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Run Reviewable Subtask Loop

Community
thienanblog
run-reviewable-subtask-loop

Deliver an explicitly requested multi-subtask plan as sequential reviewed and verified commits with one aggregate publication path. Use only when the user requests or accepts this workflow; subtasks do not authorize subagents.

Overview

Publisherthienanblog
Repositoryawesome-ai-agent-skills
Skill namerun-reviewable-subtask-loop
Stars
66
Forks
21
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Run Reviewable Subtask Loop 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/thienanblog/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/plugins/project-development-skills/skills/run-reviewable-subtask-loop .claude/skills/run-reviewable-subtask-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Run Reviewable Subtask Loop 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 Run Reviewable Subtask Loop 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 Run Reviewable Subtask Loop 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.

Run Reviewable Subtask Loop

Deliver coherent, recoverable increments and review the aggregate result before publication. Use this workflow only after explicit opt-in; a large task or an internal plan alone is insufficient.

Working agreement

Follow the user's request and applicable repository instructions over these defaults. Use existing authorization; ask only about missing decisions that materially affect scope, cost, safety, or the result. Continue independent authorized work while awaiting an answer.

Run in the main conversation by default. Delegation can increase usage: obtain explicit approval for the proposed agent count and scope before using subagents. Reuse that approval within its bounds; ask again before expanding the approved count or scope.

Establish the series

Inspect repository instructions, working-tree and worktree state, the intended base and its fetched remote counterpart, and relevant delivery checks. Follow repository freshness policy; preserve the intended base and user changes. Do not silently merge, rebase, reset, or overwrite work to make the starting state convenient.

Resolve the branch, commit, push, PR, merge, and cleanup permissions from the user's request. A request to implement does not automatically authorize publication or final merge. Ask only about missing permissions when needed, after completing independent preparatory work.

Use one integration branch and sequential commits by default. Use temporary subtask branches when repository policy or an actual isolation/recovery need justifies them. Never check out the same branch in two worktrees. Record initial base and integration SHAs and the exact branches created by this series.

Inspect remote triggers before publication. Read remote-ci.md when push, PR, or merge can start CI. Honor explicit local-only, cost, and separate-approval requirements.

Keep a compact ledger

Use the conversation or an existing project progress surface. Persist a plan only when requested or needed for recovery across sessions; use the repository's established location. Record exact task-owned artifact paths and preserve them according to the user's retention preference.

For each subtask, track its responsibility, acceptance criteria, dependencies, focused checks, status, and resulting commit. Add findings, evidence, and recovery points as they exist. Do not prescribe a fixed number of subtasks or plan files.

Split at boundaries that have distinct review, test, rollout, or recovery value. Keep source and generated output together. Combine mechanical fragments; separate unrelated contracts or risky migrations. Adjust implementation details within the agreed outcome, and ask before materially changing an approved scope.

Execute each subtask

  1. Start from the verified integration tip, using an isolated branch only when warranted.
  2. Implement the complete responsibility with needed tests, documentation, and generated artifacts.
  3. Run checks for that responsibility and affected shared contracts. Do not run the aggregate suite for every commit.
  4. Review the complete subtask diff. Fix actionable issues and rerun affected checks; revisit the wider review only when the fix changes its assumptions.
  5. Commit when the required checks pass. If using a subtask branch, integrate by the repository-approved method, preferring fast-forward when possible.
  6. Record the commit, review outcome, checks, and last-known-good tip. Recheck integration only when it changes the tested behavior or conditions.

Keep dependent work behind unresolved failures. For invalidated work, read recovery.md and preserve a recovery point before any authorized history change. Temporary branch cleanup follows branch-cleanup.md.

Verify the complete result

Review the complete diff against the intended base and map it to the user's criteria. Fetch the base again before delivery. If it advanced, assess the integration risk and follow repository policy; obtain direction before an unapproved merge or history rewrite.

Run the smallest aggregate checks that cover the changed responsibilities and their contracts, plus required release or repository gates. Reuse valid subtask evidence. Respect explicit suite budgets; propose broader tests only for a concrete gap instead of always asking for a full run.

For material visual work, inspect relevant rendered states and viewports and compare any selected reference. Use earlier browser checks when they help expose design or integration errors; there is no requirement to defer all visual work to the end. Follow the host browser policy and distinguish manual Browser evidence from source-controlled E2E tests.

Record the final commit/tree and the inputs or environment on which checks depended. Changes invalidate affected evidence, not every unrelated check. Revalidate changed contracts after conflict resolution; a documentation-only progress update does not by itself invalidate runtime tests. Provider-required checks may still need the current PR SHA.

Publish and clean up

Use the authorized aggregate path: one PR, or commit and push if that is what the user requested. Keep intermediate branches local unless remote backup or collaboration is requested. Report the result, meaningful review fixes, checks, and gaps in plain language without a mandatory confidence score.

Merge only when requested, repository-required checks pass on the candidate, and no unresolved finding makes the merge unsafe. Verify the resulting commit/tree, including any base changes. An existing passing check does not need a local rerun merely because the next step is squash merge.

Clean up only the exact task-owned branches and temporary files covered by authorization. Keep evidence needed for pending review or recovery; do not delete plan documents the user wants retained. Follow branch-cleanup.md, including its squash-merge checks. Preserve unrelated branches and worktrees.

Resume or report a blocker

Reconcile the ledger with actual Git state, review status, and verification evidence. Resume from the first incomplete step, keeping completed work. If ownership, authorization, base freshness, protected-branch checks, or an unresolved scope decision prevents safe progress, explain the precise blocker and continue any independent work that remains possible.

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 Run Reviewable Subtask Loop AI skill do?

Deliver an explicitly requested multi-subtask plan as sequential reviewed and verified commits with one aggregate publication path. Use only when the user requests or accepts this workflow; subtasks do not authorize subagents.

Why use Run Reviewable Subtask Loop on TypingMind?

Because you install it once and use it with any model. Run Reviewable Subtask Loop 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 Run Reviewable Subtask Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/thienanblog/awesome-ai-agent-skills/tree/main/plugins/project-development-skills/skills/run-reviewable-subtask-loop. 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 Run Reviewable Subtask Loop?

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 Run Reviewable Subtask Loop?

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

Is the Run Reviewable Subtask Loop AI skill free?

Yes. It is published on GitHub by thienanblog under the Apache-2.0 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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