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Opencode Delegate

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amElnagdy
opencode-delegate

Delegate a coding task to the OpenCode CLI as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to OpenCode — phrasings like "have OpenCode do X", "delegate this to OpenCode", "run it through OpenCode", or "use OpenCode to implement/fix/refactor" — or wants to run a queue of coding tasks through OpenCode while staying the reviewer. Prefer it when the user will review the diff and commit it themselves. DO NOT USE for tasks small enough to do inline, or when the user wants the code written directly without delegating.

Overview

PublisheramElnagdy
Repositorydelegate-skills
Skill nameopencode-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 Opencode 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/opencode-delegate .claude/skills/opencode-delegate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Opencode 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 Opencode 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 Opencode 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.

OpenCode Delegate

You are the orchestrator. This skill lets you hand a bounded coding task to a separate implementer — the OpenCode CLI — then review what it produced and land it yourself. You write the brief and own the judgment; OpenCode does the typing in its own session; you verify and commit.

Nothing here is specific to one orchestrating agent. The loop needs only the ability to run a shell command and read a file, so any agent with those two capabilities — Claude Code, OpenCode driving a sibling session, or a comparable one — can drive it. (It is designed for and run on Claude Code; treat other orchestrators as designed-for, not yet proven.)

When NOT to use this

  • The task is small enough to just do inline — delegation overhead is not worth it.
  • The opencode CLI is not installed or not authenticated (run opencode auth login).
  • You want to write the code yourself, or you only need a review (use the plan agent via --read-only).

Prerequisites (check once)

  1. opencode --version succeeds. If not, install (npm i -g opencode-ai, or the native installer from opencode.ai) and opencode auth login.
  2. Confirm which opencode is on PATH. command -v opencode shows the active binary and opencode --version its version. The relay records the version it ran into result.json, so a stale binary is visible after the fact.
  3. A model provider is authenticated — opencode auth list shows at least one credential.
  4. You are in (or will point --cd at) the target git repository.

Choose the implementer model

OpenCode has no safe default — a bare opencode run errors — so a fresh run needs a model via --model or a fleet --lane that sets one (a resumed run inherits its session's model). Naming the model is the one decision a single-model backend like codex-delegate never had, and it has two owners:

  • The human owns which models are allowed. opencode models lists hundreds of entries, most billed per token (OpenRouter and the like); only the human knows which are their flat-rate subscriptions, and the CLI can't tell them apart. So the usable set is theirs — ideally stated once in the repo's AGENTS.md or their CLAUDE.md (e.g. "delegate mechanical work to opencode-go/…, hard logic to ").
  • You, the orchestrator, pick per task — from that set. Match the model to the brief: a cheap, fast model for a mechanical sweep (rename, migration, removal); a strong one for a subtle bug or a money/security path.
  • If no usable set is stated, ask — don't guess. Guessing from the catalog risks a metered model and a surprise bill. Name the constraint to the human and let them choose.

More depth: references/writing-the-brief.md.

The loop

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

1. Write the brief

OpenCode sees only the text you send plus what it can read from the working tree — no chat history, no shared context. Everything the task needs goes in the brief: the goal, the current state, what to change, what to leave untouched, the project's actual gate commands (discover them from the repo's AGENTS.md/CLAUDE.md/Makefile — do not assume), and a report contract. Tell OpenCode it will not commit (you will). Keep one task per brief. Full guidance and a template: references/writing-the-brief.md.

2. Dispatch

Send the brief to OpenCode with the bundled helper. It wraps opencode run, captures the run, and writes a structured result.json — so your only job is "run a command, read a file." (<skill-dir> below is this skill's installed directory — the folder containing this SKILL.md. Claude Code prints it as "Base directory for this skill" when the skill loads; on other orchestrators use that same directory — if unsure where it landed, run find ~ -name relay.mjs -path '*opencode-delegate*' and substitute the directory above it.)

bash
node "<skill-dir>/scripts/relay.mjs" --brief brief.txt --model <provider/model> --cd /path/to/repo
# --model (or a --lane that sets model) is required on a fresh run
# fleet lane from delegate-setup:           add --lane <name>  (dials apply; flags still win)
# read-only (review/diagnosis, no edits):   add --read-only   (uses the plan agent)
# continue the previous OpenCode session:   add --resume-last  (delta brief only; keeps the model)
# hard time limit (watchdog):               add --timeout 2h  (default: off; implementation runs routinely need 1-2h)
# see all options:                          node .../relay.mjs --help

The helper defaults to the write-capable build agent and writes its artifacts to a temp dir, so the repo under review stays clean. It never commits — see step 5. Mechanics, flags, and the result.json shape: references/dispatch-and-poll.md.

3. Wait for completion

The helper blocks until OpenCode finishes, so back it with whatever your orchestrator offers and resume when it returns:

  • Claude Code: run the Bash call with run_in_background: true; you are notified on completion.
  • Plain shell / other agents: run it in the foreground for short tasks, or background it and poll the result file — … & in bash/zsh (including Git Bash/WSL), or your shell's equivalent (Start-Job in PowerShell, start /b in cmd). The run is done when result.json exists with a status. (A pre-run usage error — bad args or an empty brief — instead exits with code 2 and writes no result file, so check the exit code too. A missing opencode binary exits 127 but does write a result.json with status opencode_unavailable.)

Do not trust progress trackers over reality: a run is finished when result.json is written and the process has exited. Read the working tree, not a status line. The implementer's full report is the finalMessage field in result.json (also printed in full on stdout between the report markers).

4. Review — do not trust the self-report

OpenCode's result.json includes its own final message and any gate claims. Re-verify, don't accept:

  • Re-run the project's gates yourself (the test/lint/build commands from step 1). Never take "gates passed" on faith.
  • Read the diff against the brief: did OpenCode do what was asked, nothing more (scope creep) and nothing less? touchedFiles in the result is your starting point.
  • Run the relevant guard skills on the diff if you have them installed (clean-code-guard, test-guard, etc. from guard-skills) — this skill produces the work; those skills judge it.
  • For schema/migration changes, round-trip them; for removals, grep for dangling references.

Full checklist: references/review-and-land.md.

5. Land it

The implementer edits the working tree; the orchestrator commits. Committing should be the act of the party that verified the work. Only after the gates pass and the diff holds:

  • Commit the verified work yourself, with a clear message.
  • If it needs changes, send a delta brief with --resume-last (don't restate the whole task) and review again.

Autonomy model

OpenCode's autonomy is governed by the agent, not a sandbox enum:

  • build (the relay default) — write-capable; edits files in the working dir headlessly. The equivalent of "let it implement."
  • plan (via --read-only) — read-only; reviews and diagnoses without touching the tree. The equivalent of "let it look but not edit."

Permissions auto-approve by default: the relay passes --auto so a headless run never blocks on a prompt no one can answer. That is the point of unattended delegation — the orchestrator's diff review and the implementer sweep (step 4) are the safety net, not a per-action prompt. Pass --no-auto to honor the agent's own permission config instead (allow/ask/deny per action); pair it with an agent whose in-workspace permissions are set to allow, or a headless run can hang waiting on an ask. Read-only (plan) runs never get --auto — auto-approving would let the plan agent's ask-gated edit/bash permissions through and defeat "read-only," so a review can't be tricked into touching the tree.

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 — that is the whole point. Two limits on that mandate: surface, don't absorb (report OpenCode's design decisions, defensible-but-unasked turns, and non-blocking nitpicks rather than silently keeping them) and stop for scope changes (if correct completion needs going beyond the brief, ask — don't expand the mandate yourself). The full treatment is in 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 Opencode Delegate AI skill do?

Delegate a coding task to the OpenCode CLI as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to OpenCode — phrasings like "have OpenCode do X", "delegate this to OpenCode", "run it through OpenCode", or "use OpenCode to implement/fix/refactor" — or wants to run a queue of coding tasks through OpenCode while staying the reviewer. Prefer it when the user will review the diff and commit it themselves. DO NOT USE for tasks small enough to do inline, or when the user wants the code written directly without delegat...

Why use Opencode Delegate on TypingMind?

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

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

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

Is the Opencode 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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