1,272 件 ・ 関連度順
概要と使いどころ
Analyze ENCODE functional genomics screens including CRISPR screens, MPRA (Massively Parallel Reporter Assays), and STARR-seq. Find screen data in ENCODE, process results, identify functional elements, and integrate with epigenomic annotations.
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。
Multi-agent code review using the Pragmatic Quality framework. Orchestrates parallel review agents (CLAUDE.md compliance, bug scanning, git history, security) with independent confidence scoring to produce high-signal findings. Use when user runs /code-review, /review:code-review, requests a "code review", "review my changes", "PR review", or mentions "review diff", "review branch".
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。
Creates isolated git worktrees in .claude/worktrees/ with intelligent branch naming, auto-incrementing, and commit type detection (feat/fix/refactor). Supports manual descriptions and auto-generation from uncommitted git changes. Use when user requests to create worktree, start isolated work, parallel development, run /worktree command, or mentions "worktree".
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。
Multi-agent review resolution with trust-but-verify methodology. Accepts a PR number/URL or a local code-review report file. Extracts review comments, verifies each with parallel agents using confidence scoring (0-100), filters false positives, presents a triage report for approval, then applies fixes and replies. Use when user runs /resolve-review, mentions "resolve PR comments", "address review feedback", "fix PR suggestions", "handle review comments", or "resolve review findings".
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 an Obsidian knowledge base from a sitemap — bulk-fetch pages via Python, generate Obsidian-optimized MD notes with parallel agents
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。
Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to up to 7 specialists, generates health score. Enhanced with live Ahrefs (DR, backlinks, traffic) and GSC (indexing, top pages) data when MCPs are available. Use when user says "audit", "full SEO check", "analyze my site", or "website health check".
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。
Archive multiple completed OpenSpec changes at once. Use when archiving several parallel changes. Also use for a plural archive request - "openspec bulk-archive", "opsx bulk-archive", "openspec archive all", or "openspec archive these changes".
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 the user needs broad parallel research across many independent inputs with a shared output schema.
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。
Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Grok Build, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 78 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "auto research", "switch model", "hand off a session", "session handoff", "switch engine", "continue on codex", "continue on claude", "move this session to another engine", "multi-agent coding", "coding session", "session inbox", "grok session", "grok build", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness", "clawo runs", "run ledger", "session cost", "coding-agent token usage", "spend cap", "budget limit", "maxBudgetUsd", "durable workflow", "resume a run", "verify a coding run", "acceptance contract", "evidence bundle", "did the agent's tests actually pass", "protected tests", "agent edited the tests", "human gate", "repair loop", "clawo workflow", "clawo verify", "clawo solve", "clawo fanout", "fix and verify", "solve from the terminal".
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。
Expert guidance for Swift Testing: test structure, #expect/#require macros, traits and tags, parameterized tests, test plans, parallel execution, async waiting patterns, and XCTest migration. Use when writing new Swift tests, modernizing XCTest suites, debugging flaky tests, or improving test quality and maintainability in Apple-platform or Swift server projects.
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。
Run the Yuzu full governance pipeline on a commit range. Produces Gate 1 Change Summary, then orchestrates Gates 2-6 as parallel agent fan-outs with pre-written prompt preambles. Use when the user says "/governance <range>" or "full governance" or asks to run the multi-gate review pipeline on a PR/commit set.
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。
Run the current session as a senior developer (Opus) leading a configurable junior fleet. Decomposes requests into scoped tasks, dispatches junior-developer subagents in parallel, optionally runs an architect plan-review gate before dispatch, autonomously resolves escalations, optionally dispatches a doc-writer second wave after juniors complete, then integrates and gates with /test + /governance. Use when the user says "/dev-team", "run the dev team", "delegate this to the juniors", "act as the senior dev", or wants a task built by a senior-led fleet.
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 batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).
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。
Documenting As-Is and To-Be business processes for a Salesforce project as swim-lane diagrams — one lane per actor, explicit decision points, parallel and exception/sad paths, and per-step automation-tier annotations that drive downstream build decisions. Trigger keywords: As-Is To-Be salesforce process, swim lane salesforce, business process map, automation candidate identification, sad path documentation, process actor mapping, manual residue. NOT for Opportunity stage and exit-criteria design — use admin/sales-process-mapping. NOT for picking Flow vs Apex for one requirement — use admin/process-automation-selection.
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。
Configure and troubleshoot Salesforce Case Escalation Rules: time-based escalation entries, business hours configuration, escalation actions (email alerts and reassignment), and diagnosing why cases are not escalating. Covers the EscalationRules metadata shape, businessHoursSource and escalationStartTime semantics, staged escalationAction thresholds, deploy-inactive parallel-run cutover, and monitoring escalated cases. Trigger keywords: escalation rule, case not escalating, SLA breach notification, escalate after X hours, reassign case automatically, IsEscalated. NOT for SLA milestone timers — use admin/entitlements-and-milestones. NOT for case routing on creation — use admin/assignment-rules. NOT for the calendar itself — use admin/business-hours-and-holidays.
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。
Diagnose and mitigate Salesforce data skew — ownership skew (single user owns >10,000 records) and parent-child skew (>10,000 children under one parent) — that cause sharing recalculation slowness, group membership lock errors, and record-level locking failures. Covers lookup skew and UNABLE_TO_LOCK_ROW, Defer Sharing Calculation (deferGroupMembership / deferSharingRules in Sharing.settings), ownership bucket groups, Controlled by Parent OWD, Bulk API serial vs parallel concurrency for skewed loads, and the documented load ordering for roles, groups and sharing rules. NOT for planning and batching a recalculation job — use data/sharing-recalculation-performance. NOT for designing the sharing model itself — use admin/sharing-and-visibility.
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。
Orchestrate AI coding agents (Claude Code, Codex, OpenCode) in isolated sandboxes using the @ai-hero/sandcastle SDK. Use when the user needs to (1) run agents AFK in Docker/Podman containers, (2) build multi-agent pipelines with plan-execute-review patterns, (3) run parallel agents on separate worktrees, (4) create iterative agent loops with maxIterations, (5) extract structured output from agent runs, (6) set up sandcastle in a new or existing project, or (7) write prompt files with template args and shell expressions.
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。
Orchestrate work as a cyclic directed graph of subagent-executed nodes with transition criteria on edges, budgeted cycles, and per-node gates (runtime verification, metric, or artifact). Use when the user says "workgraph", "run this as a graph", "graph this work", or when a goal has parallel branches, feedback loops, or mixed gate types that a linear loop cannot express. Every node is sized to a 64k context budget, so small goals run as a two-node do/verify graph and large goals scale out without caps. For clock-driven or metric-driven single loops, timeboxed-iterating and autoresearch remain lighter.
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。
Run a task iteratively over a user-specified duration by dispatching subagents. The orchestrator stays strictly linear and time-checked, but each iteration fans out MULTIPLE subagents in parallel over independent units (and those subagents may fan out further). Shared state — a compact progress digest and a growing environment cheatsheet — lives on disk and is passed to every subagent BY REFERENCE, killing the per-subagent rediscovery tax. Every subagent first reads an initialiser preamble that points it at that shared state and makes it write findings back, so the cheatsheet populates itself; role prompts are produced ONCE on the filesystem by a single scaffold command and dispatched by path, never re-typed per dispatch. Subagents persist their results to disk and return only a tiny status, so the orchestrator's context stays small and lasts. The orchestrator classifies the goal as FINITE or OPEN-ENDED: finite lists may finish early; open-ended goals use the full duration, never idle, never manufacture busywork. Use when the user gives a task and a duration and wants it ground out iteratively by subagents over that time.
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。
Fully autonomous bug hunting pipeline — discover bugs in a scoped area using parallel subagents, independently triage each finding, fix confirmed issues with subagents, then audit all fixes against repo constraints and target platforms. Runs end-to-end without user interaction.
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。
Catalog-then-fix slop removal on an existing prose draft. Parallel catalog subagents inventory four slop categories (theatrics, overwrought register, corrective antithesis, dramatic short sentences), fix subagents repair only what a catalog names, mechanical lint + frequency analysis bracket the run, and a regression gate (lint delta, word count, cold meaning check) decides whether the result ships. Use when the user says "deslop this draft", "catalog deslop", "strip the AI patterns from this file", or as the final stage of the article-factory and mosaic-writing skills. For writing or redrafting prose from scratch, use the prose skill instead - this skill only repairs an existing draft in place.
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。
Full quality pass -- chains parallel UX + scalability audit, then QA verification, then consistency gate. Fixes everything it finds. Works with any stack.
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。
Displays the full skills catalog with descriptions, autonomous build chains, recommended pipelines, and parallelization rules.
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。
Reconstructs the development cycle from git history, distills sequential/parallel patterns, and produces actionable insights for improving future iterations. Triggers: recall, retrospective, development analysis, what happened, dev cycle 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。