BytesAgain is a curated directory of 60,000+ AI agent skills from ClawHub, GitHub, LobeHub, and Dify. Search skills by keyword in 7 languages, browse by role (developer, creator, trader, marketer) or by use case.

How do I find AI skills on BytesAgain?

Use the search bar on BytesAgain.com to search by keyword in 7 languages. You can also browse by role (developer, creator, trader, marketer) or by use case. Each skill shows install instructions for Claude, Cursor, OpenClaw, Continue, and more.

Yes, BytesAgain is completely free. No registration required for searching skills. The MCP API is also free with rate limits.

Does BytesAgain have an API for AI agents?

Yes! BytesAgain provides a free MCP SSE endpoint at /api/mcp/sse for AI agents, plus a REST API at /api/mcp?action=search&q= . No authentication needed.

Can I request a new AI skill on BytesAgain?

Yes! Visit the Requests page on BytesAgain.com to submit a skill request. Your request will be visible to the community and notified to the site admin.

🦀 ClawHub

Epic AI Swarm Orchestration

by @linkbag

Production playbook and portable runtime for parallel AI coding swarms using Codex, Gemini, DeepSeek, and optional Claude. Use when orchestrating multi-agent...

Versionv3.3.1

Downloads681

#crypto-defi #security #finance #legal

View on ClawHub →

TERMINAL

clawhub install epic-ai-swarm-orchestration

📖 About This Skill

name: epic-ai-swarm-orchestration description: Production playbook for running parallel AI coding agents (Claude, Codex, Gemini) with automatic model selection via duty table, token-limit auto-fallback, human oversight, quality gates, and automated integration. Use when orchestrating multi-agent coding swarms, spawning parallel builders with review loops, managing model availability/rotation across vendors, or integrating branches from multiple AI agents. Triggers on phrases like "run the swarm", "spawn agents", "AI swarm", "multi-agent build", "duty table", "model rotation", "parallel coding agents".

Epic AI Swarm Orchestration v3.2.1

Production system for running parallel AI coding agents with dynamic model selection, automatic token-limit failover, and quality gates.

Prerequisites

Required CLIs (on PATH)

tmux — agent sandboxing (each agent in its own session)

git — worktree creation, branching, commits, push

gh — GitHub CLI (authenticated via gh auth login)

python3 — JSON manipulation (no pip packages)

openclaw — notification delivery (Telegram/other)

Model CLIs (at least one, authenticated)

claude — Anthropic CLI (OAuth or API key)

codex — OpenAI Codex CLI (optional)

gemini — Google Gemini CLI (optional)

Scripts use host-authenticated CLIs — they do not store credentials.

Quick Start

1. Copy scripts/ to ~/workspace/swarm/ 2. Edit scripts/swarm.conf with notification target 3. Run scripts/assess-models.sh to initialize the duty table 4. Read references/workflow.md for the 3-phase workflow 5. Read references/duty-table.md for model rotation system 6. Read references/tools.md for spawn commands

v3.2.1 Bookkeeping Fix

This release fixes a critical integration-watcher bookkeeping bug: spawn-batch.sh and queue-watcher.sh now record the actual tmux session emitted by spawn-agent.sh after duty-table/fallback resolution, instead of predicting names from the requested agent. integration-watcher.sh also refuses to treat unknown/misspelled expected sessions as complete.

Architecture Overview

┌─────────────────────────────────────────────────────────┐
│                    DUTY TABLE                           │
│  assess-models.sh → duty-table.json (daily cron)       │
│  architect=claude/opus, builder=codex, reviewer=gemini  │
└───────────┬─────────────────────────────┬───────────────┘
            │                             │
    ┌───────▼───────┐           ┌─────────▼────────┐
    │ spawn-agent.sh│           │ spawn-batch.sh   │
    │ (single task) │           │ (parallel tasks)  │
    └───────┬───────┘           └────────┬─────────┘
            │  Reads role → agent/model  │
            │  from duty-table.json      │
    ┌───────▼────────────────────────────▼───────────┐
    │              RUNNER (in tmux)                    │
    │  On token limit → model-fallback.sh             │
    │  Auto-retry up to 2x with next available model  │
    │  Updates duty table for future spawns            │
    └───────┬─────────────────────────────────────────┘
            │
    ┌───────▼───────────────────────┐
    │  notify-on-complete.sh        │
    │  → auto-spawns reviewer       │
    │  → integration-watcher.sh     │
    │  → ESR + work log persistence │
    └───────────────────────────────┘

Duty Table System

The duty table (duty-table.json) maps roles to agents/models:

| Role | Purpose | Default Assignment | |------|---------|-------------------| | architect | Planning, design | Claude Opus (best reasoning) | | builder | Implementation | Codex or Claude Sonnet (fast) | | reviewer | Code review + fixes | Gemini Flash or Sonnet | | integrator | Branch merging | Claude Opus (deep thinking) |

Auto-Assessment

assess-models.sh runs daily (or on-demand) to: 1. Test all models across all 3 vendors (45s timeout each) 2. Assign optimal 3-vendor spread to roles 3. If both Codex + Gemini down → fallback to all-Claude table

Mid-Run Token Failover

When an agent hits a token/rate limit during execution: 1. Runner detects the error pattern in output 2. Calls model-fallback.sh with the role + failed model 3. Gets the next available model from the per-role fallback chain 4. Retries the task (up to 2 attempts) 5. Updates duty table so future spawns use the working model 6. Logs the switch to pending-notifications.txt

See references/duty-table.md for full details.

Core Scripts

| Script | Purpose | |--------|---------| | spawn-agent.sh | Spawn single agent (resolves role from duty table) | | spawn-batch.sh | Spawn parallel agents with auto-queuing | | assess-models.sh | Test models, update duty table | | model-fallback.sh | Find next available model for a role | | fallback-swap.sh | Pre-spawn primary/fallback test | | try-model.sh | Quick model health check | | notify-on-complete.sh | Watcher: auto-review + integration | | integration-watcher.sh | Merge all branches after batch | | queue-watcher.sh | Auto-spawn queued overflow tasks | | pulse-check.sh | Detect stuck agents, auto-kill | | check-agents.sh | Monitor all active agents | | endorse-task.sh | Human endorsement gate | | esr-log.sh | Engineering Status Report logging | | daily-standup.sh | Daily status summary | | cleanup.sh | Remove old worktrees + logs |

Workflow

Phase 1: PLAN (Architect)

Read project context, ESR, codebase

Break work into parallel tasks with prompts

Present plan to human → HOLD until endorsed

Phase 2: BUILD + REVIEW (Builder + Reviewer)

spawn-batch.sh deploys agents in tmux + worktrees

Each agent codes autonomously with structured work log

notify-on-complete.sh auto-spawns reviewer (max 3 fix loops)

Token limits trigger automatic model switch mid-run

Phase 3: SHIP (Integrator)

integration-watcher.sh merges all branches sequentially

Conflict resolution, build verification

ESR + work log persisted to project history

Telegram notification with shipped summary

Configuration

swarm.conf:

SWARM_NOTIFY_TARGET=""
SWARM_NOTIFY_CHANNEL="telegram"
SWARM_MAX_CONCURRENT=8

Endorsement System

Every task requires human approval before agents spawn:

endorse-task.sh            # Single task
spawn-batch.sh ...      # Batch endorsement (auto per-task)

30-second cooldown between endorsement and spawn prevents accidental double-runs.

💡 Examples

⚙️ Configuration

swarm.conf:

SWARM_NOTIFY_TARGET=""
SWARM_NOTIFY_CHANNEL="telegram"
SWARM_MAX_CONCURRENT=8