| 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 ←── 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

╔══════════════════════════════════════════════════════════╗
║              🚌 Agent Skill Bus Dashboard               ║
╚═══════════════════════════════════════════════════════╝

📊 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 dependsOn other 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
}

Full documentation →

🔄 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).

Full documentation →

👁️ 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:

1. Assess impact on existing skills
2. Generate a Prompt Request with severity rating
3. Route to Self-Improving Skills or human reviewer

Full documentation →

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

┌─────────────────────────────────────────────────────┐
│                  Agent Skill Bus                     │
│                                                     │
│  ┌──────────────┐  ┌──────────────┐  ┌───────────┐ │
│  │   Knowledge   │  │    Prompt    │  │   Self-   │ │
│  │   Watcher     │──│   Request    │──│ Improving │ │
│  │   (detect)    │  │   Bus (route)│  │  (repair) │ │
│  └──────────────┘  └──────────────┘  └───────────┘ │
│         │                  │                │       │
                                               : 0¶»§q«^