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Delegate Setup

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amElnagdy
delegate-setup

Configure delegation fleet lanes: which implementer CLI handles which kind of work, with optional model and effort (or variant) dials. Discovers installed CLIs, proposes a lane map for user approval, and writes global or project config only after explicit yes. Use when the user asks to set up, configure, or reconfigure delegation lanes, a fleet of lanes, or which implementer handles feature/tests/ui work — not for dispatching a coding task to an implementer.

Overview

PublisheramElnagdy
Repositorydelegate-skills
Skill namedelegate-setup
Stars
2.1K
Forks
167
Bundled files
6
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by amElnagdy on GitHub. Read the source before you install it.

Installation

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

Use it in TypingMind

Enable Delegate Setup 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 Setup 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 Setup 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.

Delegate Setup

You are the orchestrator in setup mode. Discover installed implementer CLIs, propose a fleet of lanes, and write configuration only after the user approves.

This skill does not dispatch coding work. It only authors the lane map.

One concept: lanes. Never say “routes.”

Example lane: feature → implementer opencode, model opencode/grok, variant high (OpenCode uses variant for reasoning intensity, not effort).

When NOT to use this

  • The user wants a task implemented — use the matching *-delegate skill instead.
  • A one-off model change on a single dispatch — pass --model / --effort / --variant on that relay.

Hard rules

  1. Every lane must include implementer.
  2. Put dials on the same object (model, effort or variant, …) only if that implementer supports them — see references/schema.md.
  3. Show a human-readable lane table and the full JSON before every write; re-show after every tweak.
  4. Write only after an explicit approval (“yes”, “approve”, “write it”).
  5. Ask scope unless already clear: global (all projects) vs this repo only. Never create a project file just because cwd is a git repo. If there is no git repo, default to global and say so.
  6. Do not invent model identifiers.
  7. In interview or usage-scan mode, never write any dial the user did not give you and the schema does not require — omit it, so the CLI’s or relay’s own default applies.
  8. Prefer 3–5 useful lanes over a kitchen-sink map.
  9. Never edit AGENTS.md, CLAUDE.md, or other user agent-instruction files.
  10. Never run a *-delegate relay from this skill.

(<skill-dir> is this skill’s install directory — the folder that contains this SKILL.md.)

Flow

discover → load → grounding menu → propose (with Basis) → scope → approve → write

1. Discover

bash
node "<skill-dir>/scripts/discover.mjs"

Summarize installed vs missing, auth (true / false / null = unknown), and whether models were reported, aliases (curated aliases in the registry, not live discovery — full model names also work), unsupported, or failed.

2. Load existing (effective map)

bash
node "<skill-dir>/scripts/config.mjs" load --cwd "$PWD"
  • Neither present → “No lanes configured yet.”
  • Otherwise → table of effective lanes with a Source column (global / project). Do not paste both raw files unless asked.
  • If projectPresent is true and projectTrusted is false, label the project lanes untrusted. They cannot dispatch until the user reviews and approves a project write.

3. Propose

Discovery reports capability, never task fit. So ask one grounding question before proposing anything — one question, three options, not a wizard:

How should I pick the lanes? (1) Quick defaults — I decide, no questions. (2) Interview — about four questions on how you want work allocated. (3) Usage scan — I re-read your CLIs’ local session folders (counts and dates only, never the conversations) and let the numbers place your lanes — if one CLI dominates, expect one question about its role. Happy to do 2 and 3 together.

  • Quick defaults → propose immediately.
  • Interview → the four questions (allocation policy, never model rankings) and how to ask them (one medium per round) live in references/setup-dialogue.md — read it before you ask.
  • Usage scannode "<skill-dir>/scripts/discover.mjs" --usage. Tell the user it is metadata only before running it. Each discovered CLI gains usage: { sessions, lastUsed }; null means no probe is wired — unknown, not unused.
  • Both → run the scan first, then ask only what the numbers cannot answer.
  • Inside a git repo, repo signals (languages, test weight, frontend share) are a fourth source of evidence. They do not change the menu; they feed the proposal and the repo basis.

That menu is also the consent surface — the option chosen sets how much of the map is yours to decide:

  • Quick defaults — the user hired your opinion. A full map is legitimate, dials included; label every lane my opinion, say plainly that the map is your opinion, and keep it cheap to revise.
  • Interview / usage scan — evidence modes, so every dial is gated (rule 7): set one only from the user’s answer, or where the schema requires it (opencode lanes require model). Omitting is always safe — every dial has a default the user already lives with, and a CLI’s configured default is their standing choice, better evidence than your priors. Choosing which installed implementer gets a lane is still yours — Basis my opinion — but a dial that raises spend is not: offer your dial picks only as an addendum after the proposal, see references/setup-dialogue.md.
  • An unanswered question shrinks the map; it never licenses a substitution. Propose fewer, more conservative lanes, name the axis you are blind on (no quota answer → say the map is quota-blind), and invite the answer anytime. Re-ask once at most; never backfill silence with priors.

Delegation economics. The orchestrator reviews and lands every result — the review is the quality gate, so optimize total cost, not implementer prestige:

  • Prefer capable, authenticated, burnable, low-usage CLIs for bounded, objectively gated work (tests, mechanical refactors, straightforward fixes) when their reliability keeps review and rework economical — lanes push token burn away from the subscriptions the user is protecting. Low usage alone does not establish burnable: discovery cannot see plans, limits, or per-run cost, and a rarely-used CLI may be metered or deliberately avoided. Burnable comes from the user's quota answer — or, in quick defaults, from your labeled opinion.
  • Avoid binding a lane to a CLI the user is protecting or orchestrates from, by default; bind it only when the user asks for it or no acceptable alternative exists. Lanes are orchestrator-blind: the same lane fires from every seat the user drives from, and from that CLI's own seat it dispatches the CLI to itself.
  • Surplus placement breaks down when rework and review cost exceed the savings; when the implementer is flaky; when correctness rides on security, concurrency, migrations, or unstated domain knowledge; and when the output is the product (debate, architecture, research) — review limits damage, it does not manufacture a good first attempt. Bind those lanes to stronger implementers.
  • An explicit "spare X" answer removes X from proposed lanes by default, and overrides blanket posture answers on any lane the user explicitly retains for X — ask whether the posture applies there; omit the dial if unanswered. Never silently stretch one answer across an axis it conflicts with.

Question phrasings for the burn/spare and trust interview live in references/setup-dialogue.md.

Then propose the lanes. Name them after the work the user described; fall back to feature, tests, ui, fast, complex. Installed implementers only.

Show:

LaneImplementerModelEffort / variantBasisSource (if updating)
featureopencodeopencode/grokvariant: highyour answer + schema requirement
testscodexusage data
uiclaudemy opinion (implementer)

Basis is mandatory on every lane: your answer / usage data / repo / my opinion / schema requirement (a dial the schema forces is neither evidence nor opinion — say so). A lane you picked from model-quality priors is my opinion — never present it as something the tooling determined, and “installed and authenticated” is capability, not evidence of fit. When a lane’s implementer and its dials come from different places, split the label — see references/setup-dialogue.md.

Then the complete JSON (version: delegate-fleet.v1). One line of why per lane; flag auth or model uncertainty.

Schema and dial table: references/schema.md.

4. Scope

  • User said global / all projects / outside the project → global.
  • No git repo → global (say so).
  • Else ask once: global vs this repo only.

5. Approve and write

On explicit yes, write only the chosen scope (validate first). Build the payload from that scope’s raw file (or an empty lanes object if new) — not from the effective merged load view, or a project write will shadow global-only lanes and a global write will promote project-only ones.

Create a uniquely named file under the platform temporary directory ($TMPDIR, %TEMP%, or Node os.tmpdir(); never hard-code /tmp, which breaks on native Windows), write the exact approved JSON into it with the orchestrator's file-writing tool, and use that populated path as <lanes-json> below. Never validate an empty temp file. Remove the temp file after the validation/write attempt, whether it succeeds or fails.

bash
node "<skill-dir>/scripts/config.mjs" validate "<lanes-json>"
node "<skill-dir>/scripts/config.mjs" write --scope global "<lanes-json>"
# or:  write --scope project --cwd /path/to/repo "<lanes-json>"

Re-read with load, then confirm the path written and the active lane names. Project writes bind approval to the exact config content; later changes fail closed until re-approved. On update, a short before/after is enough.

6. Ready to delegate

Stop after confirming. Tell the user the map is ready. For later work: read the lane’s implementer, load that *-delegate skill, and dispatch with --lane <name> (explicit --model / --effort / --variant still win when passed). Do not start a delegate task unless they ask.

Reconfigure

Same flow. Show the effective current map, propose changes, approve, write one scope’s file. Reinstalling the skills package must not rewrite these files — they live outside the package.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Delegate Setup AI skill do?

Configure delegation fleet lanes: which implementer CLI handles which kind of work, with optional model and effort (or variant) dials. Discovers installed CLIs, proposes a lane map for user approval, and writes global or project config only after explicit yes. Use when the user asks to set up, configure, or reconfigure delegation lanes, a fleet of lanes, or which implementer handles feature/tests/ui work — not for dispatching a coding task to an implementer.

Why use Delegate Setup on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/amElnagdy/delegate-skills/tree/master/skills/delegate-setup. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Delegate Setup?

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 Setup?

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

Is the Delegate Setup AI skill free?

Yes. It is published on GitHub by amElnagdy 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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