Also trigger when the user says "generate SEO report for X vs Y and Z", "create a performance report", "compare my SEO against competitors", or pastes a domain and asks how it's performing versus the market. Always use this skill for SEO report generation — do not attempt to build the report without following this structured data-fetch and HTML generation 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。

Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

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, modify, audit, repair, optimize, debug, and deploy agents with Agentforce Agent Script. TRIGGER when: user creates, reviews, or changes .agent files or aiAuthoringBundle metadata; asks to fix AgentScript, audit an existing agent, run an AgentScript health check, common-pitfall review, or baseline-versus-candidate repair loop; changes a response, action, subagent, route, state flow, or Agent Spec; previews, debugs, deploys, publishes, or tests agents; uses sf agent generate/preview/publish/test; or manages Agentforce MCP servers, tools, assets, or authentication. DO NOT TRIGGER when: Apex, Flow, Prompt Template, Experience Cloud, or general Salesforce CLI work is unrelated to Agent Script; or the primary input is a production session or trace ID rather than an agent artifact.

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 training, tuning, comparing or evaluating a model — choosing the cross-validation scheme, baselines, tuning inside CV, thresholds and calibration, per-slice metrics, feature importance and error 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。

Use when a task asks for a model, a score, a forecast or a prediction-driven feature — pin down the decision, target, unit, prediction time, metric and baseline before writing any modelling 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。

Phase G of a WinForms-to-Avalonia migration: decompose oversized UI state and orchestration, harden asynchronous workflows and external boundaries, preserve composition lifetimes, keep Headless tests process-isolated, and update ownership documentation. Use only when an explicit migration has post-port structural or resilience debt, or when the orchestrator routes here. Baseline input security belongs in the phase that introduces the boundary, not only here.

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 pinned default technology stack for SMB Product-Builder products. One source of truth so the architect, app-scaffolder, auth-engineer, and senior-dev never re-decide the stack per build — they build ON it. Covers framework, ORM/DB, auth, UI, payments/email/SMS, files, jobs, testing, hosting, and observability, with the chosen default + the one sanctioned alternative for each. Applied whenever a new product is scaffolded or a stack choice would otherwise be improvised. Re-deciding the stack every build is the biggest silent time sink; this kills 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。

Designs and runs reproducible evaluations for AI agents, prompts, tools, skills, and model-backed workflows using realistic datasets, isolated baselines, objective assertions, rubric grading, trajectory analysis, cost/latency tracking, and regression comparison. Use when measuring agent quality, optimizing skill triggering, comparing prompts or models, or gating an AI feature release. Not for ordinary deterministic unit tests.

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。

Tests a real web user flow with a browser by asserting semantic behavior, network and loading states, keyboard access, responsive layouts, and stable visual evidence. Use for browser bugs, end-to-end UI behavior, responsive or accessibility checks, and screenshot baselines. Not for static source review without a browser or for backend-only tests.

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。

Designs and executes compatibility-preserving migrations across APIs, schemas, frameworks, dependencies, protocols, storage, and infrastructure with baselines, coexistence, staged cutover, rollback or forward-fix paths, and explicit removal criteria. Use for deprecations, upgrades, data moves, contract changes, and replacing live implementations. Not for a local refactor with no external state or compatibility boundary.

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 adding a plugin to the marketplace, or when authoring, editing, or re-rendering a scene under scripts/animations/scenes/ — including when a scene fails the render contract, the contrast gate, or the grid baseline.

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。

Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.

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。

Implement bounded features and fixes in existing or new mini-program codebases while preserving repository rules, user-owned changes, approved behavior, assets, framework conventions, and evidence boundaries. Use when users ask to write or modify mini-program code, implement a confirmed specification or architecture, add a scoped feature, remove behavior semantically, update internal documentation required by a change, or carry out a well-defined small fix that does not require root-cause investigation. Establishes a baseline and change boundary, applies test-driven small steps, distinguishes source from generators and build artifacts, verifies the affected contract, and never reports source completion as device validation, formal acceptance, upload, or release.

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。

Acquire and verify finite environment, data, weight, and baseline inputs.

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。