Delegate Sol logo

Delegate Sol

Organization
instructa
delegate-sol

Delegate a bounded task from a Codex session to GPT-5.6 Sol in the same Herdr workspace, then wait for and collect the result. In review mode, require Sol to use parallel GPT-5.6 Terra subagents with medium reasoning for read-only context gathering before Sol makes the final judgment. Use for requests such as "ask Sol", "have Sol review this", "get Sol's opinion", "use Sol with Terra context gatherers", or "let Sol implement this".

Overview

Publisherinstructa
Repositoryagent-skills
Skill namedelegate-sol
Stars
141
Forks
16
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Delegate Sol 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/instructa/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/delegation/delegate-sol .claude/skills/delegate-sol
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Delegate Sol 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 Sol 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 Sol 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 to Sol

Delegate through Herdr with one explicit mode:

  • review: Keep Sol and all Terra gatherers read-only. Require Sol to launch two or three distinct gpt-5.6-terra subagents at medium reasoning, collect their evidence, and own the final review.
  • implement: Make Sol the sole writer. Stop editing until Sol finishes, then inspect and verify its work.

Default to review when authorization is ambiguous. Never infer permission to edit from phrases such as "ask Sol", "take a look", "analyze", or "check this". Start Sol with gpt-5.6-sol and xhigh reasoning unless the user explicitly requests another Sol reasoning level.

Prepare the handoff

Create a self-contained task containing:

  • the objective and relevant project context;
  • the exact scope and files when known;
  • current evidence, errors, or competing hypotheses;
  • constraints and explicit non-goals;
  • required verification and return format.

Do not forward the entire conversation. Preserve only information needed for the delegated task.

Before implement, inspect the workspace state and preserve unrelated or pre-existing changes. Tell Sol not to commit, check in, shelve, push, switch branches, or alter workspace configuration unless the user explicitly requested it.

Run the delegation

Resolve this skill's directory and invoke its script. Pass the task with --task, --task-file, or stdin.

bash
bash <skill-dir>/scripts/delegate-sol.sh review \
  --target-title "Sol Reviewer" \
  --task "Review the current change for correctness, regressions, and missing tests."
bash
bash <skill-dir>/scripts/delegate-sol.sh implement \
  --task "Implement the agreed fix within the stated scope and run the relevant verification."

The script must:

  1. Honor --target first, then --target-title. If a requested target is missing, is the current caller pane, or is busy, stop instead of falling back.
  2. Without an explicit target, always create a fresh Sol pane. Reuse only a user-designated --target or the single exact, settled --target-title match when that pane was created by this skill. Never reuse the calling Codex pane or an arbitrary Codex pane.
  3. Start new panes with gpt-5.6-sol and xhigh reasoning, submit the task, wait for settlement, and return visible output. For a fresh pane, place the full task in a mode-600 temporary packet outside the project and pass only a short packet-reading instruction as Codex's initial CLI prompt. Keep the packet until Sol settles, then delete it through an exit trap.
  4. In review, require Sol to spawn two or three parallel, read-only context gatherers with model: "gpt-5.6-terra", reasoning_effort: "medium", and fork_turns: "none". Give each a distinct evidence-gathering scope, wait for all of them, and synthesize their results. Terra gatherers must not decide approval or edit files.
  5. Correlate the captured terminal tail to Sol's final response with a unique per-invocation handoff ID. Verify that it reports two or three distinct structured Terra records, each with the required model, effort, fork setting, distinct scope, and evidence disposition.
  6. Leave the Sol pane open for follow-up work.

Do not manually duplicate these steps unless the script reports a concrete failure.

Default to one complete Sol turn per hypothesis. If the response lacks the required handoff sections, report an incomplete handoff; do not automatically send an open-ended continuation. On timeout in a fresh pane owned by this invocation, send Escape, collect its last output, and stop. For an explicitly reused pane, leave it untouched on timeout to avoid cancelling unrelated work.

Handle the result

For review, report Sol's findings and Terra-gathered context alongside your own assessment. Do not implement findings unless the user separately authorizes changes.

For implement:

  1. Resume ownership only after Sol settles.
  2. Inspect the actual changed files and version-control state.
  3. Run proportionate verification independently.
  4. Report what Sol changed, what you verified, and any remaining risk.

If Sol is blocked, show the blocking output. Never auto-approve permissions, destructive actions, commits, check-ins, shelvesets, pushes, or branch changes.

If no Herdr environment is available, stop and explain that the skill must run from an agent pane inside Herdr.

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 Sol AI skill do?

Delegate a bounded task from a Codex session to GPT-5.6 Sol in the same Herdr workspace, then wait for and collect the result. In review mode, require Sol to use parallel GPT-5.6 Terra subagents with medium reasoning for read-only context gathering before Sol makes the final judgment. Use for requests such as "ask Sol", "have Sol review this", "get Sol's opinion", "use Sol with Terra context gatherers", or "let Sol implement this".

Why use Delegate Sol on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/instructa/agent-skills/tree/main/skills/delegation/delegate-sol. 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 Sol?

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 Sol?

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

Is the Delegate Sol AI skill free?

It is published on GitHub by instructa. Check the repository for licensing terms. 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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