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Redo

OrganizationPopular
udecode
redo

Redo a prior pstack run at the current session's higher effort or stronger model, treating its plan as a draft: rerun only the plan, design, subagent and seat lanes, code and review judgments that ran below the current level, and reuse the rest. Use for "redo <plan, page or PR>", "run poteto again on it at this effort", "treat this plan as a draft", or a teammate's run made at a lower effort.

Overview

Publisherudecode
Repositorydotai
Skill nameredo
Stars
1.2K
Forks
82
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Redo 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.
    https://github.com/udecode/dotai/tree/main/skills/redo
  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/udecode/dotai.git /tmp/dotai
mkdir -p .claude/skills
cp -r /tmp/dotai/skills/redo .claude/skills/redo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Redo 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 Redo 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 Redo 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.

Redo

A run made at a lower effort or on a weaker model is a draft. Redo follows pstack's Session pickup, except that it redoes the work that ran below the current level and reuses everything else. Load pstack:poteto-mode first; its playbooks and the project's gates still own each step.

  1. Find the draft. A plan path is the draft. For a page link, find the plan whose subject or plan file holds that Page: line (git grep -l 'Page: <link>' in the plans directory); for a PR, the plan its body links. Read the plan, its subject file, its decision log and its run directory under <plans>/artifacts/. Never read the published page, because another account may own it.
  2. Read the levels. node .agents/pstack/lead.mjs prints the current session's <model> @<effort>. In the draft's log, each lead row names the model and effort of the session that wrote the rows after it, and each seats row names every seat's level. A subagent role runs at its models-sheet line. A stretch with no lead row, or a seat with no level, counts as below the current level.
  3. Write the ledger. Open a new iteration of the draft's subject with the plan-page skill. Its Brief names the draft and the level change, and its ## Redo table lists every unit of the draft with its producer, that producer's level and one verdict:
    • redo: judgment a producer made below the level the current setup gives its role. That covers the plan and its Defaults, design picks, each subagent or seat lane, each panel finding's disposition, the code each built: row names, and the Close and its claims. It also covers any unit the draft marked weak: a partial, gap, open or inconclusive row, a missing seat, a same-family round or a dismissed critical finding.
    • reuse: deterministic evidence on bytes the redo will not change, such as a test run, a proof log or a render, and any lane already at or above the current level. Cite it, and do not run it again.
    • recheck: reused evidence whose inputs a redo may change. It reruns after the redo. When no unit is redo, say so on the page and stop.
  4. Redo the plan and design first. Work from the owner's asks and the draft's inputs before reading the draft's conclusions, then compare and log each pick kept, changed or dropped. A design step that runs architect or an arena takes the draft's design as one more candidate. An unbuilt draft plan becomes superseded; a built one stays and the new iteration continues it.
  5. Rerun the lanes. Each redo lane runs again on its configured model and level, from its brief in the run directory. Re-judge every reused seat answer at the current level, and log a new panel row for each finding whose disposition changes.
  6. Clean up the code as a PR. Run the project's Babysit playbook, or pstack's, over the draft's diff at the current level. The fixes then take the project's usual gates.
  7. Recheck and close. Rerun each recheck proof. The Close lists what was redone and what changed, what was reused and why, and any unit whose level stayed unknown.

Frequently asked questions

What does the Redo AI skill do?

Redo a prior pstack run at the current session's higher effort or stronger model, treating its plan as a draft: rerun only the plan, design, subagent and seat lanes, code and review judgments that ran below the current level, and reuse the rest. Use for "redo <plan, page or PR>", "run poteto again on it at this effort", "treat this plan as a draft", or a teammate's run made at a lower effort.

Why use Redo on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/udecode/dotai/tree/main/skills/redo. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Redo?

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 Redo?

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

Is the Redo AI skill free?

It is published on GitHub by udecode. 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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