smart-swarm
Automatically organize multi-agent teams for complex tasks.
Skill by Leo-Atienza
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: frontend, backend, productivity, agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/smart-swarm
What's inside
- Skill name
smart-swarm- Description
- Automatically organize multi-agent teams for complex tasks. Scores task complexity across 5 dimensions, selects appropriate agents from the Flow ecosystem, coordinates parallel work, and synthesizes results. Use when: a task involves multiple independent concerns (frontend + backend + tests), when speed matters and subtasks are parallelizable, or when user says 'use agents', 'swarm this', 'parallelize', or the task clearly benefits from multi-agent execution.
- License
- MIT
- Instructions
- 166 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Smart Swarm — Intelligent Multi-Agent Orchestration
Automatically detects when a task is complex enough for multi-agent execution, selects the right agents, coordinates parallel work, and synthesizes results.
## Task Complexity Scoring
Score each dimension 0-3, then sum for routing:
| Dimension | 0 | 1 | 2 | 3 |
|-----------|---|---|---|---|
| **File Scope** | 1-2 files | 3-5 files | 6-15 files | 16+ files |
| **Concern Count** | 1 concern | 2 concerns | 3-4 concerns | 5+ concerns |
| **Risk Level** | Low (cosmetic) | Medium (logic) | High (data/auth) | Critical (security/infra) |
| **Isolation** | Fully dependent | Mostly dependent | Mostly independent | Fully independent |
| **Time Pressure** | None | Implied | Explicit ("fast") | Blocking other work |
**Total Score → Routing**:
- **0-4 → SOLO**: Just do it. No agents needed.
- **5-7 → DUO**: 2 agents working in parallel (e.g., implementation + tests)
- **8-11 → TEAM**: 3-4 agents with a lightweight coordinator
- **12-15 → SWARM**: Full swarm with wave execution via `/flow:go --swarm`
## Agent Selection Matrix
Based on the task fingerprint (what concerns are involved), select agents:
| Task Fingerprint | Mode | Agents |
|---|---|---|
| **Feature + Tests** | DUO | `flow-executor`(feature) + `flow-executor`(tests) |
| **Frontend + Backend** | DUO/TEAM | `flow-executor`(frontend) + `flow-executor`(backend) + `flow-verifier` |
| **Bug + Root Cause** | DUO | `flow-debugger` + `flow-repo-analyst` |
| **Research + Implement** | TEAM | `flow-external-researcher` + `flow-repo-analyst` + `flow-executor` |
| **Multi-file Refactor** | TEAM | `flow-mapper` + N×`flow-executor` + `flow-verifier` |
| **Full Feature (deep)** | SWARM | `flow-planner` + 3×`flow-executor` + `flow-verifier` + `flow-uat` |
| **Code Review** | TEAM | Use CE-005 multi-agent review (already built for this) |
| **Discovery/Research** | TEAM | `flow-external-researcher` + `flow-repo-analyst` + `flow-learnings-researcher` + `flow-research-synthesizer` |
## Orchestration Protocol
### 1. Score & Route
Run the complexity scoring on the task. Announce the score and routing decision:
```
Complexity: FILE=2, CONCERNS=3, RISK=1, ISOLATION=2, TIME=1 → Total: 9 → TEAM mode
```
### 2. Decompose
Break the task into independent subtasks. Write each to a coordination file:
```
.flow/swarm-{timestamp}/
task-1.md # e.g., "Implement API endpoint for /users"
task-2.md # e.g., "Create React component for user list"
task-3.md # e.g., "Write integration tests for user flow"
```
Each task file contains:
- Task description (what to do)
- Input files (what to read)
- Output files (what to create/modify)
- Constraints (don't modify files assigned to other agents)
- Success criteria
### 3. Spawn Agents
Use the `Agent` tool to spawn each agent. Each agent gets:
- Its task file
- Project CLAUDE.md for context
- Instruction to write results to `result-{N}.md`
For DUO mode, spawn both agents in a single message (parallel).
For TEAM mode, spawn all agents in a single message.
For SWARM mode, delegate to `/flow:go --swarm` (already handles wave dependencies).
### 4. Collect Results
After agents complete:
- Read all `result-{N}.md` files
- Check for file conflicts (did two agents modify the same file?)
- If conflicts: present diff to user or use `flow-verifier` to auto-merge
### 5. Synthesize
Write `synthesis.md` summarizing:
- What each agent accomplished
- Any conflicts resolved
- Files created/modified
- Test results
- Remaining work (if any)
### 6. Clean Up
- Archive swarm directory if fully successful
- Leave it if there's remaining work for the user
## Tier Routing Integration
When spawning agents, apply `rules/tier-routing.md` model selection:
| Swarm Mode | Agent Role | Model Tier |
|------------|-----------|------------|
| **SOLO** | Single executor | Tier 2 (haiku) if trivial, Tier 3 (sonnet) otherwise |
| **DUO** | Test writer | Tier 2 (haiku) — pattern-based |
| **DUO** | Implementer | Tier 3 (sonnet) |
| **TEAM** | Coordinator | Tier 4 (opus) — needs reasoning |
| **TEAM** | Executors | Tier 3 (sonnet) |
| **TEAM** | Verifier | Tier 3 (sonnet) |
| **SWARM** | Planner | Tier 4 (opus) |
| **SWARM** | Executors | Tier 3 (sonnet) |
| **SWARM** | Verifier/UAT | Tier 3 (sonnet) |
Consult `logs/agent-profiles-summary.json` if available — prefer agents with reliability > 0.7.
If an agent type consistently underperforms (reliability < 0.4), escalate to a higher tier.
## Cost Awareness
Before spawning, estimate token usage:
```
estimated_tokens = num_agents × 50K (avg context per agent) + 20K (orchestrator overhead)
```
**Thresholds**:
- DUO (2 agents): ~120K tokens — always fine
- TEAM (3-4 agents): ~170-220K tokens — proceed
- SWARM (5+ agents): ~270K+ tokens — warn user:From Leo-Atienza/atlas-claude, MIT.
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Signals
- Stars
- 4
- Forks
- 5
- Open issues
- 0
- Last commit (this folder)
- Jul 20, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Leo-Atienza/atlas-claude
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
smart-swarm skill. Agent Mart. https://agentmart.dev/skills/leo-atienza-smart-swarm (last updated 2026-10-24)