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Super Swarm Spark

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am-will
super-swarm-spark

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

Overview

Publisheram-will
Repositorycodex-skills
Skill namesuper-swarm-spark
Stars
1K
Forks
60
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by am-will on GitHub. Read the source before you install it.

Installation

Install the Super Swarm Spark AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/am-will/codex-skills.git /tmp/codex-skills
mkdir -p .claude/skills
cp -r /tmp/codex-skills/skills/super-swarm-spark .claude/skills/super-swarm-spark
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Super Swarm Spark in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Super Swarm Spark on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Super Swarm Spark is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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]

Frequently asked questions

What does the Super Swarm Spark AI skill do?

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

Why use Super Swarm Spark on TypingMind?

Because you install it once and use it with any model. Super Swarm Spark is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Super Swarm Spark in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/am-will/codex-skills/tree/main/skills/super-swarm-spark. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Super Swarm Spark?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Super Swarm Spark?

As many as you like. As long as a model supports skills, you can use Super Swarm Spark with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Super Swarm Spark AI skill free?

It is published on GitHub by am-will. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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