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Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con

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Friday bulk football matchday simulations on MiroShark - pay $1 USDC per sim via x402 (Finance District agent wallet), launch Premier League, Serie A, and La Liga matchday sims in parallel, collect share links and full reports, and render or hand off one video per sim.

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Expand a hard-to-build niche list from ~10 known-good seed companies into a full qualified TAM. Seed fingerprinting (how do good-fit companies ACTUALLY show up in the company database's filters) → lookalike generation (Prospeo company_lookalike, Exa findSimilar, Parallel.ai entity search) → filter mining with a precision/volume scorecard → wide pull with auto-sharding → cheap-AI qualification → live-website verification → client transparency report. Use when a vertical list feels "too small" (medical groups, hedge funds, Medicare brokerages), when known good-fit companies don't show up in keyword searches, or when someone says "expand this list", "the TAM should be bigger", "find more companies like these".

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Adversarially review a grooming spec before implementation starts. Finds hidden risks, unvalidated assumptions, and missing dependencies. Standalone entry point for the challenger agent.

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Use this skill AUTOMATICALLY before writing any Higgsfield prompt. Query the memory databases for relevant past failures and pre-apply known fixes before the user even hits generate. Triggers include: any request to write a Higgsfield prompt, any use of the higgsfield-prompt skill, any mention of generating a video or image on Higgsfield, any MCSLA prompt construction. This skill should run SILENTLY in the background — don't announce it, just apply what's known. If the databases are empty, skip silently and proceed with normal prompt generation.

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Manage capa CLI configuration — `capabilities.yaml` / `capabilities.json`, skills, MCP servers, tools, hooks, sub-agents, rules, plugins, AGENTS.md / CLAUDE.md, security options, tool exposure modes, `capa wrap` shadow workspaces, `--passthrough` native writes, registries (including Claude marketplaces), activity traces, and the local Web UI. Use whenever the user edits the capabilities file, runs any `capa` command (init, install, add, wrap, clean, sh, start/stop/restart/status, auth, upgrade, cache, registry), wires up an MCP server, adds a skill from GitHub / GitLab / a registry / a remote URL / a local path, configures secrets via `${VarName}` placeholders, installs lifecycle hooks, defines sub-agents, or troubleshoots a failed install. Use even when the user only names a fragment ("add a skill", "wire up brave search", "wrap cursor", "block this phrase in skills") without saying "capa" — if a `capabilities.yaml` or `capabilities.json` lives at the project root, this is almost certainly the right skill. If the project does NOT yet have a capabilities file, point at the `bootstrap` skill instead.

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Audit EVERY Google Search Console property at once — rank all sites by clicks and impressions with period-over-period deltas, then diff keywords per site to surface what is newly ranking, rising, dropping, lost, or ranking well without earning clicks. Built for agencies and multi-site owners. Use when asked to compare all sites, rank properties by traffic, find new keywords across a portfolio, or spot which site is down. Trigger phrases: "audit GSC", "all my sites", "rank my properties", "portfolio search performance", "which sites are down", "what new keywords are we ranking for", "client site performance", "GSC report across accounts". For ONE site's queries, pages, or index coverage, use the search-console skill instead.

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Use when calling callable functions (httpsCallable), passing data to server-side logic, handling function errors/timeouts, configuring regions, or testing with the Emulator Suite.

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Runs once by hand on the first day and once a month after that. On the first run it researches the business from its own public presence, writes the charter with a source and a date beside every conclusion, discovers the AI Employees actually installed on this machine within a bounded search, writes the map every other routine in this kit reads, seeds the priorities and the watchlist once, builds the fleet dashboard, and registers this Employee's own seven jobs. On every monthly run it re-runs discovery and reconciles the map against what is really there. It never writes into another Employee's folder, never registers a job it does not own, sends only where you released the channel, spends only where you released it, and never touches a credential.

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Speculative parallel implementation methodology — dispatch N candidate implementations, audit all, score, pick best. Auditability mandate: ALL candidates logged (not just winner).

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根据用户指定的数学规则（如取整、特定尾数四舍五入），生成用于批量修改AutoCAD图元（如直线、多段线）坐标值的VisualLISP代码。

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Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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Use when debugging how a value or request reaches Elixir code, finding who calls a function, or planning a signature change. Builds the call tree with mix xref callers instead of reading files one by one.

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Use when debugging how a value or request reaches Elixir code, finding who calls a function, or planning a signature change. Builds the call tree with mix xref callers instead of reading files one by one.

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Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.

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Mobil uygulama paywall strateji rehberi. 14 kategori benchmark database, 4 paywall modeli, trial optimizasyonu, placement mapping, pricing psychology, regional pricing (PPP) ve Apple/Google compliance checklist.

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ALWAYS USE when asked to migrate, replace, or make testable existing C# static calls with a named wrapper or built-in abstraction: DateTime.UtcNow/Now or DateTimeOffset.UtcNow to TimeProvider/IClock, File.* to IFileSystem or an existing store such as ITextFileStore, and Environment.* to an existing reader such as IEnvironmentReader. Covers scoped files/projects, constructor injection, replacing temp-file or process-environment tests with fakes, "already registered" abstractions, and static classes whose callers/signatures must stay unchanged. Preserves DateTimeKind and call count. DO NOT USE for finding statics (detect-static-dependencies), choosing/designing a new wrapper (generate-testability-wrappers), behavior tests with no chosen seam (testability-obstacle), or test-framework migration.

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Discover and list all URLs on a website without extracting content, via the Tavily CLI. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", "list all pages", or "site structure". Faster than crawling — returns URLs only. Essential when you know the site but not the exact page. Combine with extract for targeted content retrieval.

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Read before a js eval cell that installs a package, spawns a server or PTY, or starts a long run, and before any bun -e, script, server, CLI, test, bundle, or package-management work: this session's eval js kernel runs Bun 1.4+ (this skill is present only when it does). Bun 1.4 ships builtins that replace 15+ npm deps — check here BEFORE installing sharp, puppeteer/playwright (scraping), marked, node-cron, node-pty, concurrently, serve-static, tar, json5, fast-xml-parser, string-width. Triggers: eval js, bun, Bun.serve, bun test, bun build, bun install, bun run, image resize, headless browser, markdown render, cron, PTY.

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AI-powered intelligent phone calling service to automatically handle restaurant reservation, modification, and cancellation tasks without manual dialing. Use when users want to reserve, modify, or cancel restaurant bookings.

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Produce a high-confidence, CONCISE implementation / architecture / strategy PLAN that follows the OMC plan system end to end. It first runs OMC's real requirements interview (auto-chains the `omc-plan` skill: one question at a time, explore-first, Analyst consult) to produce an initial plan in `.omc/plans/`, then DEEPENS that plan with a 3-round ITERATIVE fusion loop — each round an Opus 4.8 panelist (Agent subagent) and a GPT-5.5 panelist (via codex) critique/improve it IN PARALLEL and BLIND, then Opus 4.8 (the orchestrator) judges and synthesizes one tighter plan that seeds the next round, stopping early on convergence. Writes the final dense plan back to the same `.omc/plans/<slug>.md`, then offers the OMC handoff (`/omc-plan --review` → `/team` or `/ralph`). Use for high-stakes planning, architecture decisions, strategy calls, and hard debugging — especially when the user runs /plan or asks how to approach a non-trivial build. Reuses the `fusion` skill's run_codex.sh + judge rubric. NOT for trivial tasks (single pass is enough).

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Answer a hard question by fanning it out to a PANEL of models running in parallel — each answering independently with web search and bash, none seeing the others' work — then having Opus 4.8 judge every response into a structured analysis (consensus, contradictions, partial coverage, unique insights, blind spots) and write a final answer grounded in it. The panel is two independent Opus 4.8 runs (slug opus4.8-4.8), Opus 4.8 + GPT-5.5 via codex (opus4.8-gpt5.5), Opus 4.8 + Gemini 3.1 Pro via agy (opus4.8-gemini3.1pro), or all three (opus4.8-gpt5.5-gemini3.1pro). Opus always judges and writes the final answer — the pipeline can't be reversed. Runs on local CLI subscriptions (no metered API), and saves a timestamped provenance .md per run. Use this whenever the user asks to "run it through Fusion", says /fusion, wants a multi-model / panel / ensemble answer, wants a question cross-checked across models, or wants a higher-confidence answer with consensus and blind spots surfaced — even if they don't say "fusion". General-purpose: any topic (research, law, strategy, technical, personal). Best for high-stakes research, design calls, and debugging where being confidently wrong is expensive.

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Use when configuring, repairing, planning, or running MiniMax H3 locally on a Windows NVIDIA computer, especially when installation compatibility, component sets, resolution, generation-time budget, path, low-VRAM risk, or H3 prompt mode must be chosen from hardware and user requirements.

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Use when you need to install the embedded robot agents into .github/agents, .claude/agents, .cursor/agents, or .codex/agents, selecting the destination interactively and copying the embedded agent definitions from project assets. This should trigger for requests such as Install embedded agents; Bootstrap .github/agents; Bootstrap .cursor/agents; Bootstrap .claude/agents; Bootstrap .codex/agents; Copy robot agents. Part of Plinth Toolkit

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。