super-swarm-spark

Only to be triggered by explicit super-swarm-spark commands.

25 stars

Best use case

super-swarm-spark is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Only to be triggered by explicit super-swarm-spark commands.

Teams using super-swarm-spark should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/super-swarm-spark/SKILL.md --create-dirs "https://raw.githubusercontent.com/ComeOnOliver/skillshub/main/skills/aiskillstore/marketplace/am-will/super-swarm-spark/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/super-swarm-spark/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How super-swarm-spark Compares

Feature / Agentsuper-swarm-sparkStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Only to be triggered by explicit super-swarm-spark commands.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

```markdown
# 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

spark-sql-optimizer

25
from ComeOnOliver/skillshub

Spark Sql Optimizer - Auto-activating skill for Data Pipelines. Triggers on: spark sql optimizer, spark sql optimizer Part of the Data Pipelines skill category.

spark-job-creator

25
from ComeOnOliver/skillshub

Spark Job Creator - Auto-activating skill for Data Pipelines. Triggers on: spark job creator, spark job creator Part of the Data Pipelines skill category.

pyspark-transformer

25
from ComeOnOliver/skillshub

Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.

oss-contributor-swarm

25
from ComeOnOliver/skillshub

Autonomous 9-agent swarm that continuously contributes to open source projects on GitHub. Finds good-first-issues, analyzes requirements, writes code/tests/docs, creates PRs, and responds to reviews - all automatically with learning.

spark-optimization

25
from ComeOnOliver/skillshub

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

swarm

25
from ComeOnOliver/skillshub

Autonomous multi-agent workflow system for complex coding tasks. Use when the user requests non-trivial changes that would benefit from autonomous execution with built-in review gates. Ideal for refactoring modules, adding major features, migrating codebases, adding comprehensive test coverage, or any multi-step development task that can run autonomously. NOT for simple fixes, typos, or single-file changes.

when-using-advanced-swarm-use-swarm-advanced

25
from ComeOnOliver/skillshub

Advanced swarm patterns with dynamic topology switching and self-organizing behaviors for complex multi-agent coordination

when-orchestrating-swarm-use-swarm-orchestration

25
from ComeOnOliver/skillshub

Complex multi-agent swarm orchestration with task decomposition, distributed execution, and result synthesis

when-deploying-cloud-swarm-use-flow-nexus-swarm

25
from ComeOnOliver/skillshub

Deploy cloud-based AI agent swarms with event-driven workflow automation using Flow Nexus platform. Supports hierarchical, mesh, ring, and star topologies with E2B sandbox distribution.

swarm-orchestration

25
from ComeOnOliver/skillshub

Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

swarm-advanced

25
from ComeOnOliver/skillshub

Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

parallel-swarm-implementation

25
from ComeOnOliver/skillshub

Loop 2 of the Three-Loop Integrated Development System. META-SKILL that dynamically compiles Loop 1 plans into agent+skill execution graphs. Queen Coordinator selects optimal agents from 86-agent registry and assigns skills (when available) or custom instructions. 9-step swarm with theater detection and reality validation. Receives plans from research-driven-planning, feeds to cicd-intelligent-recovery. Use for adaptive, theater-free implementation.