agentsys

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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.

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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.

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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:

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.

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Commands

Command	What it does
/foreman	Work the GitHub issue queue with native subagents, from pick to land
/banthis	Durable negative memory: persist banned agent behaviors
/agnix	Lint agent configurations (757 rules)
/deslop	Clean AI slop patterns
/learn	Research topics, create learning guides
/skillers	Workflow pattern learning and automation
/skill-curator	Create and improve reliable SKILL.md files
/system-prompt-curator	Create and improve autonomous agent system prompts

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

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Skills

14 skills included across the plugins:

Category	Skills
Workflow	foreman
Message Queues	glide-mq-migrate-bee, glide-mq-migrate-bullmq, glide-mq
Enhancement	skill-curator, system-prompt-curator
Cleanup	deslop
AI Collaboration	learn, recommend, skillers-compact
Memory	banthis
Linting	agnix
Languages	ada-spark, mojo

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

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.

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Quick Navigation

Section	What's there
The Approach	Why it's built this way
Commands	Overview of every command
Skills	14 skills across plugins
Skill-Only Plugins	glide-mq and other non-command plugins
Command Details	Deep dive into each command
How Commands Work Together	Standalone vs integrated
Design Philosophy	The thinking behind the architecture
Installation	Get started
Research & Testing	What went into building this
Documentation	Links to detailed docs

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Skill-Only Plugins

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

Plugin	What it does
mojo	mojo skill: current Mojo syntax, ownership, SIMD and GPU kernels, Python interop
ada-spark	ada-spark skill: Ada 2022 and SPARK contracts, proof, Alire and GNAT tooling
zig-lsp	Zig language server for Claude Code via ZLS (no skill). Needs zls on PATH

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.

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 → | glide-mq docs → | npm →

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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 →

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/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.

banthis is a tiny standalone CLI plus skill. It maintains a managed banned-behaviors section, supports project or global targets, and includes an init meta-rule so agents learn when to invoke it automatically.

What it does:

Command	Use
banthis add "<title>" "<rule>"	Add or update a banned behavior
banthis list	List current bans
banthis show	Print the managed section
banthis remove "<title>"	Remove a ban
banthis init	Install the meta-rule that teaches agents to call banthis

Usage:

/banthis "stop ending with vague optional follow-up offers"
banthis add "No vague endings" "Do not end with vague optional follow-up offers."
banthis init --file AGENTS.md

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/agnix

Purpose: Lint agent configurations before they break your workflow. The first dedicated linter for AI agent configs.

agnix is a standalone open-source project that provides the validation engine. This plugin integrates it into your workflow.

The problem it solves:

Agent configurations are code. They affect behavior, security, and reliability. But unlike application code, they have no linting. You find out your SKILL.md is malformed when the agent fails. You discover your hooks have security issues when they're exploited. You realize your CLAUDE.md has conflicting rules when the AI behaves unexpectedly.

agnix catches these issues before they cause problems.

What it validates:

Category	What It Checks
Structure	Required fields, valid YAML/JSON, proper frontmatter
Security	Prompt injection vectors, overpermissive tools, exposed secrets
Consistency	Conflicting rules, duplicate definitions, broken references
Best Practices	Tool restrictions, model selection, trigger phrase quality
Cross-Platform	Compatibility across Claude Code, Codex, OpenCode, Cursor, Kiro, Copilot, Gemini CLI, Cline, Windsurf, Roo Code, Amp, and more

757 validation rules derived from:

• Official tool specifications (Claude Code, Codex CLI, OpenCode, Cursor, Kiro, GitHub Copilot, Gemini CLI, Cline, Windsurf, Roo Code, Amp, and more)
• Research papers on agent reliability and prompt injection
• Real-world testing across 500+ repositories
• Community-reported issues and edge cases

Supported files:

File Type	Examples
Skills	SKILL.md, */SKILL.md
Memory	CLAUDE.md, AGENTS.md, .github/CLAUDE.md
Hooks	.claude/settings.json, hooks configuration
MCP	*.mcp.json, MCP server configs
Cursor	.cursor/rules/*.mdc, .cursorrules
Copilot	.github/copilot-instructions.md
Kiro	.kiro/steering/**/*.md, .kiro/agents/*.json, .kiro/hooks/*.kiro.hook, POWER.md
Windsurf	.windsurf/rules/**/*.md, .windsurf/workflows/**/*.md, .windsurfrules
Roo Code	.roo/rules/*.md, .roo/rules-{mode}/*.md, .roomodes, .rooignore, .roorules
Gemini CLI	GEMINI.md, .gemini/settings.json, gemini-extension.json
OpenCode	opencode.json
Amp	.agents/checks/**/*.md, .amp/settings.json

CI/CD Integration:

agnix outputs SARIF format for GitHub Code Scanning. Add it to your workflow:

- name: Lint agent configs
  run: agnix --format sarif > results.sarif
- uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: results.sarif

Usage:

/agnix                       # Validate current project
/agnix --fix                 # Auto-fix fixable issues
/agnix --strict              # Treat warnings as errors
/agnix --target claude-code  # Only Claude Code rules
/agnix --format sarif        # Output for GitHub Code Scanning

Agent: agnix-agent (sonnet model)

External tool: Requires agnix CLI

npm install -g agnix         # Install via npm
# or
cargo install agnix-cli      # Install via Cargo
# or
brew tap agent-sh/agnix && brew install agnix  # Install via Homebrew

Why use agnix:

• Catch config errors before they cause agent failures
• Enforce security best practices across your team
• Maintain consistency as your agent configs grow
• Integrate validation into CI/CD pipelines
• Support multiple AI tools from one linter

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/deslop

Purpose: Finds AI slop - debug statements, placeholder text, verbose comments, TODOs - and removes it.

How detection works:

Three phases run in sequence:

1. Phase 1: Regex Patterns (HIGH certainty)

   • console.log, print(), dbg!(), println!()
   • // TODO, // FIXME, // HACK
   • Empty catch blocks, disabled linters
   • Hardcoded secrets (API keys, tokens)

2. Phase 2: Multi-Pass Analyzers (MEDIUM certainty)

   • Doc-to-code ratio (excessive comments)
   • Verbosity ratio (AI preambles)
   • Over-engineering patterns
   • Buzzword inflation
   • Dead code detection
   • Stub functions

3. Phase 3: CLI Tools (LOW certainty, optional)

   • jscpd, madge, escomplex (JS/TS)
   • pylint, radon (Python)
   • golangci-lint (Go)
   • clippy (Rust)

Languages supported: JavaScript/TypeScript, Python, Rust, Go, Java

Usage:

/deslop              # Report only (safe)
/deslop apply        # Fix HIGH certainty issues
/deslop apply src/    # Fix issues in src/ only

Thoroughness levels:

• quick - Phase 1 only (fastest)
• normal - Phase 1 + Phase 2 (default)
• deep - All phases if tools available

Pattern reference →

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/learn

Purpose: Research any topic online and create a comprehensive learning guide with RAG-optimized indexes.

What it does:

1. Progressive Discovery - Uses funnel approach (broad → specific → deep) to find quality sources
2. Quality Scoring - Scores sources by authority, recency, depth, examples, uniqueness
3. Just-In-Time Extraction - Fetches only high-scoring sources to save tokens
4. Synthesis - Creates structured learning guide with examples and best practices
5. RAG Index - Updates the agent-knowledge/AGENTS.md master index for future lookups

Depth levels:

Depth	Sources	Use Case
brief	10	Quick overview
medium	20	Default, balanced
deep	40	Comprehensive

Output structure:

agent-knowledge/
  AGENTS.md                    # Master index (updated each run)
  recursion.md                 # Topic-specific guide
  resources/
    recursion-sources.json     # Source metadata with quality scores

Usage:

/learn recursion                    # Default (20 sources)
/learn react hooks --depth=deep     # Comprehensive (40 sources)
/learn kubernetes --depth=brief     # Quick overview (10 sources)

A CLAUDE.md mirror of the index is written only with --legacy-claude-index.

Agent: learn-agent (sonnet model)

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/skillers

Learn from your workflow patterns and suggest automations

/skillers show              # Display current config and knowledge stats
/skillers compact           # Analyze recent transcripts, extract patterns
/skillers compact --days=14 # Analyze older transcripts
/skillers recommend         # Get automation suggestions from accumulated knowledge

Reads your Claude Code, Codex and OpenCode transcripts, identifies recurring patterns (pain points, repeated workflows, wishes), clusters them into weighted themes, and suggests skills, hooks, or agents to automate them.

No per-turn overhead - it reads transcripts your tools already save.

Agents: skillers-compactor (sonnet), skillers-recommender (opus)

Skills: skillers-compact, recommend

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/skill-curator

Create and improve reliable SKILL.md files

/skill-curator "create a skill for reviewing background jobs"
/skill-curator --improve path/to/SKILL.md --category review

The skill curator focuses on trigger quality, clear scope, router patterns, concrete Skip unless: gates, token budgets, and agnix-ready structure across Claude Code, Codex, OpenCode, Cursor, Kiro, and similar tools.

Skill: skill-curator

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/system-prompt-curator

Create and improve autonomous coding-agent system prompts

/system-prompt-curator "GitHub issue resolver"
/system-prompt-curator --improve path/to/prompt.md --for-orchestrator

The system prompt curator rewrites prompts around task-matched identity, phased workflow, explicit tools, evidence-based completion criteria, and realistic error recovery examples. It separates prompt guidance from harness-level checks that belong in code.

Skill: system-prompt-curator

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How Commands Work Together

Standalone use:

/deslop apply          # Just clean up your code
/agnix                 # Just lint your agent configs
/foreman               # Work the issue queue, one worktree agent per issue
/learn react hooks     # Just research a topic

Together:

/foreman picks an issue and hands it to a worktree worker. The worker can run /deslop on its diff and /agnix on any agent config it touched before it reports back. foreman then reads Revuto's findings and lands the PR only when every check is green at the head it checked.

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Design Philosophy

The Actual Problem

Frontier models write good code. That's solved. What's not solved:

• Context management - Models forget what they're doing mid-session
• Compaction amnesia - Long sessions get summarized, losing critical state
• Task drift - Without structure, agents wander from the actual goal
• Skipped steps - Agents skip reviews, tests, or cleanup when not enforced
• Token waste - Using LLM calls for work that static analysis can do faster
• Babysitting - Manually orchestrating each phase of development
• Repetitive requests - Asking for the same workflow every single session

How This Addresses It

1. One agent, one job, done extremely well

Same principle as good code: single responsibility. The deslop-agent cleans. The learn-agent researches. The agnix-agent lints agent configs. No agent tries to do everything. Specialized agents, each with narrow scope and clear success criteria.

2. Pipeline with gates, not a monolith

Same principle as DevOps. Each step must pass before the next begins. Can't push before review. Can't merge before CI passes. Hooks enforce this - agents literally cannot skip phases.

3. Tools do tool work, agents do agent work

If static analysis, regex, or a shell command can do it, don't ask an LLM. Pattern detection uses pre-indexed regex. File discovery uses glob. Platform detection uses file existence checks. The LLM only handles what requires judgment.

4. Agents don't need to know how tools work

The slop detector returns findings with certainty levels. The agent doesn't need to understand the three-phase pipeline, the regex patterns, or the analyzer heuristics. Good tool design means the consumer doesn't need implementation details.

5. Build tools where tools don't exist

Many tasks lack existing tools. Multi-pass analyzers for slop detection. A rule engine for agent configs. The result: agents receive structured data, not raw problems to figure out.

6. Research-backed prompt engineering

Documented techniques that measurably improve results:

• Progressive disclosure - Agents see only what's needed for the current step
• Structured output - JSON between delimiters, XML tags for sections
• Explicit constraints - What agents MUST NOT do matters as much as what they do
• Few-shot examples - Where patterns aren't obvious
• Tool calling over generation - Let the model use tools rather than generate tool-like output

7. Validate plan and results, not every step

Approve the plan. See the results. The middle is automated. /foreman picks, briefs, reads the review and lands without a prompt at each step.

8. Right model for the task

Match model capability to task complexity:

• opus - Planning, implementation, review orchestration
• sonnet - Pattern matching, validation, discovery
• haiku - Git operations, file moves, CI polling

Quality compounds. Poor exploration → poor plan → poor implementation → review cycles. Early phases deserve the best model.

9. Persistent state survives sessions

State files track the workflow phase. Sessions can die and resume. Multiple sessions run in parallel in separate worktrees.

10. Delegate everything automatable

Agents don't just write code. They:

• Clean their own output (deslop-agent)
• Work the issue queue from pick to land (/foreman)
• Lint their own configs (/agnix)
• Research what they need to know (/learn)

If it can be specified, it can be delegated.

11. Orchestrator stays high-level

The main workflow orchestrator doesn't read files, search code, or write implementations. It launches specialized agents and receives their outputs. Keeps the orchestrator's context window available for coordination rather than filled with file contents.

12. Composable, not monolithic

Every command works standalone. /deslop cleans code without needing /foreman. /agnix lints configs without needing the full workflow. Pieces compose together, but each piece is useful on its own.

What This Gets You

• Run multiple sessions - Different tasks in different worktrees, no interference
• Fast iteration - Approve plan, check results, repeat
• Stay in the interesting parts - Policy decisions, architecture choices, edge cases
• Minimal review burden - Most issues caught and fixed before you see the output
• No repetitive requests - The workflow you want, without asking each time
• Scale horizontally - More sessions, more tasks, same oversight level

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Installation

Claude Code (Recommended way)

/plugin marketplace add agent-sh/agentsys
/plugin install foreman@agentsys

All Platforms (npm)

npm install -g agentsys && agentsys

Interactive installer for Claude Code, OpenCode, Codex CLI, Cursor, and Kiro.

# Non-interactive install
agentsys --tool claude              # Single tool
agentsys --tool cursor              # Cursor (~/.cursor: skills, commands, rules)
agentsys --tool kiro                # Kiro (~/.kiro: prompts, skills, agents)
agentsys --tools "claude,opencode"  # Multiple tools
agentsys --development              # Dev mode (bypasses marketplace)
agentsys install deslop             # One plugin, or plugin:component for one agent, skill or command

Full installation guide →

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Requirements

Required:

• Git
• Node.js 18+

For GitHub workflows:

• GitHub CLI (gh) authenticated

For GitLab workflows:

• GitLab CLI (glab) authenticated

For repo-intel data (read by deslop):

• agent-analyzer, downloaded automatically on first use

For /foreman:

• Node.js 22+, gh authenticated, and waitfor on PATH

For /agnix:

• agnix CLI installed (npm install -g agnix, cargo install agnix-cli, or brew tap agent-sh/agnix && brew install agnix)

Local diagnostics (optional):

npm run detect   # Platform detection (CI, deploy, project type)
npm run verify   # Tool availability + versions

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Research & Testing

The system is built on research, not guesswork.

Knowledge base (agent-docs/): 8,000 lines of curated documentation from Anthropic, OpenAI, Google, and Microsoft covering:

• Agent architecture and design patterns
• Prompt engineering techniques
• Function calling and tool use
• Context efficiency and token optimization
• Multi-agent systems and orchestration
• Instruction following reliability

Testing:

• Jest suite runs on Linux and Windows in CI
• Cross-platform validation (Claude Code, OpenCode, Codex CLI, Cursor, Kiro)

Methodology:

• Certainty levels derived from pattern analysis across repositories

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Documentation

Topic	Link
Installation	docs/INSTALLATION.md
Cross-Platform Setup	docs/CROSS_PLATFORM.md
Usage Examples	docs/USAGE.md
Architecture	docs/ARCHITECTURE.md

Workflow Deep-Dives

Workflow	Link
/foreman Flow	docs/workflows/FOREMAN.md

Reference

Topic	Link
Slop Patterns	docs/reference/SLOP-PATTERNS.md
Agent Reference	docs/reference/AGENTS.md

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Support

• Issues: github.com/agent-sh/agentsys/issues
• Discussions: github.com/agent-sh/agentsys/discussions

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MIT License | Made by Avi Fenesh

Contributor instructions

Repository instructions live in AGENTS.md. Claude Code v2.1.281 or later supports native loading across supported providers. If an ancestor instruction file masks AGENTS.md, select claude-md-and-agents-md in user-level Project instructions. The platform helpers retain support for analyzing older instruction layouts; they do not control the native CLI loader. See Claude Code instruction loading.