agentsys

A modular runtime and orchestration system for AI agents.

11 plugins · 5 agents · 14 skills (across all repos) · 13k lines of lib code · tests on Linux and Windows · 5 platforms Plugins distributed as standalone repos under agent-sh org - agentsys is the marketplace & installer

Running open models for your company? Tiyuvta helps with model choice, deployment and optimization, and fine-tuning on your hardware or cloud account.

Commands · Installation · Website · Discussions

Built for Claude Code · Codex CLI · OpenCode · Cursor · Kiro

AI models can write code. That's not the hard part anymore. The hard part is everything around it - branch management, code review, artifact cleanup, CI, PR comments. 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 is the CLI + LSP linter that catches config errors before they fail silently - real-time IDE validation, auto suggestions, auto-fix, and 757 rules for Claude Code, Codex, OpenCode, Cursor, Kiro, Copilot, Gemini CLI, Cline, Windsurf, Roo Code, Amp, and more.

What's New in 6.1.1

• OpenCode, Codex, Cursor and Kiro installs get the plugins that ship no .claude-plugin/plugin.json at their pin. The installer fetched such a plugin and installed nothing from it. It now makes one from the marketplace entry, as Claude Code does.
• agnix installs its /agnix command, agent and skill on every platform. Claude Code installed only its skill before, and the other platforms nothing. The marketplace entry is now a git-subdir source for the plugin's plugin/ folder.
• The installer fetches a git-subdir source as that folder, pinned by sha or commit.
• Contributor docs: every change reaches main through a PR, and a release commit merges through a PR with only the tag pushed.

What This Is

An agent orchestration system - 11 plugins, 5 agents, and 14 skills that compose into structured pipelines for software development. Each plugin lives in its own standalone repo under the 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: certainty-graded findings throughout

Certainty levels exist because not all findings are equal:

This came from testing on 1,000+ repositories.

Commands

Each command works standalone. Together, they compose into end-to-end pipelines.

Skills

14 skills included across the plugins:

External skill plugins (standalone repos, installed separately):

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

Skill-Only Plugins

Plugins without a / command. The marketplace ones install with agentsys; glide-mq installs from its own repo.

glide-mq

Build message queues, background jobs, and workflow orchestration with glide-mq - high-performance Node.js queue on Valkey/Redis. Not in the agentsys marketplace: install its skills from agent-sh/glidemq.

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 → | glide-mq docs → | npm →

Command Details

/foreman

Purpose: Work a repo's GitHub issues as a task queue. One main session picks the next ready issue, claims it, and hands it to a worktree subagent. Revuto reviews the PR, and foreman lands it at the exact head it checked. It replaces next-task and tower-crane.

There is no daemon and no state store. Labels, assignees and comments on GitHub are the state, and every command reads GitHub live, so several sessions can share one queue.

The loop:

1. Capacity, pick, claim - checks load, CPU pressure and memory, takes the next ready issue (urgent first, then oldest, open dependencies skipped) and claims it
2. Brief and spawn - one worktree subagent per issue from its brief, in parallel up to agents_max
3. Review - reads Revuto's findings; every real finding gets fixed, a wrong one gets a reply. When Revuto is capped or down, the session reviews the diff itself and records it
4. Land - every check green at the head, no unaddressed finding, the repo's own land command, then a squash merge pinned to that head and the issue closed

/foreman                    # Run the queue until nothing is ready
/foreman status             # Counts by label, claims and capacity
/foreman stats              # Revuto caps, self-reviews, manual review requests
/foreman 123                # Work issue #123 only, from claim to land

Config: optional .foreman.json in the repo root: label names, agents_max, load and memory holds, the check and land commands, required checks, Revuto round cap, forbidden words for posted comments.

Requires: Node 22+, gh (logged in), git and waitfor. run-long needs systemd user units (Linux).

Skill: foreman (no agents: workers are native worktree subagents)

Full workflow documentation → · Repo →

/banthis

Purpose: Durable negative memory for repeated agent mistakes. Turn a user's "stop doing this" correction into a persistent rule in CLAUDE.md or AGENTS.md.