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Warp Delegate

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amElnagdy
warp-delegate

Delegate a coding task to the Warp Agent CLI (`oz`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Warp - phrasings like "have Warp implement X", "delegate this to the Warp CLI", "run it through Warp", "use oz to implement/fix/refactor" - or wants to run a queue of coding tasks through Warp while staying the reviewer. DO NOT USE for tasks small enough to do inline, when the user wants the code written directly without delegating, or for the interactive `warp` TUI (this skill drives the headless `oz agent run`, not the terminal app).

Overview

PublisheramElnagdy
Repositorydelegate-skills
Skill namewarp-delegate
Stars
2.1K
Forks
167
Bundled files
5
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.

  • 5 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 Warp Delegate 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/warp-delegate .claude/skills/warp-delegate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Warp Delegate 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 Warp Delegate 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 Warp Delegate 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.

Warp Delegate

You are the orchestrator. Delegate a bounded coding task to a separate implementer - the Warp Agent CLI - then review what it produced and land it yourself. You write the brief and own the judgment; the implementer makes changes in its own conversation; you verify and commit.

The loop needs only a shell command and file access, so any comparable orchestrator can drive it.

The binary is oz, not warp

Warp ships two different programs, and only one of them can be delegated to:

  • oz - the Warp Agent CLI. Headless and scriptable; oz agent run executes an agent against a local directory. This is what the relay drives.
  • warp - the interactive Warp TUI. It requires a terminal device, has no prompt or print flag (its only options are --resume, --auto-approve, --api-key, and the provider-key commands), and exits with Device not configured when stdin is a pipe. It cannot be relayed.

If oz is missing but warp is installed, you have the TUI, not the CLI.

When NOT to use this

  • The task is small enough to do inline; delegation overhead is not worth it.
  • The oz CLI is not installed or authenticated.
  • You need a sandboxed or read-only implementer. oz agent run has no sandbox, no permission mode, and no read-only run - see Autonomy and permissions.
  • The work must stay off Warp's servers. oz agent run uploads an end-of-run workspace snapshot unless --no-snapshot is passed, and conversations live server-side.

Prerequisites (check once)

  1. Install the Warp Agent CLI - see https://docs.warp.dev/cli/.
  2. Authenticate: oz login, or set WARP_API_KEY for CI, a container, or any headless host.
  3. Confirm the account has AI quota. A working login is not enough - unlike the other CLIs in this package. oz whoami can succeed while every dispatch fails with In order to use Warp's AI features, subscribe to a Warp plan, or bring your own inference. Warp records this internally as QuotaLimit / "lack of AI quota", so it is a credit condition on the account rather than a CLI-specific entitlement: oz runs the same agent harness as the Warp app and draws on the same account, plan, and credits. Check that oz whoami names the account holding the plan - if it does not, oz logout && oz login fixes it. Otherwise confirm the plan's AI credits are not spent, or store your own provider key - warp --set-provider-api-key <openai|anthropic|google|grok>, or /api-keys inside the TUI. Bring-your-own-key needs no paid Warp plan.
  4. Confirm oz --version succeeds and oz whoami prints your user.
  5. Work in, or point --cd at, the target git repository.

On macOS the CLI is distributed as a signed Developer ID binary; a first run may be held by Gatekeeper until it is approved.

Choose the model (optional)

Omit --model to use Warp's configured default. To pick another, choose an id from oz model list and pass it verbatim. The relay accepts letters, digits, and . _ : / - only, so a value cannot be mistaken for another oz flag.

The loop

Run these five steps per task. Steps 1, 4, and 5 require judgment; 2 and 3 are mechanical.

1. Write the brief

Warp sees only the text you send plus what it can inspect in the workspace - no chat history or shared context. Include the goal, current state, what to change, what to leave untouched, the project's actual gates, and a report contract. Tell it not to commit. Keep one task per brief. The brief is delivered as the --prompt value on argv, so it is visible in the host process list - keep secrets out of it and reference workspace files instead. See references/writing-the-brief.md.

2. Dispatch

Use the bundled relay. It runs oz agent run --output-format ndjson, captures the event stream, and writes result.json. (<skill-dir> is the installed folder containing this SKILL.md.)

bash
node "<skill-dir>/scripts/relay.mjs" --brief brief.txt --cd /path/to/repo
# choose a model:                          add --model <id from oz model list>
# use an agent profile:                    add --profile <id>
# label the run:                           add --name <label>
# continue an existing conversation:       add --conversation <id> (delta brief only)
# base the run on a Warp skill:            add --skill <name|repo:name|org/repo:name>
# start MCP servers:                       add --mcp <path-or-inline-json>  (repeatable)
# suppress the workspace snapshot upload:  add --no-snapshot
# hard time limit (watchdog):              add --timeout 2h  (the 30m default suits short runs; implementation briefs routinely need 1-2h)
# see all options:                         node .../relay.mjs --help

The relay pins the workspace with both the child process's cwd and Warp's own --cwd. It writes artifacts under the system temp dir by default and never commits. See references/dispatch-and-poll.md.

3. Wait for completion

The relay blocks until oz finishes. Run it with the orchestrator's background-command facility, or background it in the shell and poll for result.json. A pre-run usage error exits 2 and writes no result; a missing oz exits 127 and writes status: "warp_unavailable".

Trust process state and the working tree over a progress display. Completion means the process exited and result.json exists. Warp's report is the finalMessage field in result.json (also printed on stdout between the report markers); the raw event stream is always in events.jsonl.

4. Review - do not trust the self-report

Treat Warp's final message and gate claims as claims:

  • Re-run the project's gates yourself.
  • Read the diff against the brief, starting with touchedFiles.
  • Run relevant guard skills if installed.
  • Round-trip migrations and grep for dangling references after removals or renames.

Because there is no read-only mode to fall back on, the diff is the only record you get - and it records what git can see in the workspace afterward, not everything the run did. Dispatch from a clean tree so the two are as close as they can be. See references/review-and-land.md.

5. Land it

The implementer edits the working tree; the orchestrator commits. Commit only after the gates pass and the diff holds. If rework is needed, send a delta brief with --conversation <id> using the conversationId from result.json, then review again.

Autonomy and permissions

oz agent run has no sandbox, no permission mode, and no read-only mode. A headless run reads, writes, edits, and executes commands with your own user permissions and never prompts. There is nothing in the CLI to restrict that surface, so this relay ships no --read-only flag - offering one would imply an enforcement that does not exist. The controls you actually have are:

  1. Scope by directory. --cd pins the workspace, and the relay passes it to Warp's own --cwd. Treat this as aim, not a fence: on oz 0.2026.05.27 shell commands did run in the pinned workspace, but the agent's file tool resolved bare relative paths against $HOME. Name absolute paths in the brief - see references/writing-the-brief.md.
  2. Review the diff. touchedFiles is git status --porcelain taken after the run - post-run, git-visible worktree state, not a log of what the agent did. It cannot show an ignored file, an edit the run made and then reverted, or a write outside the repository (see item 1), and it carries anything that was already dirty before dispatch. Dispatch from a clean tree so those are the same set, and treat the diff as the best available record, not a complete one.
  3. Snapshot egress. --no-snapshot forwards Warp's flag so the end-of-run workspace snapshot is not uploaded. Without it, the upload is Warp's default.

--auto-approve belongs to the interactive warp TUI and has no bearing on oz agent run.

Authorization model

Delegation is something the human opts into. Once they have ("run this queue", "proceed"), committing verified, gate-passing work is the agreed contract. Two limits remain: surface, don't absorb (report Warp's design decisions, defensible-but-unasked turns, and non-blocking nitpicks) and stop for scope changes (if correct completion needs going beyond the brief, ask instead of expanding the mandate). See references/review-and-land.md.

References

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 Warp Delegate AI skill do?

Delegate a coding task to the Warp Agent CLI (`oz`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Warp - phrasings like "have Warp implement X", "delegate this to the Warp CLI", "run it through Warp", "use oz to implement/fix/refactor" - or wants to run a queue of coding tasks through Warp while staying the reviewer. DO NOT USE for tasks small enough to do inline, when the user wants the code written directly without delegating, or for the interactive `warp` TUI (this skill drives the headless `oz agen...

Why use Warp Delegate on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/amElnagdy/delegate-skills/tree/master/skills/warp-delegate. 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 Warp Delegate?

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 Warp Delegate?

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

Is the Warp Delegate 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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