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Super Swarm Spark

  • 1.1k installs
  • 1k repo stars
  • Updated July 14, 2026
  • am-will/codex-skills

super-swarm-spark is an agent skill orchestrating up to twelve parallel Sparky subagents from markdown plan files.

About

The super-swarm-spark skill orchestrates parallel Sparky subagents parsing markdown plan files into delegated tasks with a rolling scheduler keeping up to twelve agents active. Triggered only by explicit super-swarm-spark commands. Step one extracts plan file path and optional task ID subset. Step two parses task subsections like T1 headers capturing IDs, linkage metadata, descriptions, locations, acceptance criteria, and validation steps. Step three builds per-task context packs with canonical file paths, exact new filenames, neighboring task conflicts, naming constraints, and cross-task expectations; subagents must not invent alternate filenames. Step four launches sparky agent_type tasks immediately up to twelve concurrent slots, validating each completion, updating the plan file, and filling open slots until pending and running queues empty. Ignores dependency maps to maximize throughput. Post-run integration fixes merge parallel outputs, adds or adjusts tests, runs tests, and fixes failures. Every launch must use agent_type sparky; other roles are invalid. Emphasizes continuous project movement with maximum path context per subagent.

  • Rolling pool schedules up to twelve concurrent Sparky subagents until plan completes.
  • Context packs pin canonical paths and forbid alternate filenames across parallel tasks.
  • Parses plan tasks from headers like ### T1 with acceptance criteria and validation.
  • Validates each subagent result and updates the plan file on completion.
  • Final integration pass merges outputs, adjusts tests, runs tests, and fixes failures.

Super Swarm Spark by the numbers

  • 1,146 all-time installs (skills.sh)
  • Ranked #944 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 3, 2026 (Skillselion catalog sync)
At a glance

super-swarm-spark capabilities & compatibility

Capabilities
plan file task parsing · rolling twelve agent scheduler · per task context pack enforcement · plan file progress updates · post run integration and test fixes
Use cases
orchestration · planning
From the docs

What super-swarm-spark says it does

Only to be triggered by explicit super-swarm-spark commands.
SKILL.md
Keep up to 15 agents running whenever pending work exists
SKILL.md
npx skills add https://github.com/am-will/codex-skills --skill super-swarm-spark

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Listed on Skillselion
Installs1.1k
repo stars1k
Security audit3 / 3 scanners passed
Last updatedJuly 14, 2026
Repositoryam-will/codex-skills

How do I execute all tasks in a plan file in parallel with validated Sparky subagents?

Orchestrate parallel Sparky subagents from plan files using a rolling pool of up to twelve concurrent tasks.

Who is it for?

Teams with large markdown implementation plans needing rolling parallel Sparky execution.

Skip if: Skip unless user explicitly invokes super-swarm-spark commands.

When should I use this skill?

User explicitly triggers super-swarm-spark with a plan file path and optional task subset.

What you get

Completed plan tasks with updated plan file, integrated code, and passing tests after final merge pass.

  • Completed plan task outputs
  • Verified parallel agent results

By the numbers

  • Supports up to 15 concurrent Sparky subagents in a rolling pool

Files

SKILL.mdMarkdownGitHub ↗

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]

Related skills

Forks & variants (1)

Super Swarm Spark has 1 known copy in the catalog totaling 0 installs. They canonicalize to this original listing.

How it compares

Choose super-swarm-spark for high-throughput parallel plan execution rather than single-agent sequential implementation skills.

FAQ

How many agents run at once?

Up to twelve Sparky subagents in a rolling pool until all tasks finish.

Are dependencies honored?

No; the skill ignores dependency maps to keep agents running continuously.

What agent_type is required?

sparky only; any other role is invalid for launches.

Is Super Swarm Spark safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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