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Poteto Help

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ScriptedAlchemy
poteto-help

Guides users through pstack setup, $poteto-mode, and picking the skill, playbook, or principle for a task. Type $poteto-help with a question.

Overview

PublisherScriptedAlchemy
Repositorypstack-codex
Skill namepoteto-help
Stars
77
Forks
7
Bundled files
3
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.

  • 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 ScriptedAlchemy on GitHub. Read the source before you install it.

Installation

Install the Poteto Help 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/ScriptedAlchemy/pstack-codex/tree/main/plugins/pstack/skills/poteto-help
  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/ScriptedAlchemy/pstack-codex.git /tmp/pstack-codex
mkdir -p .claude/skills
cp -r /tmp/pstack-codex/plugins/pstack/skills/poteto-help .claude/skills/poteto-help
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Poteto Help 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 Poteto Help 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 Poteto Help 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.

Before following this workflow, read the Codex runtime contract. It defines plugin paths, model configuration, delegation, and persistence.

Poteto help

Answer the user's question about pstack, hand them a prompt they can send, and link the file the answer came from. For a help question, don't start the work. The user asked how, and a pstack run spends real tokens, so let them send the prompt.

A message that asks for work, such as "use pstack to fix this bug", is not a help question. Read poteto-mode, do the work under it, and retain any user instruction to apply it throughout this task in durable notes.

This file maps questions to the skills and guide pages that hold the answers. Those files own the details. Read the file you route to before you quote it, and trust it when it disagrees with this map. The links here point into the installed plugin, which the user may not be able to open, so give the user the file's public copy: https://github.com/ScriptedAlchemy/pstack-codex/blob/main/plugins/pstack/ followed by its path.

Find out what they need

Infer the need from the message and the conversation. A named situation, such as "which skill reviews a PR?", goes straight to its section. If the need is still unclear, ask one multiple-choice question with these options, then answer only the section they pick:

  • Get set up
  • Start a task with $poteto-mode
  • Pick a skill for a situation
  • Fix a run that went wrong
  • Make pstack my own

Check the state that changes the answer, and mention it only when it does:

  • No ~/.codex/pstack-models.md means $setup-pstack hasn't run for this user, so every role uses its default model.
  • No verify-* skill or other app harness in the project means agents have no scripted way to drive the app. Mention $create-verification-skill when the question is about proving a change works.
  • No formal verification skill in the project means no property is checked past what tests reach. Mention $create-formal-verification-skill when the question is about a race, an invariant, or another case tests rarely hit. When one exists and the code it covers changed after the commit it records as last in sync, mention $maintain-formal-verification-skill if the question touches that code.

When the model rule is missing and it matters, ask whether the user wants to pick a model for each role and a reasoning budget now. It matters when the user is new, the question is about setup or cost, or the answer depends on which models run. Ask at most once per chat. If the need is also unclear, ask both questions together. Offer two choices:

  • Now: give them $setup-pstack to type, and answer their question too.
  • Later: answer their question, and add one line saying every role keeps its default model until they run $setup-pstack.

Get set up

  1. Install with codex plugin marketplace add ScriptedAlchemy/pstack-codex, then codex plugin add pstack@pstack-codex. Start a new Codex task.
  2. Run $setup-pstack. It asks for a reasoning budget, maps a model to each role, and writes ~/.codex/pstack-models.md. Delegated roles read that file. It then offers to generate an app verification skill and a formal verification skill when the project lacks them.
  3. Start a real task with $poteto-mode, a goal, and a check that can pass or fail.

Installing changes nothing until the user invokes a skill. Only $setup-pstack loads from the user's words. The README and guide page 1 have the details. Offer to word their first prompt with them, per references/prompting.md.

If cost is the worry, say where the tokens go and how to spend fewer. pstack spends extra tokens on subagents and review panels. Rerun $setup-pstack and pick a smaller budget or cheaper models. A role set to auto or inherit-parent runs on the chat's model, which saves tokens when the chat runs on Auto or a cheaper model. A shorter panel list runs fewer subagents, one for each entry. Save $poteto-mode for work that needs rigor.

This is the Codex port of pstack. Its runtime contract defines collaboration, worktrees, model overrides, heartbeat automations, and persistence. Installation does not register Cursor Custom Modes or provide Cursor built-ins.

Start a task with $poteto-mode

$poteto-mode matches the task to a playbook, copies the playbook's steps into the todo list, and runs the other skills as the steps need them. A step it skips stays in the list as skip: <reason>. A good prompt states the goal and how to tell it's done. It doesn't list skills, because a hand-written sequence tends to drop or reorder steps the playbook would keep. Read references/prompting.md before you help word one. Guide page 2 has examples.

Ask Codex to use $poteto-mode throughout the current task, and preserve that standing instruction in resume notes. For persistence in a repository across tasks, read the optional scoped AGENTS.md method and use it only when requested.

For subagents, follow CODEX.md and pass the absolute persona instruction path. Use a named agent only when the active host exposes that role. Spawning does not isolate a checkout or create a cloud VM.

Pick a skill

The default answer is $poteto-mode, which runs most of the others when its steps need them. Name a skill directly when the user wants more or less of something than the playbook gives. Read the skill before you recommend it, and give one example prompt.

The user wants toSkill
Do any non-trivial task with rigor$poteto-mode
Know how code works now, or where new code should live$how
Know why code is shaped this way, or where a number came from$why
Understand a change or subsystem, explained plainly$teach
Catch up on their own recent work on a topic$recall
Catch up on this chat and pending decisions$ketchup
Catch up like $ketchup, in a short silent video of slides, with the waiting calls as text under it$ketchup-video
Know what a small diff could break outside itself$blast-radius
Settle types and module shape before code that crosses a function boundary$architect
Get several attempts at one brief, merged into the best one$arena
Run parallel checks over slices, or race workers, through Codex subagents$swarm
Have several models review a diff and try to break it$interrogate
Fix a bug test-first when a cheap local test exists$tdd
Apply TypeScript rules to .ts or .tsx work$typescript-best-practices
Strip comments before review, using a reviewer that didn't write them$no-comments
Clean AI tells out of prose$unslop
Write docs, an RFC, a README, a PR description, or a commit message to a standard$technical-writing
Hear the last reply again in plain words$bro
Give agents a scripted way to drive the app and prove behavior$create-verification-skill
Bring a verification skill and its feature map back in line with the app$maintain-verification-skill
Vet a performance number before reporting or acting on it$benchmark-checklist
Run a large or cross-cutting change, or one to review after stepping away$figure-it-out
Keep a decision log during a run, and review it afterward$show-me-your-work
Pick a model for each role and a reasoning budget$setup-pstack
Turn their own working habits into a personal mode skill$automate-me
Turn what a finished task taught into skill edits$reflect
Stop agents from repeating the same mistakes in this repo$correct
Build a page over an existing Codex endpoint or the bundled loopback bridge$make-bot-ui
Find their way around pstack$poteto-help
Check that the agent understood their goal and the problem before it starts$in-your-own-words
Get a prompt rewritten deeper, not bigger, before sending it: what a top professional would add, their raw material and methods, and an outside check. It returns the prompt and doesn't do the task$sharpen
Formally verify a race, invariant, or other property that tests rarely reach$create-formal-verification-skill
Bring a formal verification back in line after the code it covers changed, and learn whether a failure is drift or a bug$maintain-formal-verification-skill
Show a PM or designer what the user sees as a chart checked by a model, with each bug drawn as a numbered path, turn an agreed chart into a TLA+ skeleton, or audit the code against its TLA+ spec$statechart

If a skill directory next to this one is missing from the table, read its frontmatter and route by its description. The principle-* directories are covered under principles below.

Close calls:

  • $how explains what the code does. $why explains the reasons. $teach runs one or both and explains the result plainly.
  • $arena gives every worker the same brief and merges the best parts. $swarm splits work into slices or a race and returns one report.
  • $architect implements right after it settles the design. Add "with checkpoint" to review the design before it writes code.
  • $interrogate reviews the diff. $blast-radius looks for breakage outside the diff and proves the one fact that makes the change safe.
  • $recall rebuilds context across recent chats. $ketchup covers only this chat since the user's last message. Resuming one specific chat or branch is the Session pickup playbook.
  • $figure-it-out designs one rigorous run. The Orchestrate playbook runs a program that spans days and many PRs. The Autonomous run playbook drives one task to a finish condition.

Host capabilities:

  • A focused diff cleanup pass replaces Cursor deslop. Use the available browser, computer-use, terminal, or project verification skill for live proof.
  • Use Codex skill-creator for authoring and the supported automation tool for future wakeups.
  • Orchestrate is a poteto-mode playbook, not a separate pstack skill.

Playbooks and principles

Playbooks are step lists inside $poteto-mode, not skills, so they have no slash command. Inside $poteto-mode, describing the task picks one, and these phrases name one directly:

  • "babysit this pr" or "check on pr 123" runs Babysit. It drives the PR to merge-ready and stops there. It doesn't merge unless the user asks to merge, land, or ship.
  • "land the stack" runs Shipping.
  • "take over this branch" runs Session pickup.
  • "pause safely" runs Pause safely.
  • "full autopilot on this queue" runs Autopilot-full. "stack them, don't ship" runs Autopilot-stack.
  • "run the eval playbook" runs Eval.

Name $poteto-mode when you want pstack's Babysit playbook. The Playbooks section of poteto-mode lists every playbook and when it applies. Guide page 6 covers opening, babysitting, and landing a PR.

pstack has no separate planning skill. Use the host's available planning flow when appropriate. For work that spans phases or stacked PRs, asking $poteto-mode for a plan runs the Multi-phase plan playbook, which writes the plan and doesn't implement it. For a design question, the Prototype playbook or $architect settles it in code first.

Principles are one-rule skills that $poteto-mode reads and cites in its replies. The user rarely invokes one. They steer with the names instead, as in "apply prove it works. show me the real output." Typing $principle-<name> still loads one on demand. Guide page 8 lists them.

Fix a run that went wrong

SymptomFix
The mode stopped applying after a few turnsRestate the task-long standing instruction and retain it in resume notes, or invoke $poteto-mode for the next task.
A question got treated as the next step of the last taskSay "new task", or say the turn doesn't need the mode.
A new model choice had no effectCheck ~/.codex/pstack-models.md and the active host model schema before the next delegation.
Runs cost more than expectedSee the cost paragraph under Get set up.
A skill didn't load on its ownOnly $setup-pstack loads from the user's words. The others load when the user types them or when $poteto-mode runs them, and it doesn't run every skill.
Parallel agents overwrote each otherGive each agent its own worktree, or run them run sequentially. Codex subagents share the host workspace.
An overnight run moved but finished nothingA heartbeat or bounded monitor needs a check that can pass or fail, not a duration. See guide page 7.
The reply claims success from a green buildAsk for the real command, flow, stored value, or profile. That's the prove-it-works principle.

For a run that drifts, references/prompting.md has one-line steers. Guide page 10 has more pitfalls and the recipes worth copying.

Make pstack my own

  • $automate-me drafts a personal mode skill from the user's own history, to use alongside $poteto-mode.
  • $automate-maintainer captures named GitHub users or groups from their engineering activity.
  • $automate-team discovers a repository's active core contributors and captures team conventions.
  • $reflect after a session turns its lessons into skill edits the user approves.
  • $poteto-mode write a skill for <workflow> runs the authoring playbook. The eval playbook tests a skill change blind.
  • Fix a misbehaving skill in its own PR, not inside the feature work where it went wrong.

Guide page 9 covers each of these.

Reply

Lead with the answer. Give at most one example prompt in a code block, adapted from references/recipes.md when one fits, then the link to that file. Keep it short unless the user asked for the whole map.

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 Poteto Help AI skill do?

Guides users through pstack setup, $poteto-mode, and picking the skill, playbook, or principle for a task. Type $poteto-help with a question.

Why use Poteto Help on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ScriptedAlchemy/pstack-codex/tree/main/plugins/pstack/skills/poteto-help. 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 Poteto Help?

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 Poteto Help?

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

Is the Poteto Help AI skill free?

Yes. It is published on GitHub by ScriptedAlchemy 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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