agent-skill-bus
The missing runtime for Agent Skills — health monitoring, self-improvement, and dependency management for any AI agent framework. Zero dependencies.
🚌 Agent Skill Bus
The missing runtime for Agent Skills — health monitoring, self-improvement, and dependency management for any AI agent framework.
Your agent skills silently break. Agent Skill Bus detects it, diagnoses the root cause, and fixes it automatically.
Built by åˆåŒä¼šç¤¾ã¿ã‚„ã³ (LLC Miyabi) — Running 42 AI agents in production daily.
Looking for the full ecosystem? This repo is the core runtime. For 110+ production-ready skills, marketplace, and the complete Miyabi Agent Society platform, visit agentskills.bath.me.
What is this?
Agent Skill Bus is a framework-agnostic runtime for AI agent skill health — orchestrating, monitoring, and self-improving agent skills across any framework. Think of it as the operational backbone that keeps your agent skills healthy over weeks and months, not just during a single run. It consists of three integrated modules:
| Module | Purpose | Standalone? |
|---|---|---|
| Prompt Request Bus | DAG-based task queue with dependency resolution & file locking | ✅ Yes |
| Self-Improving Skills | Automatic skill quality monitoring & repair loop | ✅ Yes |
| Knowledge Watcher | External change detection → automatic improvement triggers | ✅ Yes |
They work independently, but together they form a closed-loop self-improving agent system:
External Changes ──→ Knowledge Watcher ──→ Prompt Request Bus ──→ Execute
↑ │
│ ↓
Self-Improving â†â”€â”€ Skill Runs Log
Skills
Why?
Most agent frameworks handle execution (LangGraph, CrewAI, AutoGen). None handle operational health:
- 🔴 Skills silently degrade — An API changes, a model updates, auth expires. Nobody notices until it fails in production.
- 🔴 Tasks collide — Two agents edit the same file simultaneously. Data corruption.
- 🔴 No dependency management — Complex tasks need A→B→C ordering. Most systems just run everything in parallel.
- 🔴 No learning loop — Failures repeat because there's no feedback mechanism.
Agent Skill Bus solves all four.
Quick Start
# One command to set up everything
npx agent-skill-bus init
# Log a skill execution
npx agent-skill-bus record-run --agent my-agent --skill api-caller --task "fetch data" --result success --score 1.0
# Check what needs attention
npx agent-skill-bus flagged
# Queue a task
npx agent-skill-bus enqueue --source human --priority high --agent dev --task "Fix auth bug"
# See what's ready to dispatch
npx agent-skill-bus dispatch
# See which data files this project is actually using
npx agent-skill-bus daths
For Claude Code / Codex users
Add this to your AGENTS.md:
After completing any task, log the result:
npx agent-skill-bus record-run --agent claude --skill <skill-name> --task "<task>" --result <success|fail|partial> --score <0.0-1.0>
That's it. The self-improving loop runs automatically.
Dashboard
Get a real-time overview of all your agent skills with a single command:
npx agent-skill-bus dashboard
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║ 🚌 Agent Skill Bus Dashboard ║
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📊 Queue: 3 queued │ 1 running │ 12 completed │ 0 failed
Status Skill Score Trend Health
────────────────────────────────────────────────────────
◠ALERT api-caller 0.42 ↓ ██░░░░░░░░░░
◠OK code-review 0.95 ↑ ███████████░
◠OK deploy-pipeline 0.88 ─ ██████████░░
âš Flagged Skills:
api-caller — score_drop: dropped from 0.91 to 0.42 (drift: -53.8%)
Options: --days N (default: 7), --no-color for CI/piping.
Modules
📬 Prompt Request Bus
A JSONL-based task queue with:
- DAG dependency resolution — Tasks specify
dependsOnother tasks. Automatic topological execution. - File-level locking — Prevent two agents from editing the same file. TTL-based deadlock prevention.
- Priority routing —
critical > high > medium > low. Critical tasks bypass the queue. - Multi-source ingestion — Human commands, cron jobs, GitHub webhooks, internal triggers all use the same format.
- Deduplication — Same task won't be queued twice.
{
"id": "pr-001",
"ts": "2026-03-18T08:00:00Z",
"source": "human",
"priority": "high",
"agent": "dev-agent",
"task": "Fix authentication bug in auth.ts",
"status": "queued",
"dependsOn": [],
"affectedFiles": ["myapp:src/auth.ts"],
"dagId": null
}
🔄 Self-Improving Skills
A 7-step quality loop inspired by Cognee's self-improving agents:
OBSERVE → ANALYZE → DIAGNOSE → PROPOSE → EVALUATE → APPLY → RECORD
- Automatic failure detection — Score drops, trend analysis, consecutive failure alerts.
- LLM-powered diagnosis — Reads the failing skill + error logs, identifies root cause.
- Safe auto-repair — Low-risk fixes applied automatically. High-risk changes need human approval.
- Drift detection — Catches silent degradation (score drops >15% week-over-week).
ðŸ‘ï¸ Knowledge Watcher
Monitors external changes and triggers improvement requests:
- Tier 1 (every check): Dependency versions, API changes, config drift
- Tier 2 (daily): Community patterns, user feedback, platform changes
- Tier 3 (weekly): Industry trends, competitor releases, best practice updates
When a change is detected:
- Assess impact on existing skills
- Generate a Prompt Request with severity rating
- Route to Self-Improving Skills or human reviewer
Documentation
| Guide | Description |
|---|---|
| Architecture Deep Dive | System design, JSONL data layer, DAG scheduling, file locking |
| Self-Improving Skills | The 7-step quality loop, drift detection, auto-repair |
| Knowledge Watcher | Three-tier monitoring, change detection, impact assessment |
| Integration Guide | Claude Code, Codex, LangGraph, CrewAI, CI/CD setup |
| Framework Comparison | Feature matrix vs. LangGraph, CrewAI, AutoGen, Mastra, VoltAgent |
Architecture
┌─────────────────────────────────────────────────────â”
│ Agent Skill Bus │
│ │
│ ┌──────────────┠┌──────────────┠┌───────────┠│
│ │ Knowledge │ │ Prompt │ │ Self- │ │
│ │ Watcher │──│ Request │──│ Improving │ │
│ │ (detect) │ │ Bus (route)│ │ (repair) │ │
│ └──────────────┘ └──────────────┘ └───────────┘ │
│ │ │ │ │
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