「reference」の検索結果

First-run conversational setup wizard for marketing-cli. Use when /cmo detects no populated brand files and no cmo-preferences.md, when the user just installed marketing-cli for the first time, or when they say things like "set up marketing", "start marketing", "marketing wizard", "I just installed marketing-cli", "onboard me", "first time", "initial setup". Asks 3 sharp questions (identity, posture, distribution preferences), records the answers to brand/cmo-preferences.md, then hands off to /cmo's foundation flow which spawns 3 research agents in parallel. The user never has to type /mktg-setup directly — /cmo auto-routes here on first run.

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Build a new-topic PPTX by reusing an existing reference deck's template (layouts / footers / fonts / palette) while replacing all content from primary sources. Use when "use this product deck's look for a different product deck", "use this deck as template for new topic", "参照デックの体裁で別内容を作る", "既存 PPTX を流用して別テーマで作り直す". Not for deck translation, from-scratch generation, or outline-only planning.

Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.

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Use when hunting Broken Object Level Authorization (BOLA) or Insecure Direct Object Reference (IDOR) vulnerabilities in APIs or web applications. Trigger on: "BOLA", "IDOR", "broken object level", "access other users", "object reference", numeric or UUID IDs in URLs or request bodies, user-scoped resources, horizontal privilege escalation, "change the ID in the request", second-order IDOR, blind IDOR, indirect reference, encoded ID, deprecated API version, JSON globbing.

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Use this agent when you need to extract and summarize key findings from scientific references that are relevant to a gene's function. This agent should be used after references have been identified in a gene review to provide detailed supporting evidence from the literature. The agent will look up cached publications and extract only the most relevant findings related to the gene's direct function, excluding irrelevant experimental details unless they directly inform gene function.

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UI design and implementation for real product surfaces with taste-first critique, reference routing, project-specific design briefs, variation comparison, and implementation verification. Use when Codex should redesign, build, polish, or critique frontend UI by reading project context, exploring taste, routing through `references/catalog.md`, synthesizing `PROJECT_DESIGN.md` or `design.md`, using investment gates to avoid wrong taste/reference/scope, implementing in the existing stack, and evaluating whether the result is actually better. Best for app screens, dashboards, landing pages, query tools, product surfaces, and design explorations; not for backend-only work or tiny cosmetic edits.

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Query the Ensembl REST API for regulatory feature annotations, variant effect prediction (VEP), coordinate liftover, gene lookups, and cross-references. Use when the user needs to annotate variants with VEP (consequence, CADD, REVEL, SpliceAI), check Ensembl Regulatory Build overlap for ENCODE regions, convert coordinates between GRCh37 and GRCh38, resolve gene IDs (Ensembl ↔ symbol ↔ RefSeq), look up gene phenotype associations, or cross-reference ENCODE targets with Ensembl annotations. Also use when the user mentions Ensembl, VEP, variant effect predictor, liftover, assembly conversion, regulatory build, gene lookup, or cross-references between databases.

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Email consent management in Marketing Cloud: configuring Subscription Centers, building Preference Centers on CloudPages, enforcing CAN-SPAM and GDPR requirements, and handling opt-out propagation. Trigger keywords: unsubscribe handling, CAN-SPAM compliance, GDPR consent tracking, publication lists, subscription center, preference center, opt-out, double opt-in, Privacy Center, MC consent. NOT for subscriber key and suppression lists — use data/subscriber-data-management. NOT for the CRM consent data model or MC-to-CRM sync — use architect/marketing-consent-architecture.

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Write, restructure, or review documentation — tutorials, how-to guides, reference pages, concept/explanation docs, API references, READMEs, changelogs, release notes, and troubleshooting guides. Distills documentation craft from Mintlify's guides (compiled from technical writers at Stripe, GitHub, Amplitude, and Anaconda), the Diátaxis framework (including the compass and per-type voice), the Google and Microsoft style guides, Every Page Is Page One, Write the Docs, and Docs for Developers: audience analysis, content-type selection, style and word-level rules, procedure writing, structure for humans and AI agents, code-example standards, page templates (templates.md), mechanical enforcement and llms.txt (mechanics.md), maintenance, and success metrics. Use this skill when the user asks to "write docs", "document this feature", "improve this page", "review these docs", "write a tutorial / how-to / reference page", "structure the docs", "write API documentation", "write a README / changelog", or when authoring any file under a `docs/` tree. For SKMTC docs specifically, this skill governs the *craft* (what makes the page good); the content split between skills, `llms.md`, and the docs tree is governed by `docs/skills/README.md`. Distinct from `skmtc-retro` (captures observations about work) and the `skmtc-*` operational skills (guide doing the work) — this skill guides writing *about* the work for readers.

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Recommend a conservative shadow-only review reference architecture from valid offline evidence. Use only when the user explicitly asks for a reference-architecture recommendation or uses the bundled synthetic fixture.

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Generate consensus FASTA sequences by applying VCF variants onto a reference with bcftools consensus, or build viral/amplicon consensus with iVar. Use when reconstructing a sample-specific reference or haplotype, deciding -H haplotype vs IUPAC vs all-ALT projection, masking no-coverage sites so a consensus does not manufacture false reference calls, or setting iVar min-depth/min-frequency policy for surveillance genomes.

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Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Dev-only check that a drafted spec for THIS baseline repo won't ship dev-tree references to consumer installs. Catches three failure modes — shipped SKILL.md prose that references paths under `src/`, `tests/`, `scripts/`, `obj/` as runtime invocations (in ```bash fences``` OR `inline backticks`, plus shipped `.mjs`/`.js`/`.sh`/`.py` helper-file imports); new Python helpers added under `.claude/skills/<slug>/` (shipped helpers must be `.sh` or `.mjs`/`.js` going forward); and imports of modules that aren't in `obj/template/.claude/manifest.json` (consumer won't have the file). The aggregate scanner (`scan-shipped-skills.mjs`) walks only baseline-owned skill dirs (via `owner: baseline` frontmatter) at top level — `references/` and other subdirs are documentation, not runtime. BLOCKER findings hard-block implementation entry; ADVISORY surfaces but doesn't block. Read-only — surfaces a punch list; maintainer edits the spec and re-runs until CLEAN.

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Pure API reference for reddapi.dev - authentication, all endpoints (vector search, semantic search, trends, subreddit lookup), request parameters, response schemas, and error codes, with no research-workflow framing. Use when the user wants raw endpoint documentation, is debugging a reddapi.dev integration, needs exact request/response field names, or asks for 'reddapi API reference', 'reddapi.dev endpoints', or 'reddapi error codes'. For guided research workflows and query playbooks, see reddit-research. For B2B lead scoring, see reddit-leads.

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Simple Preference Optimization para alinhamento de LLMs. Alternativa sem modelo de referência ao DPO com melhor desempenho (+6.4 pontos no AlpacaEval 2.0). Sem modelo de referência necessário, mais eficiente que DPO. Use para alinhamento de preferências quando quer treinamento mais simples e rápido que DPO/PPO.

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Load this skill whenever the project has personalization features, user preference controls, theme selectors, font size adjusters, motion toggles, contrast settings, or any user-configurable accessibility accommodations. Under no circumstances override or ignore user OS-level accessibility preferences without explicit user consent. Absolutely always persist user preferences, apply them immediately, and respect prefers-reduced-motion, prefers-contrast, and similar media queries.

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Expert guidance for Salesforce Flow implementation from design through deployment. Use this skill when building Screen Flows, Record-Triggered Flows, Schedule-Triggered Flows, or Autolaunched Flows. Covers Flow design patterns, metadata XML generation, variable/element reference validation, governor limit optimization, and sf CLI deployment. Critical for preventing reference errors (undeclared variables, invalid element references) and deployment failures. Use when creating new Flows, debugging Flow errors, optimizing Flow performance, or deploying Flows to production.

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Inspect a reference (web URL, image, PDF, screenshot) and propose a candidate Style for the workspace. Use when the user says "save this style", "guarda este estilo", or shares a reference and wants to reapply it later.

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Creates exhaustive technical references and API documentation. Generates comprehensive parameter listings, configuration guides, and searchable reference materials.

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Guide for interpreting ResolveProjectReferences time in MSBuild performance summaries. Only activate in MSBuild/.NET build context. Activate when ResolveProjectReferences appears as the most expensive target and developers are trying to optimize it directly. Explains that the reported time includes wait time for dependent project builds and is misleading. Guides users to focus on task self-time instead. Do not activate for general build performance -- use build-perf-diagnostics instead.

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Set up and run Direct Preference Optimization (DPO) training on preference datasets using the Tinker API. Use when the user wants to train with preference data, chosen/rejected pairs, or DPO.

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Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

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Maps query single-cell data onto reference atlases and transfers cell-type labels using scArches surgery (scVI/scANVI), Symphony, Azimuth, CellTypist, scPoli, popV, and foundation models, with explicit out-of-distribution and label-transfer uncertainty. Use when annotating new single-cell datasets against a pre-trained reference, deciding which mapping method fits, or judging whether transferred labels are trustworthy. For de novo clustering and manual annotation see single-cell/cell-annotation; for batch integration without a reference see single-cell/batch-integration.

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OpenAI API (developers.openai.com) の画像・動画生成リファレンス。 image generation（gpt-image, Images API `images.generate` / `images.edit`）、 image edit（mask, reference images）、image generation tool（Responses API 組み込み）、 vision（画像入力, image tokens, vision fine-tuning）、 video generation（Sora, `sora-2` / `sora-2-pro`, videos エンドポイント）、 extend / edits, characters。