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Delegation Protocol

Community
Dark-Alex-17
delegation-protocol

Structured 6-section delegation template and session-continuity rules for orchestrating sub-agents. Load before spawning any agent.

Overview

PublisherDark-Alex-17
Repositorycoyote
Skill namedelegation-protocol
Stars
56
Forks
2
Bundled files
Instructions only
LicenseAGPL-3.0
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 Dark-Alex-17 on GitHub. Read the source before you install it.

Installation

Install the Delegation Protocol 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.
    https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/delegation-protocol
  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/Dark-Alex-17/coyote.git /tmp/coyote
mkdir -p .claude/skills
cp -r /tmp/coyote/assets/skills/delegation-protocol .claude/skills/delegation-protocol
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Delegation Protocol 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 Delegation Protocol 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 Delegation Protocol 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.

You are delegating work to a sub-agent. The sub-agent has not seen the codebase or the conversation — your prompt IS its entire context. Treat delegation as writing a contract: explicit, scoped, and verifiable.

The 6-section template (every delegation)

Every agent__spawn prompt MUST include all six sections. Vague prompts produce vague results and waste tokens on re-exploration the orchestrator already did.

## TASK
[One atomic goal. One verb. One outcome. No "and also".]

## EXPECTED OUTCOME
[Concrete deliverables and success criteria. "I will know this is done when ..."]

## REQUIRED TOOLS
[Explicit allowlist: fs_read, fs_grep, etc. Prevents tool sprawl.]

## MUST DO
[Exhaustive requirements. Leave nothing implicit. If you'd be annoyed by the agent not doing X, list X.]

## MUST NOT DO
[Forbidden actions. Anticipate rogue behavior. "Do not modify files outside src/auth/."]

## CONTEXT
[File paths, code snippets, existing patterns, constraints. Paste actual code lines from prior exploration — not just file paths.]

Session continuity (NON-NEGOTIABLE)

Every agent__spawn result includes a session_id. Use it. A fix or follow-up to a child's work resumes that child's session_id; a fresh spawn is for new work.

  • Task failed/incomplete → resume with session_id + a tight "Fix: " prompt.
  • Follow-up on a result → resume with session_id + "Also: ".
  • Multi-turn with the same agent → always resume. Never start fresh.

Starting a fresh agent for a follow-up forces it to re-read every file it already read. That's 70%+ wasted tokens, plus the agent loses the reasoning it built up.

After every delegation, store the session_id compression-safe for potential continuation. Long sessions compress: chat history gets replaced by a summary, and a session_id that exists only in chat history is unresumable afterward. Embed it in the todo item for that work (todo__add "Implement auth endpoint (coder <session_id>)") or in your run-state memory file. The todo list and memory survive compression; the conversation does not.

Skill nudges to delegates

Sub-agents have their own skills. Nudge them in the CONTEXT section:

"Load code-review before evaluating the diff." "Load frontend-ui-ux before editing component files." "Load git-master before touching history."

A one-line nudge saves the delegate a skill__list turn.

Verification after delegation

A delegation is NOT complete when the sub-agent returns. It is complete when YOU have verified:

  1. Did it work as expected? (Did the file change? Did the test pass?)
  2. Did it follow existing codebase patterns?
  3. Did the EXPECTED OUTCOME actually materialize?
  4. Did it respect MUST DO and MUST NOT DO?

If any answer is no → resume the session with a corrective prompt. Do not re-spawn from scratch.

Anti-patterns

  • "Follow existing patterns" with no snippet → agent guesses, often wrong
  • Multi-goal prompts → agent does the easy one, skips the rest
  • Missing MUST NOT DO → agent over-reaches into unrelated files
  • Discarding session_id on failure → forced re-exploration, wasted tokens
  • Re-spawning instead of resuming for a 1-line fix → 10x cost

Frequently asked questions

What does the Delegation Protocol AI skill do?

Structured 6-section delegation template and session-continuity rules for orchestrating sub-agents. Load before spawning any agent.

Why use Delegation Protocol on TypingMind?

Because you install it once and use it with any model. Delegation Protocol 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 Delegation Protocol in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/delegation-protocol. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Delegation Protocol?

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 Delegation Protocol?

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

Is the Delegation Protocol AI skill free?

Yes. It is published on GitHub by Dark-Alex-17 under the AGPL-3.0 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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