「orr」の検索結果

seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990).

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Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and '

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TrailSnap 仓库提交、推送、需求平台与 PR 工作流规则。Use when preparing commits, pushing branches, managing the corresponding platform requirement, creating or merging pull requests, or monitoring PR CI in this repository; do not use for ordinary code changes that do not involve submission.

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Build crafted, highly detailed, interactive isometric SVG figures that explain a technical idea as a real physical object (a test rig, a dial gauge, a cabinet of drawers, a plotter, a lock), with many small precise solid parts, four-tone shading, correct painter's order, a framed card with title, hint and live readout, and calm eased motion. Use this whenever someone wants an isometric illustration, an explanatory diagram or figure for docs, a blog post, a landing page or an article, a "Linear-style" or "premium" technical drawing, an interactive explainer, or an SVG that shows how a system, algorithm or component works, even if they never say "isometric". Works framework-free (TypeScript/JS that writes SVG and HTML) and in React/Next.js, and can add realistic WebGL shaders (fire, exhaust, water, caustics, lamp light) that live inside the drawing in the same isometric perspective.

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Static config-correctness linter for this instance - catches the silent-failure class (unquoted schedules, duplicate keys, unconfigured skills, mode typos, broken requires/MCP refs) that no run-based health skill can see. Notifies only on problems.

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Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching trial loop), runtime CPU dispatch, OpenMP threading, and brgemm integration for GEMM-heavy kernels.

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Interactive loop that builds and tunes an AI prompt for evaluating whether a company fits a client's ICP. Run after any list-building skill (disco-like, blitz-list-builder, google-maps-list-builder, prospeo-full-export) to qualify companies before scaling. Iterates batches of 10 companies with user feedback, stops when 2 consecutive rounds have zero corrections, saves the final prompt for reuse. Always uses Claude Code Task sub-agents — never an external API key.

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Programmatic inbox management for Smartlead. Enable/disable warmup with correct ramp settings, set signatures in bulk, tag inboxes (active vs insurance), and pull inbox health dashboards. Use after creating a new batch of inboxes via /zapmail-domain-setup-public, or when managing an existing Smartlead account at scale. Triggers on "turn on warmup", "set signatures", "tag inboxes", "inbox health", "set up new inboxes".

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This skill should be used when searching for people/leads using the Prospeo Search Person API. It provides the correct API format, filter types, rate limiting patterns, and state-by-state crawling techniques to overcome the 25K result limit. Use when building lead lists, searching by job title/industry/location, or integrating with Prospeo.

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Run the Definition of Done checklist for the current wp-rocket branch and report PASS/WARN/FAIL with evidence. Two modes: layer 1 (self-correction inside backend-agent / frontend-agent — resolves FAILs before handoff) and layer 2 (independent orchestrator gate — fresh perspective after handoff). Pass layer: "1" or layer: "2" when invoking.

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Use when you need to determine audience awareness level and the correct messaging approach. Trigger on "awareness stage", "cold vs warm audience", "Schwartz", "message to market match".

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Review SQL and ORM queries for correctness, safety, and performance before they ship.

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Local user-profile maintenance skill for Codex, Claude Code, OpenClaw, OpenCode, and other agent harnesses. Use only when the user explicitly invokes $user-profile-keeper to create, initialize, update, query, correct, delete, export, or audit a local persistent user profile. It can extract durable collaboration preferences, requirement-expression habits, capability boundaries, recurring omissions, risk preferences, privacy boundaries, and typical events from the current session into auditable, confirmable, retractable local profile data. Do not auto-invoke, upload profile data, or replace task-clarifier's normal clarification flow.

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Deep need-clarification skill. Use only when the user explicitly invokes $task-clarifier. Once activated, keep asking until all three goals are met: the user fully understands their own needs, the AI fully understands the user's needs, and the user confirms the AI's understanding is correct. Do not intervene in task execution unless explicitly invoked.

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Cialdini's seven principles of compliance (the classic six — reciprocation, commitment-consistency, social proof, liking, authority, scarcity — plus Unity, added 2016) and Pre-Suasion. Use when crafting ethical persuasion (marketing, sales, negotiation), defending against manipulation, or designing systems that need cooperation. Includes replication-crisis caveats and known factual corrections (Bickman parking-meter numbers, Hofling modern replication, Drive Carefully condition disambiguation, Genovese myth).

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Plan, triage, and write academic rebuttals and review responses. Use when an AI research agent needs to handle conference, journal, workshop, or grant reviews; classify reviewer concerns; decide what evidence or experiments can change the decision; write an AC-facing executive summary; draft per-reviewer responses; correct factual misunderstandings without sounding hostile; handle novelty, baseline, method, experiment, clarity, and scope objections; or produce a final rebuttal checklist.

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Automate the full QuarkPanTool → mswnlz GitHub content publishing pipeline. Supports Quark / Baidu / Aliyun drives with multi-account rotation. Use when the user provides share URLs and wants: (1) auto-detect drive type and route to correct handler, (2) save resources with automatic account rotation, (3) clean junk files from shared folders, (4) copy promotional files into each folder, (5) generate permanent encrypted share links, (6) auto-classify items into local content dirs (book/movies/AIknowledge/tools/etc.), (7) append/update YYYYMM.md and README index, (8) local git commit, (9) send unified Telegram notifications to multiple groups, (10) rebuild site and deploy to Cloudflare Pages.

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Заимствование объектов из конфигурации 1С в расширение (CFE). Используй когда нужно перехватить метод, изменить форму или добавить реквизит к существующему объекту конфигурации

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Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to flag hallucinated citations, build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".

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Maintain package-level `agentmap.md` files as outline-level maps of the real file tree and core responsibilities. Use when package structure or responsibility boundaries change and the corresponding `agentmap.md` must be created or updated.

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Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.

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Knowledge base from "Solving a Million-Step LLM Task with Zero Errors" (Meyerson, Paolo, Dailey, Shahrzad, Francon, Hayes, Qiu, Hodjat, Miikkulainen — arXiv:2511.09030). Use when designing long-horizon, multi-step agentic LLM pipelines that need very high reliability, applying Maximal Agentic Decomposition (MAD), first-to-ahead-by-k voting, or red-flagging error correction, estimating cost/reliability scaling laws for multi-agent systems, or studying Massively Decomposed Agentic Processes (MDAPs).

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Implement, review, or improve SwiftUI features using the iOS 26+ Liquid Glass API. Use when asked to adopt Liquid Glass in new SwiftUI UI, refactor an existing feature to Liquid Glass, or review Liquid Glass usage for correctness, performance, and design alignment.

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Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output quality.

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