---
title: "or"
description: " ![\"AgentSys\"](\"site/assets/logo.png\")
"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/readme-17
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:36:10.733Z
license: CC-BY-4.0
attribution: "or — Claudary (https://claudary.paisolsolutions.com/skills/readme-17)"
---
# or
## Overview
AgentSys
A modular runtime and orchestration system for AI agents.
19 plugins · 49 agents · 41 skills (across all repos) · 30k lines of lib code · 3,507 tests · 5 platforms
Plugins distributed as standalone repos under agent-sh org - agentsys is the marketplace & installer
Commands · Installation · Website · Discussions
Built for Claude Code · Codex CLI · OpenCode · Cursor · Kiro
New skills, agents, and integrations ship constantly. Follow for real-time updates:
---
AI models can write code. That's not the hard part anymore. The hard part is everything around it - task selection, branch management, code review, artifact cleanup, CI, PR comments, deployment. **AgentSys is the runtime that orchestrates agents to handle all of it** - structured pipelines, gated phases, specialized agents, and persistent state that survives session boundaries.
---
> Building custom skills, agents, hooks, or MCP tools? [agnix](https://github.com/agent-sh/agnix) is the CLI + LSP linter that catches config errors before they fail silently - real-time IDE validation, auto suggestions, auto-fix, and 399 rules for Claude Code, Codex, OpenCode, Cursor, Kiro, Copilot, Gemini CLI, Cline, Windsurf, Roo Code, Amp, and more.
## What This Is
An agent orchestration system - 19 plugins, 49 agents (39 file-based + 10 role-based specialists in audit-project), and 41 skills that compose into structured pipelines for software development. Each plugin lives in its own standalone repo under the [agent-sh](https://github.com/agent-sh) org. agentsys is the marketplace and installer that ties them together.
Each agent has a single responsibility, a specific model assignment, and defined inputs/outputs. Pipelines enforce phase gates so agents can't skip steps. State persists across sessions so work survives interruptions.
The system runs on Claude Code, OpenCode, Codex CLI, Cursor, and Kiro. Install via the marketplace or the npm installer, and the plugins are fetched automatically from their repos.
---
## The Approach
**Code does code work. AI does AI work.**
- **Detection**: regex, AST analysis, static analysis - fast, deterministic, no tokens wasted
- **Judgment**: LLM calls for synthesis, planning, review - where reasoning matters
- **Result**: 77% fewer tokens for [/drift-detect](#drift-detect) vs multi-agent approaches, certainty-graded findings throughout
**Certainty levels exist because not all findings are equal:**
| Level | Meaning | Action |
|-------|---------|--------|
| HIGH | Definitely a problem | Safe to auto-fix |
| MEDIUM | Probably a problem | Needs context |
| LOW | Might be a problem | Needs human judgment |
This came from testing on 1,000+ repositories.
---
## Benchmarks
Structured prompts and enriched context do more for output quality than model tier. Benchmarked March 2026 on real tasks (`/can-i-help` and `/onboard` against [glide-mq](https://github.com/avifenesh/glide-mq)), measured with `claude -p --output-format json`. Models: Claude Opus 4 and Claude Sonnet 4.
### Sonnet + AgentSys vs raw Opus
Same task, same repo, same prompt ("I want to improve docs"):
| Configuration | Cost | Output tokens | Result quality |
|---------------|------|---------------|----------------|
| Opus, no agentsys | $1.10 | 2,841 | Generic recommendations, no project-specific context |
| Opus + agentsys | $1.95 | 5,879 | Specific recommendations with effort estimates, convention awareness, breaking change detection |
| **Sonnet + agentsys** | **$0.66** | **6,084** | **Comparable to Opus + agentsys: specific, actionable, project-aware** |
Sonnet + agentsys produced more output with higher specificity than raw Opus - at 40% lower cost.
### With agentsys, model tier matters less
Once the pipeline provides structured prompts, enriched repo-intel data, and phase-gated workflows, the model does less heavy lifting. The gap between Sonnet and Opus narrows:
| Plugin | Opus | Sonnet | Savings |
|--------|------|--------|---------|
| /onboard | $1.10 | $0.30 | 73% |
| /can-i-help | $1.34 | $0.23 | 83% |
Both models reached the same outcome quality - Sonnet just costs less to get there. The structured pipeline captures most of the gains that would otherwise require a more expensive model.
### What this means
| Scenario | Model cost | Quality |
|----------|-----------|---------|
| Without agentsys | Need Opus for good results | Depends on model capability |
| **With agentsys** | **Sonnet is sufficient** | **Pipeline handles the structure, model handles judgment** |
The investment shifts from model spend to pipeline design. Better prompts, richer context, enforced phases - these compound in ways that model upgrades alone don't.
---
## Commands
| Command | What it does |
|---------|--------------|
| [`/next-task`](#next-task) | Task workflow: discovery, implementation, PR, merge |
| [`/prepare-delivery`](#prepare-delivery) | Pre-ship quality gates: deslop, review, validation, docs sync |
| [`/gate-and-ship`](#gate-and-ship) | Quality gates then ship (/prepare-delivery + /ship) |
| [`/agnix`](#agnix) | Lint agent configurations (399 rules) |
| [`/ship`](#ship) | PR creation, CI monitoring, merge |
| [`/deslop`](#deslop) | Clean AI slop patterns |
| [`/perf`](#perf) | Performance investigation with baselines and profiling |
| [`/drift-detect`](#drift-detect) | Compare plan vs implementation |
| [`/audit-project`](#audit-project) | Multi-agent iterative code review |
| [`/enhance`](#enhance) | Plugin, agent, and prompt analyzers |
| [`/repo-intel`](#repo-intel) | Unified static analysis - git history, AST symbols, project metadata |
| [`/sync-docs`](#sync-docs) | Sync documentation with code changes |
| [`/learn`](#learn) | Research topics, create learning guides |
| [`/consult`](#consult) | Cross-tool AI consultation |
| [`/debate`](#debate) | Structured debate between AI tools |
| [`/web-ctl`](#web-ctl) | Browser automation for AI agents |
| [`/release`](#release) | Versioned release with ecosystem detection |
| [`/skillers`](#skillers) | Workflow pattern learning and automation |
| [`/onboard`](#onboard) | Codebase orientation for newcomers |
| [`/can-i-help`](#can-i-help) | Match contributor skills to project needs |
Each command works standalone. Together, they compose into end-to-end pipelines.
---
## Skills
41 skills included across the plugins:
| Category | Skills |
|----------|--------|
| **Workflow** | `discover-tasks`, `prepare-delivery`, `check-test-coverage`, `orchestrate-review`, `validate-delivery` |
| **Message Queues** | `glide-mq-migrate-bee`, `glide-mq-migrate-bullmq`, `glide-mq` |
| **Enhancement** | `enhance-agent-prompts`, `enhance-claude-memory`, `enhance-cross-file`, `enhance-docs`, `enhance-hooks`, `enhance-orchestrator`, `enhance-plugins`, `enhance-prompts`, `enhance-skills` |
| **Performance** | `baseline`, `benchmark`, `code-paths`, `investigation-logger`, `perf-analyzer`, `profile`, `theory-gatherer`, `theory-tester` |
| **Cleanup** | `deslop`, `sync-docs` |
| **Code Review** | `audit-project` |
| **AI Collaboration** | `consult`, `debate`, `learn`, `recommend`, `skillers-compact` |
| **Onboarding** | `can-i-help`, `onboard` |
| **Web** | `web-auth`, `web-browse` |
| **Release** | `release` |
| **Analysis** | `drift-analysis`, `repo-intel` |
| **Linting** | `agnix` |
**External skill plugins** (standalone repos, installed separately):
| Category | Skills | Plugin |
|----------|--------|--------|
| **Message Queues** | `glide-mq`, `glide-mq-migrate-bullmq`, `glide-mq-migrate-bee` | [agent-sh/glidemq](https://github.com/agent-sh/glidemq) |
Skills are the reusable implementation units. Agents invoke skills; commands orchestrate agents. When you install a plugin, its skills become available to all agents in that session.
---
## Quick Navigation
| Section | What's there |
|---------|--------------|
| [The Approach](#the-approach) | Why it's built this way |
| [Benchmarks](#benchmarks) | Sonnet + agentsys vs raw Opus |
| [Commands](#commands) | All 20 commands overview |
| [Skills](#skills) | 41 skills across plugins |
| [Skill-Only Plugins](#skill-only-plugins) | glide-mq and other non-command plugins |
| [Command Details](#command-details) | Deep dive into each command |
| [How Commands Work Together](#how-commands-work-together) | Standalone vs integrated |
| [Design Philosophy](#design-philosophy) | The thinking behind the architecture |
| [Installation](#installation) | Get started |
| [Research & Testing](#research--testing) | What went into building this |
| [Documentation](#documentation) | Links to detailed docs |
---
## Skill-Only Plugins
Plugins that provide skills without a `/` command. Installed alongside agentsys; skills become available to all agents.
### glide-mq
Build message queues, background jobs, and workflow orchestration with [glide-mq](https://github.com/avifenesh/glide-mq) - high-performance Node.js queue on Valkey/Redis.
| Skill | What it does |
|-------|--------------|
| `glide-mq` | Greenfield queue development - queues, workers, ordering, rate limiting, flows, broadcast, step jobs |
| `glide-mq-migrate-bullmq` | Migrate from BullMQ to glide-mq - API mapping, breaking changes, feature comparison |
| `glide-mq-migrate-bee` | Migrate from Bee-Queue to glide-mq - API mapping, pattern conversion |
Key features: per-key ordering, group concurrency, runtime group rate limiting (`job.rateLimitGroup()`), token bucket, DAG workflows, broadcast pub/sub, step jobs, deduplication, serverless producers.
[Skill plugin →](https://github.com/agent-sh/glidemq) | [glide-mq docs →](https://avifenesh.github.io/glide-mq.dev/) | [npm →](https://www.npmjs.com/package/glide-mq)
---
## Command Details
### /next-task
**Purpose:** Complete task-to-production automation.
**What happens when you run it:**
1. **Policy Selection** - Choose task source (GitHub Issues, GitHub Projects, GitLab, local file), priority filter, stopping point
2. **Task Discovery** - Shows top 5 prioritized tasks, you pick one
3. **Worktree Setup** - Creates isolated branch and working directory
4. **Exploration** - Deep codebase analysis to understand context
5. **Planning** - Designs implementation approach
6. **User Approval** - You review and approve the plan (last human interaction)
7. **Implementation** - Executes the plan
8. **Pre-Review** - Runs [deslop](#deslop)-agent and prepare-delivery:test-coverage-checker
9. **Review Loop** - Multi-agent review iterates until clean
10. **Delivery Validation** - Verifies tests pass, build passes, requirements met
11. **Docs Update** - Updates CHANGELOG and related documentation
12. **[Ship](#ship)** - Creates PR, monitors CI, addresses comments, merges
Phase 9 uses the `orchestrate-review` skill to spawn parallel reviewers (code quality, security, performance, test coverage) plus conditional specialists.
**Agents involved:**
| Agent | Model | Role |
|-------|-------|------|
| task-discoverer | sonnet | Finds and ranks tasks from your source |
| worktree-manager | haiku | Creates git worktrees and branches |
| exploration-agent | sonnet | Deep codebase analysis before planning |
| planning-agent | opus | Designs step-by-step implementation plan |
| implementation-agent | opus | Writes the actual code |
| prepare-delivery:test-coverage-checker | sonnet | Validates tests exist and are meaningful |
| prepare-delivery:delivery-validator | sonnet | Final checks before shipping |
| ci-monitor | haiku | Watches CI status |
| ci-fixer | sonnet | Fixes CI failures and review comments |
| simple-fixer | haiku | Executes mechanical edits |
**Cross-plugin agent:**
| Agent | Plugin | Role |
|-------|--------|------|
| deslop-agent | deslop | Removes AI artifacts before review |
| sync-docs-agent | sync-docs | Updates documentation |
**Usage:**
```bash
/next-task # Start new workflow
/next-task --resume # Resume interrupted workflow
/next-task --status # Check current state
/next-task --abort # Cancel and cleanup
```
[Full workflow documentation →](./docs/workflows/NEXT-TASK.md)
---
### /prepare-delivery
**Purpose:** Run all pre-ship quality gates without shipping. Use after completing implementation manually or outside `/next-task`.
**What it runs (in order):**
1. **Pre-review gates** (parallel) - deslop + /simplify + prepare-delivery:test-coverage-checker
2. **Config lint** (conditional) - agnix + /enhance when changes touch agent/skill/plugin files
3. **Review loop** - 4 core reviewers + conditional specialists, max 5 iterations
4. **Delivery validation** - tests pass, build passes, requirements met
5. **Docs sync** - sync-docs agent updates documentation
```bash
/prepare-delivery # Run all quality gates
/prepare-delivery --skip-review # Skip review loop
/prepare-delivery --skip-docs # Skip docs sync
/prepare-delivery --base=develop # Against a specific base branch
```
Does NOT create PRs or push - use `/ship` or `/gate-and-ship` after.
---
### /gate-and-ship
**Purpose:** Quality gates then ship in one command. Chains `/prepare-delivery` then `/ship`.
```bash
/gate-and-ship # Full: quality gates + ship
/gate-and-ship --skip-review # Skip review, still ship
/ga
---
Source: [Claudary](https://claudary.paisolsolutions.com/skills/readme-17) · https://claudary.paisolsolutions.com