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Everything your AI needs, in one place.
Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.
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A service that does a whole job for you - research, coding, support - and reports back.
Step-by-step instructions an AI follows for one kind of task. Install once, reuse everywhere.
A voice and set of rules layered onto any model: tone, audience, do's and don'ts.
A ready-to-use prompt with fill-in-the-blank variables and notes on when it works best.
A complete routing setup - models, rules and settings - in one file you can apply in a minute.
A single function an AI can call: a calculator, a search, a database lookup.
A language model endpoint with its price, speed and quality declared so the router can compare it.
subagent-orchestrator
Coordinate quota-aware parallel subagents for large, multi-file Antigravity tasks.
agenthub
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specializ
dispatch
Use when a task file exists in .hyperflow/tasks/ and workers need dispatching. Fans out parallel workers under per-batch Reviewers, runs a final integration review, and commits per sub-task. Endpoint
agent-workloads
Compatibility alias for the canonical `workflow-rig` front door. Use when older prompts mention `agent-workloads` or when you need the legacy workload-planning guidance; for new work, load `workflow-r
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
cupynumeric-parallel-data-load
Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/G
worktree
Create an isolated git worktree from the correct base branch and check it out into a clean, gitignored directory. Use when the user asks to make a worktree, spin up a parallel/isolated workspace, work
dispatching-parallel-agents
Use multiple Claude agents to investigate and fix independent problems concurrently
qcsd-ideation-swarm
Use when running Quality Criteria sessions during PI/Sprint planning with HTSM v6.3, Risk Storming, or Testability analysis in the QCSD Ideation phase.
qcsd-refinement-swarm
Use when running Sprint Refinement sessions with SFDIPOT product factors, generating BDD scenarios, or validating requirements in the QCSD Refinement phase.
langgraph
LangGraph 1.x (LTS) Python workflow patterns for state management, delta channels, resilience (node timeouts, error handlers, graceful drain), routing, parallel execution, supervisor-worker, tool call
large-data-with-dask
Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.
quick-quality-check
Lightning-fast quality check using parallel command execution. Runs theater detection, linting, security scan, and basic tests in parallel for instant feedback on code quality.
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
multi-agent-workforce-planner
Designs parallel agent workstreams for large feature sets by analyzing dependencies, assigning specialized agent types (Explore, Plan, Bash, Edit), maximizing parallelization, and creating execution p
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
batch-processor
Process multiple documents in bulk with parallel execution. Use when a user asks to batch process files, convert many documents at once, run parallel file operations, bulk rename, bulk transform, or p
oh-my-claudecode
Orchestrate multi-agent Claude Code teams — assign roles, coordinate parallel tasks, share context between agents, and manage team workflows. Use when: running multiple Claude Code agents simultaneous
worktrunk
Manage Git worktrees for parallel AI agent workflows with Worktrunk CLI. Use when: running multiple AI coding agents simultaneously on different branches, parallelizing feature development with AI, ma
testing-ci
CI/CD: GitHub Actions workflows, parallel sharding, flaky quarantine, junit XML/Allure, coverage gates
audit
[skaile-development] Monorepo-aware code quality audit for the skaile-dev codebase. Verifies build (lint + typecheck + build) and tests pass, then dispatches three parallel sub-agents for logic, secur
parallel
Use when two or more pieces of work are genuinely independent — disjoint files, no shared state, no ordering edge — and should fan out across subagents, then be gathered and reconciled under a combine
Find
Search or browse by kind. Every card shows who made it, how many people installed it and what they think.
Install
One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.
Rate and publish
Leave a star rating after you have used it. Made something useful? Publish it - free listings go live immediately.
Prefer the terminal? osr stack apply registry://starter installs the starter template.