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Audit score 90

super-swarm-spark

am-will/codex-skills

Orchestrate parallel task execution across up to 12 concurrent Sparky subagents from a markdown plan.

What is super-swarm-spark?

Super-Swarm-Spark is a parallel task orchestrator that reads markdown plan files, parses tasks, and delegates work to a rolling pool of up to 12 concurrent Sparky subagents. It keeps agents continuously busy, validates each completion, updates the plan file, and performs final integration fixes. Use this when you need to execute a multi-task development plan with maximum parallelism.

  • Parse markdown plan files and extract task definitions with IDs, descriptions, acceptance criteria, and validation steps
  • Launch and manage a rolling pool of up to 12 concurrent Sparky subagents, refilling slots immediately as tasks complete
  • Build context packs for each task including canonical file paths, naming constraints, and cross-task dependencies
  • Validate every subagent result against acceptance criteria and update the plan file with completion logs
  • Reconcile parallel-work conflicts, enforce canonical naming, and perform final integration fixes after all tasks complete
  • Run tests and fix failures to ensure the integrated codebase meets plan expectations

How to install super-swarm-spark

npx skills add https://github.com/am-will/codex-skills --skill super-swarm-spark
Prerequisites
  • A markdown plan file with clearly defined task sections (e.g., `### T1:` or `### Task 1.1:`)
  • Each task must include: description, location/file paths, acceptance criteria, and validation steps
  • Git repository initialized in the working directory for commit tracking
  • Sparky subagent role available in the agent environment
Claude Code
Cursor
Windsurf
Cline

How to use super-swarm-spark

  1. 1.Create or provide a markdown plan file with task sections, each containing ID, name, description, file paths, acceptance criteria, and validation steps
  2. 2.Invoke the skill with the plan filename: `/super-swarm-spark plan.md` to run all tasks, or `/super-swarm-spark plan.md T1 T2 T4` to run specific task IDs
  3. 3.The orchestrator parses the plan, builds context packs for each task, and launches up to 12 Sparky subagents in a rolling pool
  4. 4.Monitor the rolling execution: as each subagent completes, the orchestrator validates the result, updates the plan file, and immediately launches the next pending task
  5. 5.After all tasks complete, the orchestrator performs integration fixes, reconciles conflicts, runs tests, and fixes failures
  6. 6.Review the Execution Summary to confirm all tasks are complete, all files are correctly modified, and tests pass

Use cases

Good for
  • Execute a multi-feature development plan where tasks can run in parallel without blocking on each other
  • Coordinate large refactoring efforts across multiple files and modules using a shared plan document
  • Implement a feature roadmap where independent tasks are delegated to subagents and tracked in a single markdown plan
  • Parallelize code generation, testing, and integration work to reduce total execution time
  • Manage complex projects where naming consistency and cross-task coordination are critical
Who it's for
  • Development teams executing multi-task plans with independent work streams
  • Project leads coordinating parallel feature development
  • Developers using Claude Code or Cursor with agent orchestration capabilities
  • Teams needing structured task tracking and parallel execution with automatic validation

super-swarm-spark FAQ

What is the maximum number of concurrent subagents?

The skill maintains a rolling pool of up to 12 concurrent Sparky subagents. When a task completes and is validated, the next pending task is launched immediately to keep the pool full.

Can I run only specific tasks from a plan?

Yes. Use `/super-swarm-spark plan.md T1 T3 T5` to run only those task IDs. The orchestrator will filter the plan to include only the requested tasks.

How does the skill handle task dependencies?

The skill ignores dependency maps and launches tasks in parallel. Task relationships are provided to subagents for context only, never as blockers. The orchestrator reconciles any conflicts during the final integration pass.

What happens if a subagent fails?

The orchestrator validates every completion, detects failures, and can retry or escalate. Failed tasks are reported in the Execution Summary with details on what went wrong and what resolution was attempted.

How are file naming conflicts prevented across parallel tasks?

The orchestrator builds context packs with canonical file paths and naming constraints for each task. Subagents are required to use only the provided canonical names and must report any new paths before creating them.

Full instructions (SKILL.md)

Source of truth, from am-will/codex-skills.


name: super-swarm-spark description: > Only to be triggered by explicit super-swarm-spark commands.

Parallel Task Executor (Sparky Rolling 12-Agent Pool)

You are an Orchestrator for subagents. Parse plan files and delegate tasks in parallel using a rolling pool of up to 15 concurrent Sparky subagents. Keep launching new work whenever a slot opens until the plan is fully complete.

Primary orchestration goals:

  • Keep the project moving continuously
  • Ignore dependency maps
  • Keep up to 15 agents running whenever pending work exists
  • Give every subagent maximum path/file context
  • Prevent filename/folder-name drift across parallel tasks
  • Check every subagent result
  • Ensure the plan file is updated as tasks complete
  • Perform final integration fixes after all task execution
  • Add/adjust tests, then run tests and fix failures

Process

Step 1: Parse Request

Extract from user request:

  1. Plan file: The markdown plan to read
  2. Task subset (optional): Specific task IDs to run

If no subset provided, run the full plan.

Step 2: Read & Parse Plan

  1. Find task subsections (e.g., ### T1: or ### Task 1.1:)
  2. For each task, extract:
    • Task ID and name
    • Task linkage metadata for context only
    • Full content (description, location, acceptance criteria, validation)
  3. Build task list
  4. If a task subset was requested, filter to only those IDs.

Step 3: Build Context Pack Per Task

Before launching a task, prepare a context pack that includes:

  • Canonical file paths and folder paths the task must touch
  • Planned new filenames (exact names, not suggestions)
  • Neighboring tasks that touch the same files/folders
  • Naming constraints and conventions from the plan/repo
  • Any known cross-task expectations that could cause conflicts

Rules:

  • Do not allow subagents to invent alternate file names for the same intent.
  • Require explicit file targets in every subagent assignment.
  • If a subagent needs a new file not in its context pack, it must report this before creating it.

Step 4: Launch Subagents (Rolling Pool, Max 12)

Run a rolling scheduler:

  • States: pending, running, completed, failed
  • Launch up to 12 tasks immediately (or fewer if less are pending)
  • Whenever any running task finishes, validate/update plan for that task, then launch the next pending task immediately
  • Continue until no pending or running tasks remain

For each launched task, use:

  • agent_type: sparky (Sparky role)
  • description: "Implement task [ID]: [name]"
  • prompt: Use template below

Do not wait for grouped batches. The only concurrency limit is 12 active Sparky subagents.

Every launch must set agent_type: sparky. Any other role is invalid for this skill.

Task Prompt Template

You are implementing a specific task from a development plan.

## Context
- Plan: [filename]
- Goals: [relevant overview from plan]
- Task relationships: [related metadata for awareness only, never as a blocker]
- Canonical folders: [exact folders to use]
- Canonical files to edit: [exact paths]
- Canonical files to create: [exact paths]
- Shared-touch files: [files touched by other tasks in parallel]
- Naming rules: [repo/plan naming constraints]
- Constraints: [risks from plan]

## Your Task
**Task [ID]: [Name]**

Location: [File paths]
Description: [Full description]

Acceptance Criteria:
[List from plan]

Validation:
[Tests or verification from plan]

## Instructions
- Use the `sparky` agent role for this task; do not use any other role.
1. Examine the plan and all listed canonical paths before editing
2. Implement changes for all acceptance criteria
3. Keep work atomic and committable
4. For each file: read first, edit carefully, preserve formatting
5. Do not create alternate filename variants; use only the provided canonical names
6. If you need to touch/create a path not listed, stop and report it first
7. Run validation if feasible
8. ALWAYS mark completed tasks IN THE *-plan.md file AS SOON AS YOU COMPLETE IT! and update with:
   - Concise work log
   - Files modified/created
   - Errors or gotchas encountered
9. Commit your work
   - Note: There are other agents working in parallel to you, so only stage and commit the files you worked on. NEVER PUSH. ONLY COMMIT.
10. Double check that you updated the *-plan.md file and committed your work before yielding
11. Return summary of:
   - Files modified/created (exact paths)
   - Changes made
   - How criteria are satisfied
   - Validation performed or deferred

## Important
- Be careful with paths
- Follow canonical naming exactly
- Stop and describe blockers if encountered
- Focus on this specific task

Step 5: Validate Every Completion

As each subagent finishes:

  1. Inspect output for correctness and completeness.
  2. Validate against expected outcomes for that task.
  3. Ensure plan file completion state + logs were updated correctly.
  4. Retry/escalate on failure.
  5. Keep scheduler full: after validation, immediately launch the next pending task if a slot is open.

Step 6: Final Orchestrator Integration Pass

After all subagents are done:

  1. Reconcile parallel-work conflicts and cross-task breakage.
  2. Resolve duplicate/variant filenames and converge to canonical paths.
  3. Ensure the plan is fully and accurately updated.
  4. Add or adjust tests to cover integration/regression gaps.
  5. Run required tests.
  6. Fix failures.
  7. Re-run tests until green (or report explicit blockers with evidence).

Completion bar:

  • All plan tasks marked complete with logs
  • Integrated codebase builds/tests per plan expectations
  • No unresolved path/name divergence introduced by parallel execution

Scheduling Policy (Required)

  • Max concurrent subagents: 12
  • If pending tasks exist and running count is below 12: launch more immediately
  • Do not pause due to relationship metadata
  • Continue until the full plan (or requested subset) is complete and integrated

Error Handling

  • Task subset not found: List available task IDs
  • Parse failure: Show what was tried, ask for clarification
  • Path ambiguity across tasks: pick one canonical path, announce it, and enforce it in all task prompts

Example Usage

'Implement the plan using super-swarm'
/super-swarm-spark plan.md
/super-swarm-spark ./plans/auth-plan.md T1 T2 T4
/super-swarm-spark user-profile-plan.md --tasks T3 T7

Execution Summary Template

# Execution Summary

## Tasks Assigned: [N]

## Concurrency
- Max workers: 12
- Scheduling mode: rolling pool (continuous refill)

### Completed
- Task [ID]: [Name] - [Brief summary]

### Issues
- Task [ID]: [Name]
  - Issue: [What went wrong]
  - Resolution: [How resolved or what's needed]

### Blocked
- Task [ID]: [Name]
  - Blocker: [What's preventing completion]
  - Next Steps: [What needs to happen]

## Integration Fixes
- [Conflict or regression]: [Fix]

## Tests Added/Updated
- [Test file]: [Coverage added]

## Validation Run
- [Command]: [Pass/Fail + key output]

## Overall Status
[Completion summary]

## Files Modified
[List of changed files]

## Next Steps
[Recommendations]