Swarm Coordinator#

Swarm Coordinator is a lightweight coordination skill for multi-agent execution. It helps an agent design, validate, and operate a swarm workflow using:

• standardized SwarmCommand messages
• Redis or in-memory Pub/Sub coordination
• token-budget governance and downgrade rules
• agent status tracking
• negotiation and conflict resolution
• assignment, completion, and failure notifications

Use it when the task is not “one agent answers once”, but multiple agents must coordinate without losing control of cost, state, ownership, or completion criteria.

This skill is intentionally control-oriented: a swarm is only valuable when parallelism improves throughput or quality without causing duplicated work, hidden queue drift, or uncontrolled token spend.

---

When to Use#

Use this skill for:

• multi-agent task delegation or swarm execution
• role-based AI teams, agent crews, or collaborative agent workflows
• Pub/Sub task coordination with Redis or memory queues
• designing a standard command protocol between agents
• assigning roles, budgets, deadlines, and dependencies
• tracking task state across multiple agents
• negotiating conflicts between agents
• enforcing token quota and fallback behavior
• reviewing whether a swarm workflow can converge safely

Do not use it for simple single-agent tasks. If one agent can complete the job directly, avoid swarm overhead.

---

Core Principle#

A swarm is useful only if it improves throughput or quality without causing coordination chaos.

Always protect four invariants:

1. Single task owner — every task has one accountable owner at a time.
2. Explicit state — each command has status, deadline, budget, dependencies, and result.
3. Bounded cost — token budget and downgrade rules are part of the protocol.
4. Verifiable completion — completion requires artifacts, tests, review, or an explicit result field.

If any invariant is missing, the swarm can drift, duplicate work, or burn tokens.

---

Default Workflow#

Step 1: Decide whether swarm is justified#

Use swarm only when at least one is true:

• task can be split into independent subtasks
• different agents have clearly different roles
• review/verification must be separated from implementation
• latency can be reduced through parallel work
• negotiation is needed because constraints conflict

If not, keep it single-agent.

Step 2: Define roles and ownership#

Specify:

text

commander / coordinator
executor(s)
reviewer / auditor
monitor / verifier
fallback owner

Each task should have one current assigned_to. Multiple reviewers are allowed, but multiple executors writing the same artifact are not unless explicitly coordinated.

Step 3: Create a SwarmCommand#

A command must include:

json

{
  "command_id": "cmd_12345678",
  "timestamp": "2026-04-25T12:00:00Z",
  "sender": {"type": "coordinator", "id": "001"},
  "target": {"type": "developer", "id": "003"},
  "command": {
    "action": "develop",
    "module": "login",
    "requirements": ["JWT auth", "tests"],
    "output_format": "python_code"
  },
  "metadata": {
    "priority": "high",
    "token_budget": 1500,
    "deadline": "2026-04-25T13:00:00Z",
    "dependencies": []
  },
  "negotiation": {
    "allowed": true,
    "timeout": 300
  }
}

Prefer using coordinator/swarm_protocol.py for deterministic command creation and validation. If your project has its own agent taxonomy, map local role names to the protocol roles instead of hard-coding private labels in prompts.

Step 4: Validate before publish#

Before publishing to Redis or memory queue, validate:

• schema format
• known sender / target role
• priority is one of low | medium | high | critical
• token budget is positive and within tier quota
• dependencies exist or are intentionally empty
• deadline is realistic
• completion gate is clear

If validation fails, do not publish. Return validation errors and ask for correction or auto-fix safe fields.

Step 5: Publish, subscribe, and track state#

Use:

python

from coordinator.pubsub import PubSubCoordinator
from coordinator.swarm_protocol import SwarmProtocol

protocol = SwarmProtocol()
coordinator = PubSubCoordinator(use_redis=True)

command = protocol.create_command(
    agent_type="developer",
    command={"action": "analyze", "module": "performance"},
    priority="high",
    token_budget=2000,
)

valid, errors = protocol.validate_command(command)
if valid:
    coordinator.publish("tasks", command.to_dict())
else:
    print(errors)

Track state transitions:

text

pending → assigned → in_progress → completed / failed / cancelled

No command should remain in_progress forever. Use deadline or heartbeat timeout to trigger fallback.

Step 6: Collect result and close the loop#

Completion should include:

• success: true/false
• output or artifact path
• tests/review/verification result when applicable
• token usage
• failure reason if failed
• next action recommendation

If result is missing required artifacts, mark as incomplete instead of completed.

---

Token Budget and Downgrade Rules#

Use token budget as a control mechanism, not just metadata.

Recommended rules:

ConditionActionbudget remaining > 40%continue normal executionbudget remaining 20-40%compress context and reduce parallel agentsbudget remaining < 20%downgrade model/tier or require coordinator approvalbudget exceededstop publishing new subtasks and request confirmationrepeated failurelower concurrency and route to reviewer/monitor

For local implementation, use coordinator/token_budget.py if available.

---

Negotiation Rules#

Allow negotiation when:

• two agents propose conflicting plans
• deadline and token budget cannot both be satisfied
• a dependency is blocked
• an agent lacks capability or context

Negotiation output should be a decision, not endless discussion:

json

{
  "decision": "assign_to_developer_then_review_by_auditor",
  "reason": "developer owns implementation; auditor reviews risk",
  "budget_adjustment": 500,
  "deadline_adjustment": null,
  "blocked": false
}

If negotiation exceeds timeout, coordinator decides or escalates to human.

---

Failure Handling#

Handle these failures explicitly:

FailureResponseinvalid commandreject before publish; return schema errorstarget unavailablereroute to fallback ownerdependency blockedkeep pending; notify coordinatorbudget exceededpause or downgrade; do not silently continuedeadline missedmark failed or escalateduplicate ownerchoose one owner; cancel duplicate assignmentincomplete resultreopen task with missing artifact listrepeated failurereduce concurrency; route to reviewer/monitor

Never let failures become silent queue drift.

---

Safety Boundaries#

Ask for human confirmation before swarm actions that are:

• destructive: delete data, remove files, reset state
• public: publish, message external users, send email
• costly: paid API calls, high-token parallel execution, cloud deployment
• irreversible: production migrations, permission changes, credential rotation
• ambiguous: unclear task owner, conflicting requirements, missing acceptance criteria

Swarm coordination amplifies mistakes. High-risk actions need stronger gates than single-agent execution.

---

Output Format#

When using this skill, return:

markdown

## Swarm Plan
- Goal:
- Swarm justified? yes/no + reason
- Agents and roles:
- Ownership model:

## Commands
| command_id | target | action | budget | dependencies | gate |
|---|---|---|---:|---|---|

## Coordination Flow
pending → assigned → in_progress → completed/failed

## Budget / Downgrade
- Total budget:
- Per-agent budget:
- Downgrade trigger:

## Failure / Fallback
- Main risks:
- Fallback owner:
- Escalation condition:

## Verification
- Required artifacts:
- Tests / review:
- Done criteria:

For code-facing tasks, also mention which files or APIs to use.

---

Bundled Resources#

Use these resources when needed:

• coordinator/swarm_protocol.py — deterministic SwarmCommand creation, validation, assignment, completion, negotiation helpers.
• schemas/swarm_command.json — JSON Schema for command validation.
• tests/test_swarm_protocol.py — regression tests for protocol behavior.
• test-prompts.json — Darwin-style prompts for future skill regression evaluation.

Read or run them when modifying the protocol implementation.

---

Validation Checklist#

Before calling a swarm workflow ready, check:

• Every task has exactly one current owner.
• Every command validates against schema.
• Budget and deadline are explicit.
• Dependencies are declared.
• Completion gate is explicit.
• Failure fallback is defined.
• High-risk actions require confirmation.
• Tests or review exist for important outputs.
• No open-ended negotiation loop remains.

---

Quality Bar#

A good swarm plan should reduce confusion, not add bureaucracy.

It succeeds when:

• agents know exactly what they own
• coordinator can see state and budget
• failures route to a clear fallback
• completion is verifiable
• token use stays bounded
• parallelism improves throughput without oscillation

If the swarm adds agents without improving control, do not use swarm.