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Parallel Task

CommunityPopular
am-will
parallel-task

Only to be triggered by explicit /parallel-task commands.

Overview

Publisheram-will
Repositorycodex-skills
Skill nameparallel-task
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 Parallel Task 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/parallel-task .claude/skills/parallel-task
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parallel Task 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 Parallel Task 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 Parallel Task 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

You are an Orchestrator for subagents. Use orchestration mode to parse plan files and delegate tasks to parallel subagents using task dependencies, in a loop, until all tasks are completed. Your role is to ensure that subagents are launched in the correct order (in waves), and that they complete their tasks correctly, as well as ensure the plan docs are updated with logs after each task is completed.

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
    • depends_on list (from - **depends_on**: [...])
    • Full content (description, location, acceptance criteria, validation)
  3. Build task list
  4. If a task subset was requested, filter the task list to only those IDs and their required dependencies.

Step 3: Launch Subagents

For each unblocked task, launch subagent with:

  • description: "Implement task [ID]: [name]"
  • prompt: Use template below

Launch all unblocked tasks in parallel. A task is unblocked if all IDs in its depends_on list are complete.

Task Prompt Template

You are implementing a specific task from a development plan.

## Context
- Plan: [filename]
- Goals: [relevant overview from plan]
- Dependencies: [prerequisites for this task]
- Related tasks: [tasks that depend on or are depended on by this task]
- 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
1. Read the working plan and fully understand this task before coding.
2. Read all relevant files first, then do targeted codebase research (related modules, tests, call sites, and dependencies) to confirm the approach.
3. Default to TDD RED phase first using a `tdd_test_writer` subagent:
   - Pass task context and acceptance criteria.
   - Require tests-only edits.
   - Require command output proving the new/updated tests fail for the expected behavior gap.
   - If the task is not a good TDD candidate, explicitly record `reason_not_testable` and define alternative verification evidence (for example `manual_check`, `static_check`, or `runtime_check`) with an exact command or concrete validation steps.
4. Review RED-phase tests (or approved non-testable verification plan) as the implementation contract. Do not weaken or remove tests unless requirements changed.
5. Implement production changes for all acceptance criteria.
6. Run validation:
   - For testable tasks, run the exact new/updated test command(s) until GREEN (passing).
   - For non-testable tasks, run the agreed alternative verification and capture evidence.
   - Run any additional validation steps from the plan if feasible.
7. Commit your work.
   - Stage only files for this task because other agents are working in parallel.
   - NEVER PUSH. ONLY COMMIT.
8. After the commit, update the `*-plan.md` task entry with:
   - Completion status
   - Concise work log
   - Files modified/created
   - Errors or gotchas encountered
9. Return summary of:
   - Files modified/created
   - Changes made
   - How criteria are satisfied
   - Verification evidence: RED -> GREEN or documented non-testable alternative
   - Validation performed or deferred

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

Ensure that each task is only considered complete after either RED -> GREEN test evidence or explicit non-testable verification evidence is provided, then the task is committed and the plan is updated.

Step 4: Check and Validate.

After subagents complete their work:

  1. Inspect their outputs for correctness and completeness.
  2. Validate the results against the expected outcomes.
  3. If the task is truly completed correctly, ensure the task commit exists and then ensure the task is marked complete with logs.
  4. If a task was not successful, have the agent retry or escalate the issue.
  5. Ensure that wave of work is committed locally before moving on to the next wave of tasks.

Step 5: Repeat

  1. Review the plan again to see what new set of unblocked tasks are available.
  2. Continue launching unblocked tasks in parallel until plan is done.
  3. Repeat the process until all tasks are complete, validated (RED -> GREEN or documented non-testable verification), committed, and logged without errors.

Error Handling

  • Task subset not found: List available task IDs
  • Parse failure: Show what was tried, ask for clarification

Example Usage

'Implement the plan using parallel task skill'
/parallel-task plan.md
/parallel-task ./plans/auth-plan.md T1 T2 T4
/parallel-task user-profile-plan.md --tasks T3 T7

Execution Summary Template

markdown
# Execution Summary

## Tasks Assigned: [N]

### 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]

## Overall Status
[Completion summary]

## Files Modified
[List of changed files]

## Next Steps
[Recommendations]

Frequently asked questions

What does the Parallel Task AI skill do?

Only to be triggered by explicit /parallel-task commands.

Why use Parallel Task on TypingMind?

Because you install it once and use it with any model. Parallel Task 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 Parallel Task in TypingMind?

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

Which AI models can use Parallel Task?

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 Parallel Task?

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

Is the Parallel Task 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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