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Delegate Task

Community
danielvm-git
delegate-task

Delegate one complex task to a single subagent, review its work in two stages before merging back. Sequential — one agent at a time, with oversight. Use when a task is complex and requires careful review before the result is accepted. Distinct from dispatch-agents (no parallelism here; reviewer sees full diff before proceeding).

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill namedelegate-task
Stars
206
Forks
18
Bundled files
Instructions only
LicenseMIT
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 danielvm-git on GitHub. Read the source before you install it.

Installation

Install the Delegate 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/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/skills/delegate-task .claude/skills/delegate-task
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Delegate 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 Delegate 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 Delegate 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.

story: e45s30

Delegate Task

HARD GATEHARD GATE — Delegated work must have clear success criteria and verification commands. The delegate must be able to verify completion independently.

Delegate a single complex task to a subagent with a two-stage review gate before accepting the result. Use when oversight of a single task matters more than speed.

Distinct from dispatch-agents: This skill runs one subagent sequentially with a mandatory review. dispatch-agents runs multiple subagents in parallel without inter-task review gates.

Subagent depth tiers (e45s30)

Select brief depth from task risk: and skill effort: before spawning:

TierWhenBrief includes
full_maturityP0 stories, multi-file refactors, security workFull template + CONVENTIONS excerpts + threat model if present
standardDefault implementation tasksGoal, scope, out-of-bounds, constraints, verify, prior decisions
minimal_decisiveLight probes, read-only auditsGoal, verify, explicit file list (≤15 lines total)

State depth: <tier> in the Agent tool description field.

Process

1. Define the task

Before spawning the agent, read specs/state.yaml if it exists. Then write a minimal self-contained brief using this template (brief size directly controls token cost and hallucination risk — do not pad):

Goal: [one sentence — specific, measurable outcome]
In scope: [explicit file or module list]
Out of bounds: [what NOT to do]
Constraints: [relevant CONVENTIONS.md rules, existing patterns, test requirements]
Verify: [runnable command]
Prior decisions: [relevant entries from specs/state.yaml — omit section if none apply]

Do not include full file contents, full conversation history, or decisions unrelated to this task.

2. Spawn the subagent (iterative retrieval, max 3 cycles)

Use the Agent tool with a fresh context per spawn. Pass prior decisions only via specs/state.yaml.

Cycle: dispatch → evaluate output vs goal → refine brief → re-spawn if needed (max 3 cycles).

Include in each brief:

  • All context the agent needs (it starts cold — no shared state)
  • Reference to CONVENTIONS.md constraints
  • The verify command it must run before reporting done

3. Stage 1 review — output inspection

When the subagent returns, review its report before looking at the diff:

  • Did it run the verify command? Did it pass?
  • Does it explain what it changed and why?
  • Are there any concerns raised by the agent?

If the report raises red flags, ask the subagent for clarification or re-run with adjusted instructions.

4. Stage 2 review — diff inspection

Inspect the actual diff:

bash
git diff main...HEAD

Check:

  • Changes are scoped to what was asked — nothing extra
  • No any, no @ts-ignore, no disabled lint rules
  • Tests added for new behavior
  • CONVENTIONS.md compliance (naming, structure, no gh issue creation)
  • Boy Scout Rule: touched areas are cleaner than before

5. Decision

  • Accept: merge the result into the main working branch
  • Revise: send back to the subagent with specific feedback
  • Reject: discard and re-approach differently

After accepting, append to specs/state.yaml under ## Active Decisions:

**[task short name]**: [what approach the agent chose and why — one sentence]

Report the decision and rationale to the user.

Frequently asked questions

What does the Delegate Task AI skill do?

Delegate one complex task to a single subagent, review its work in two stages before merging back. Sequential — one agent at a time, with oversight. Use when a task is complex and requires careful review before the result is accepted. Distinct from dispatch-agents (no parallelism here; reviewer sees full diff before proceeding).

Why use Delegate Task on TypingMind?

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

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

Which AI models can use Delegate 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 Delegate Task?

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

Is the Delegate Task AI skill free?

Yes. It is published on GitHub by danielvm-git under the MIT license. 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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