「stable」の検索結果

Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.

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Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.

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Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.

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Implement, review, or improve localization and internationalization in iOS/macOS apps — String Catalogs (.xcstrings), generated localizable symbols, stable key naming, LocalizedStringKey, LocalizedStringResource, pluralization, FormatStyle for numbers/dates/measurements, right-to-left layout, Dynamic Type, and locale-aware formatting. Use when adding multi-language support, setting up String Catalogs, enabling generated symbols for compile-time-safe localization keys, handling plural forms, formatting dates/numbers/currencies for different locales, testing localizations, or making UI work correctly in RTL languages like Arabic and Hebrew.

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Migrate Cloudflare Sandbox apps from stable @cloudflare/sandbox to @cloudflare/sandbox@next (SDK 1.0 preview). Use sandbox-next for apps already on the preview.

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Build or maintain Cloudflare Sandbox apps on @cloudflare/sandbox@next (SDK 1.0 preview). Use sandbox-migrate-to-next when porting a stable app.

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Design, improve, and evaluate reusable agent skills with high-quality SKILL.md files, precise trigger descriptions, progressive disclosure, and testable behavior. This skill should be used when users ask to create a new skill, rewrite or review an existing skill, audit a skill collection such as `config/source/skills` for redundancy or overlap, improve skill trigger quality, organize skill references, or evaluate whether a skill should trigger and behave correctly.

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Turn a vague task into a testable definition of done and generate an executable goal-test script for it, optionally with a bounded retry loop around a headless agent. Use when the user wants to run an agent in a loop, asks how to know when an agent task is finished, or says a goal like "improve X" needs to become checkable. Do NOT use for building eval suites over many cases — that is evals-bootstrap.

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When the user wants to create, generate, edit, or optimize images for marketing — blog heroes, social graphics, product mockups, profile banners, listing visuals, or brand assets. Also use when the user mentions 'AI image generation,' 'generate an image,' 'create a graphic,' 'product mockup,' 'hero image,' 'social media graphic,' 'banner image,' 'cover photo,' 'profile banner,' 'listing screenshot,' 'Flux,' 'Flux Kontext,' 'Midjourney,' 'DALL-E,' 'GPT Image,' 'ChatGPT Images,' 'Ideogram,' 'Gemini image,' 'Nano Banana,' 'Recraft,' 'Stable Diffusion,' 'Canva,' 'Figma,' 'image optimization,' 'compress images,' 'WebP,' or 'OG image.' Use this for general-purpose marketing image creation and optimization. For paid ad image creative and platform-specific ad specs, see ad-creative. For video production, see video.

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Archéologie des sources juridiques anciennes via Gallica (BNF, API stables, sans clé) : débats parlementaires du JO depuis 1871 (genèse des lois de la IIIe République), notes d'arrêt et doctrine d'origine dans les recueils Sirey et Dalloz, Bulletins civil et criminel anciens, Recueil Lebon, JO Lois et décrets 1881-2015, thèses et traités du XIXe-début XXe, Gazette des tribunaux, Bulletin de l'Inspection du travail. Déclencher dès que l'utilisateur cherche l'origine historique d'un texte, d'un concept ou d'une jurisprudence : « débats de la loi de 1884 », « note de Josserand », « que disait la doctrine en 1900 », « première apparition de la notion de… », « généalogie / histoire de l'article… », « arrêt ancien », « thèse d'avant-guerre », « Gallica », « BNF », ou toute recherche juridique antérieure aux bases modernes (Légifrance ne remonte pas avant l'époque contemporaine, Judilibre non plus). Déclencher aussi en COMPLÉMENT de travaux-preparatoires quand la loi étudiée est antérieure à 1958.

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When you want to clean and reformat content (usually from your terminal) for pasting into Slack, Notion, Twitter/X, LinkedIn, email, GitHub, or plain text. Strips ANSI codes, box-drawing chars, terminal prompt artifacts, and applies destination-specific formatting. Default input is the system clipboard; default output is both the clipboard and a chat preview. Triggers on "/paste", "/paste [destination]", "clean this for X," "format for slack/notion/twitter/linkedin/email/github," "render as markdown," "paste-ready," "strip formatting," "make this copy-pastable." Default destination is plain. Also scans for secrets (API keys, tokens, .env values) and warns before copying anything sensitive.

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Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

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Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.

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Cache the parts of the prompt that don't change so a long-running loop stops paying full price on every turn. Use when the system prompt, tool defs, or reference docs are stable across many turns.

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Negotiate a pre-code contract between generator and evaluator personas that defines what "done" means before any code is written. Turns fuzzy specs into a testable target the evaluator can hold the generator to.

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Find WeChat public-account articles that break out above the account's average reads for account-growth topic selection. Use when the user asks to discover 公众号爆款, 微信低粉爆款, 平均阅读爆款, 起号选题, category-based WeChat hot articles, or article references filtered by month_read_avg rather than unstable follower_count.

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Wrap any command-line tool into a JSON-emitting agent skill. Use when you want to make a CLI reliably callable and parseable by an AI agent — introspect its --help, define a structured-output (--json) contract and a stable error contract, then generate a SKILL.md wrapper with verified examples. Triggers on "wrap this CLI", "make X agent-usable", "generate a skill for this command", "turn a tool into an agent skill".

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Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

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Create or review an AGENTS.md file so coding agents get stable repo-local instructions: environment setup, testing, style, security boundaries, PR policy, and handoff rules. Use when a repo lacks durable agent guidance or when a custom harness needs a predictable context file.

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Inserts stable test ids into production components for each selector gap named by a test plan, following the per-framework insertion policy and never renaming existing ids. Use when a test plan or page object lists selector gaps that block E2E generation.

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Turns approved AC/SRS into an executable test plan (scenarios, seed, selector gaps) for the test-loop workflow. Use for independent test-plan generation from stable requirements.

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Research a codebase and create architectural documentation describing how features or systems work. Use when the user asks to: (1) Document how a feature works, (2) Create an architecture overview, (3) Explain code structure for onboarding or knowledge transfer, (4) Research and describe a system's design. Produces markdown documents with Mermaid diagrams and stable code references suitable for humans and AI agents.

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Guidance for managing R package lifecycle according to tidyverse principles using the lifecycle package. Use when: (1) Setting up lifecycle infrastructure in a package, (2) Deprecating functions or arguments, (3) Renaming functions or arguments, (4) Superseding functions, (5) Marking functions as experimental, (6) Understanding lifecycle stages (stable, experimental, deprecated, superseded), or (7) Writing deprecation helpers for complex scenarios.

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Build chapter-level briefs (`outline/section_briefs.jsonl`) from chapter skeleton plus section bindings before stable H3 decomposition.

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