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Corral Launch Agents

CommunityPopular
davidondrej
corral-launch-agents

Launch new CLI coding agents through Corral Design 1, or correctly reopen existing agent sessions inside a Corral-active Herdr repository. Use when the user asks to launch, spawn, batch-start, reopen, relaunch, or resume agents. New launches create Corral-owned tasks; resumptions reuse existing worktrees and sessions.

Overview

Publisherdavidondrej
Repositoryskills
Skill namecorral-launch-agents
Stars
4.1K
Forks
599
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 davidondrej on GitHub. Read the source before you install it.

Installation

Install the Corral Launch Agents 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/davidondrej/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/corral-skill-backups/corral-launch-agents-codex-20260805T203558Z-70736 .claude/skills/corral-launch-agents
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Corral Launch Agents 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 Corral Launch Agents 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 Corral Launch Agents 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.

Corral Launch Agents

Launch agents through Corral's real launch pipeline. Let Corral create the branch, worktree, Herdr workspace, pane, agent process, metadata, and persisted task record. Do not recreate that sequence with raw Git or Herdr commands.

Resolve the request

Distinguish these targets before acting:

  • Specific repository: pass its checkout path with --repo. Corral resolves the primary checkout and creates a fresh worktree.
  • Specific new worktree location: also pass --worktree-path. The destination is for a new Corral-created checkout; it is not an existing Herdr workspace ID.
  • Existing Corral/Herdr agent or resumable CLI session: do not launch another Corral task. Use the herdr and relevant harness skill instead.
  • Existing external worktree: use Corral adoption only when the user asks to adopt it. Launching always creates a new task/worktree.

Treat launching as a state-changing action. Execute it only when the user asks to launch; inspection, explanation, or dry-run requests do not authorize a launch.

Reopen existing Cursor CLI sessions

Corral's launch helper is the wrong tool for resumptions. Load the herdr and cursor-cli skills, then:

  1. Identify the named Herdr session, primary repository checkout, original linked worktree, and exact Cursor chat ID.
  2. Select only non-empty chats. Use meta.json's updatedAtMs or transcript update time—not directory modification time. Distinguish the parent chat from review subagents.
  3. Inspect live panes first. Never resume one chat ID concurrently in two panes; reuse the existing pane. If it is open in an incorrectly created top-level workspace, confirm it is idle with no unsent draft, close only that workspace, and never delete the worktree.
  4. Reopen the existing linked worktree with repository metadata:
bash
herdr --session "$SESSION" worktree open \
  --cwd "$PRIMARY_CHECKOUT" \
  --path "$WORKTREE_PATH" \
  --label "$WORKTREE_NAME" \
  --no-focus

This opens the existing path; it does not create a Git worktree or Corral task. It is the required Corral-compatible layout because it groups the worktree beneath its repository while preserving the worktree name. If the result says already_open: true, reuse that linked workspace. Do not use workspace create; it lacks Git worktree metadata and produces a top-level space. Do not use tab create under the primary workspace; it shows the repository name instead of a linked worktree entry.

  1. Capture root_pane.pane_id from the result and resume the exact chat without sending a prompt:
bash
herdr --session "$SESSION" pane run "$PANE_ID" \
  "cursor-agent --yolo --trust --resume $CHAT_ID"
  1. Verify the workspace has the expected checkout_path, repo_root, shared repo_key, and is_linked_worktree: true. Then verify the history:
bash
herdr --session "$SESSION" workspace list
herdr --session "$SESSION" agent wait "$PANE_ID" --until idle --timeout 30000
herdr --session "$SESSION" pane read "$PANE_ID" \
  --source recent-unwrapped \
  --lines 200

Confirm the terminal title, conversation tail, and foreground_cwd. Resuming restores conversation history, not the old process environment.

Corral adoption is separate and normally unnecessary. Use it only when the user explicitly asks to turn an external existing worktree into a persisted Corral task. Do not adopt merely to restore Herdr grouping or resume a CLI chat.

Use the helper

Resolve the installed skill directory without hardcoding a machine path:

bash
CORRAL_LAUNCH_SKILL_DIR="${AGENT_SKILLS_DIR:-$HOME/.agents/skills}/corral-launch-agents"
CORRAL_LAUNCH_HELPER="$CORRAL_LAUNCH_SKILL_DIR/scripts/corral_agents.py"

Run preflight checks first. Always pass the intended named Herdr session when known:

bash
python3 "$CORRAL_LAUNCH_HELPER" doctor --session corral
python3 "$CORRAL_LAUNCH_HELPER" list-presets --session corral --agent pi

If the session is stopped, report that clearly. Do not silently start, stop, or delete Herdr sessions.

Dry-run the exact launch before creating anything:

bash
python3 "$CORRAL_LAUNCH_HELPER" launch \
  --session corral \
  --repo <repository-checkout> \
  --task "<task title>" \
  --preset pi \
  --priority 2 \
  --prompt "<task prompt>" \
  --no-focus \
  --dry-run

Review the resolved session, preset, repository, worktree destination, base, priority, dirty-checkout policy, and prompt length. Then repeat without --dry-run only when the launch is authorized.

To choose an exact new worktree directory, add:

bash
--worktree-path <new-worktree-path>

To launch several agents, use batch with a JSON task file. Corral itself permits many launcher processes but caps the preparation pipeline at three concurrent jobs. Read references/batch-launches.md before a batch launch.

Verify every launch

The helper returns only after Corral has created the worktree and Herdr has detected the interactive agent. Verify persisted and live state:

bash
python3 "$CORRAL_LAUNCH_HELPER" status \
  --session corral \
  --task "<task title>" \
  --live

Report the Corral task ID, title, priority, preset, worktree path, pane ID, persisted launch status, and live Herdr agent status. If launch fails after worktree creation, preserve the failed task/worktree for diagnosis; do not clean it automatically.

Safety rules

  • Never pass --allow-dirty without explicit user acceptance. It does not copy dirty primary-checkout changes; it merely proceeds without them.
  • Never use --mock-state outside Corral's smoke test.
  • Never insert API keys into command arguments, prompts, output, or preset files. Use the provider's normal credential store or environment.
  • Never edit presets.toml merely to inspect it. Ask before adding or changing a preset because it affects future launches.
  • Do not use Pi's --print, RPC mode, export, or model-list commands in a Corral preset. Corral and Herdr expect a long-lived interactive TUI.
  • Prefer --no-focus for automation so a batch does not steal the user's active pane.
  • Use the same Herdr binary/session that runs Corral; a different binary or session can target the wrong runtime.

Read detailed references

  • Read references/corral-design1.md before troubleshooting, changing presets, using the raw Corral CLI, or handling unusual runtime paths. It documents the architecture, every native launch option, preset fields, environment variables, state model, and failures.
  • Read references/pi-agent.md before selecting Pi provider/model/thinking/session/tool/skill options or creating a specialized Pi preset.
  • Read references/batch-launches.md before launching multiple agents from a file.

For helper options, run:

bash
python3 "$CORRAL_LAUNCH_HELPER" --help
python3 "$CORRAL_LAUNCH_HELPER" launch --help

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 Corral Launch Agents AI skill do?

Launch new CLI coding agents through Corral Design 1, or correctly reopen existing agent sessions inside a Corral-active Herdr repository. Use when the user asks to launch, spawn, batch-start, reopen, relaunch, or resume agents. New launches create Corral-owned tasks; resumptions reuse existing worktrees and sessions.

Why use Corral Launch Agents on TypingMind?

Because you install it once and use it with any model. Corral Launch Agents 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 Corral Launch Agents in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davidondrej/skills/tree/main/corral-skill-backups/corral-launch-agents-codex-20260805T203558Z-70736. 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 Corral Launch Agents?

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 Corral Launch Agents?

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

Is the Corral Launch Agents AI skill free?

Yes. It is published on GitHub by davidondrej 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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