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Coordinate complex engineering work across bounded subagents while protecting the main context, preventing edit conflicts, and measuring whether delegation was worthwhile. Use when the user asks to delegate, parallelize, orchestrate, use subagents, fan out a review, or handle a large task with independent workstreams; do not use for small, tightly coupled, or inherently sequential tasks.
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Subagent that scans CopilotKit + AG-UI releases shipped in (and just after) the report window, diffs the git tags to extract what each release actually fixed, and returns a fix-map the weekly-report writer cross-checks against open issues. Catches issues that are quietly already-fixed in a shipped release, and is the authoritative source for the "current release vX.Y.Z" version claims. Invoked by weekly-report (spawned in parallel with the Discord/GitHub pulls); runs as a general-purpose subagent so the orchestrator's context stays small.
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Implement a ready rolling-wave slice without locking it down with broad tests yet. Use when the user explicitly wants to code the current docs/rolling-wave/{project}/slices/NNN-slug.md slice, mark it in progress while working, implement small or cohesive work in the main context by default, use one to three gpt-5.6-terra workers only for genuine parallel chunks or large discovery/mechanical work where delegation pays for its handoff cost, pause or hand off chunks marked human-fit when appropriate, record implementation notes and final-test backlog, preserve the original slice contract, and mark completed implementation ready for review.
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What every Ollama timing field means and the arithmetic that turns it into a claim - tokens per second, time-to-first-token, why total_duration is not latency, why a second run is faster, what changes with context length, quantization, GPU offload and parallel requests - plus the reporting rules that make a benchmark a measurement instead of an anecdote.
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This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.
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Orchestrates multi-step tasks autonomously using a 3-tier agent hierarchy (Director/Manager/Worker). Use when user says "run this end-to-end", "handle everything", "execute all tasks", or needs parallel task execution with queuing, course correction, and session resume. Provides FIFO task queue, escalation policy, git worktree isolation, and context persistence across agent sessions.
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AI PR intake and multi-agent governance for deployless repositories. Use when a repo has high-volume bot or agent PRs, parallel agent work, noisy low-context PRs, merge-queue pressure, CI exhaustion, duplicate fixes, ownership confusion, or review bottlenecks that threaten a single-mainline delivery model.
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Establish build performance baselines and apply systematic optimization techniques. Only activate in MSBuild/.NET build context. USE FOR: diagnosing slow builds, establishing before/after measurements (cold, warm, no-op scenarios), applying optimization strategies like MSBuild Server, static graph builds, artifacts output, and dependency graph trimming. Start here before diving into build-perf-diagnostics, incremental-build, or build-parallelism. DO NOT USE FOR: non-MSBuild build systems, detailed bottleneck analysis (use build-perf-diagnostics after baselining).
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Decomposes a large implementation plan into small-model-sized tasks executed by supervised worker agents in dependency-ordered parallel waves, with a plan-first approval gate inside every task, file-based state for context compaction, and a hard escalation ladder. Use when a master plan or multi-workstream feature is too large for one session and should be executed cheaply by many small agents under frontier-model supervision.
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This skill should be used when the user asks how to "use VibeTags", "add VibeTags annotations", "set up AI guardrails", "protect code from AI", "configure AI platforms", asks about @AILocked, @AIContext, @AIDraft, @AIAudit, @AIIgnore, @AIPrivacy, @AICore, @AIPerformance, @AIContract, @AITestDriven, @AIThreadSafe, @AIImmutable, @AIDeprecated, @AIObservability, @AIRegulation, @AIArchitecture, @AILegacyBridge, @AIStrictClasspath, @AIInternationalized, @AIPublicAPI, @AISchemaSafe, @AIStrictExceptions, @AIStrictTypes, @AIParallelTests, @AIIdempotent, @AIFeatureFlag, @AISecure, @AICallersOnly, @AISandboxOnly, @AIMemoryBudget, @AIPure, @AIDomainModel, @AIExtensible, @AIInputSanitized, @AISecureLogging, @AIExplain, @AIPrototype, @AISunset, @AITemporary annotations, or wants to control how AI tools interact with Java code.
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This skill should be used when conducting a scored agent-native architecture review. It launches 8 parallel sub-agents to audit action parity, context injection, CRUD completeness, capability discovery, and prompt-native features.
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Upstash サーバーレスデータプラットフォーム (マネージド SaaS の JS SDK)。 @upstash/redis (REST, pipelining, transactions, JSON, Redis Search, VECTOR/Array/Functions コマンド)、 @upstash/ratelimit (Fixed/Sliding Window, Token Bucket, blockUntilReady)、 QStash (publishJSON, schedules, queues, URL Groups, flow-control, DLQ)、 @upstash/vector (upsert, query, ANN, namespace, hybrid/sparse index, embedding models, reranking)、 @upstash/workflow (serve, durable execution, context.run/sleep/call/invoke, waitForEvent, parallel steps, agents)、 Upstash Search (@upstash/search, search, upsert, fetch, range, filtering, reranking, algorithm)、 @upstash/box (サンドボックス, filesystem, git, CDP, browser, network policy, snapshots, custom harness)、 upstash CLI。Rust 製 fandhe-db とは別。
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Bulk mode — maximum development speed by spending tokens aggressively on massive parallel sub-agents and multi-agent workflows, while the orchestrator context stays lean (pairs with /slim). Invoking /bulk is the user's explicit opt-in to Workflow/ultracode-style orchestration for this session. Use when the user types /bulk, asks for "爆速", "並列で大量に", "ultracode", "多エージェント", or hands over a big task where wall-clock speed matters more than token cost.
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Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).
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Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it".
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