pstack

pstack is a bundle of skills in which developer Lauren Tan (handle: poteto) wrote down how she works, so that an AI can work the same way. It is not a program — it is a stack of instruction files, and its point is a single one: getting the AI to say “here is the evidence it’s fixed” instead of “it should be fixed now”. I didn’t install the bundle. I kept one skill from it as a candidate.

What a “skill bundle” is

AI coding tools such as Claude Code and Cursor read text files that say “in this situation, work like this” and then act on them. One such file is called a skill.

When one person ties a few dozen skills together around one way of working and gives it a name, you get a “something-stack”. pstack is about 30,000 English words of these files — roughly a book. It doesn’t swap the model and it doesn’t install a separate program.

Think of it as a meticulous senior’s notebook

Picture the most careful engineer on your team. When a bug comes in she never fixes it straight away. She reproduces it first, clicks through it herself after the fix, and changes as little code as possible.

Write all of those habits down and hand them to a new hire, and the new hire works — not as well as her, but a lot like her. pstack is that notebook. The new hire is the AI.

	A similar trending bundle (gstack)	pstack
Imitates	A company — CEO, QA lead, and so on	One craftsperson
Splits work by	Who is looking	What the job is (bug, feature, refactor)
Asks at the end	Are we building the right thing?	Does what we built actually work?

The most important skill writes the “launch it and check it” manual

It’s called create-verification-skill, and its author calls it the most important skill in the toolbox. I opened its source in the repository to confirm what it does.

Think of the note you’d leave a new part-timer: where the lights switch on, how you know the shop is open, what to check, where to post the photos, how to lock up. Here the AI writes that note itself, by reading your code. Before handing it over, it walks through the note once, end to end.

	Without the note	With it
The AI’s last line	“Build succeeded, should work”	“I launched it, clicked through, screenshot attached”

I still didn’t take the whole bundle

Three reasons. First, several of its core rules were already in my setup — “label anything you didn’t check as a guess”, “write down why you skipped a step”. Second, pstack splits work its own way, and I already have a workflow that would collide with it. Third, it always opens a pull request, which doesn’t fit solo work pushed straight to main.

So what I kept is the verification skill above — and I haven’t run it yet. It’s a candidate, nothing more.

Move repeated nagging to a stronger layer

The part of the video that stayed with me longest: if you keep telling the AI the same thing, where you put that instruction decides how well it works.

Write “no comments” in a rules file and it sometimes gets ignored. Block it with a linter and it almost never slips. Make it impossible in the code structure and there is no way around it. The author’s team took the last route — the AI copies what it can see in the codebase, so anything it shouldn’t copy was removed from sight.

The detailed record starts here

pstack is a set of SKILL.md files in Cursor’s official plugin repository. One router, poteto-mode, reads the request and picks one of 23 playbooks; the playbook then calls principle skills and task skills in order. I came in through Kim Flip’s Korean walkthrough video (24 minutes, 2026-10-23) and checked the counts and the skill source against the repository separately. Verdict: not adopted as a bundle; create-verification-skill is the one candidate.

What I took apart

Item	Value	Source
Author	Lauren Tan (@poteto) — previously Meta, Netflix, Cursor	Video
Form	Markdown skill bundle, ~30,000 English words	Video
Playbooks	23	Counted in the repo
Subagent definitions	2	Counted in the repo
SKILL.md files	61	Counted in the repo
Reference version	0.15.28 (2026-10-18)	Video description
10k uses a week · 2k PRs a month	The author’s own claim — it varies from 1k to 2.5k depending on when she said it	Video

The plugin’s defaults hand code-writing to Grok and judgement, writing and the hard tasks to Claude Opus 5.5. An install path for Claude Code was added on 2026-10-16. Claude Code has no Grok, so the current session model fills that role — which means the skills where “models from different vendors review each other’s code” can collapse into one model reviewing itself. The video flags this, and it is exactly where the information in a cross-check disappears.

A single router file enforces the order

poteto-mode is one long markdown file (about 17,000 words with the playbooks). It has three parts.

Part	What it does
Router	Reads the request and chooses a playbook. The user never spells out the steps
Working rules	Which skill when, which principle when. Proceed without asking on anything reversible
Answer rules	Tag every claim as measured, inferred or guessed. “Agreement is not the default”

Most of the skills carry disable-model-invocation: true, so the model can’t pull a refactoring principle into an unrelated job just because the word “refactor” appeared. The price is that nothing happens unless you call the router. The gap between “installed” and “switched on” is the same shape I found in the eli5 piece.

The device users credit most is a different one. The playbook’s steps are copied verbatim into the to-do list, and a skipped step stays in the list with a reason next to it. Models quietly drop tedious steps like verification; when dropping one means writing down why, they can’t slide past it.

Where the principles kicked in during the to-do app demo

The video ran a single to-do app through seven stages. There is no playbook for building an app from scratch — this is a one-to-n tool, not a zero-to-one tool.

Stage	Principle at work
Mock-ups	The only place the quality rules flip. The goal is a decision, not code, so compare throwaway HTML fast
Feature	Don’t add an “overdue” boolean next to “done”. State is open or done; overdue is computed from the date (model-the-domain)
Bug	No fix before a reproduction. “A belt-and-braces guard added just in case is a hypothesis, not a fix”
Refactor	Subtract before you add. If it isn’t easier to read, it was a move, not a refactor
Overnight run	One table row per decision. Stop only for irreversible things — force-push, deploy, deletion

The verification skill’s source is stricter than the video

Reading create-verification-skill itself turned up a bar the video didn’t mention. The flow has four steps: interview the repository (ask the human only what the code can’t tell you), write a skill with five sections — launch, doctor, drive, evidence, cleanup — seed a feature map of three to five pages, then run the whole thing once before handing it over.

The extra bar sits in the evidence and cleanup sections.

Rule in the source	What it prevents
Drive the real user path, not internal setters or test-only endpoints	Passing via a shortcut
Capture the action and the resulting state, not just the last screen	One convincing final screenshot
Don’t trust a dry-run’s name — observe what it actually skips	A “dry run” that still touches the network
After cleanup, confirm the evidence still exists — cleanup that eats the proof fails	Saying “verified” with the proof deleted
Never kill by process name; kill only what you started	Closing the user’s other windows
If a precondition is missing, report a failure instead of weakening the journey	Trimming the test until it passes

My setup already has incident notes under nearly the same names as the last two rows. Same conclusion, reached by a different road.

Put the nagging in the strongest layer you can

The author’s team banned comments outright in their internal framework. Their reason: when the AI saw comments like “temporary hack, fix later”, it concluded that sloppy was fine here and followed suit. For the same reason they deleted old half-done code and kept exactly one way of doing each thing. pstack’s cut skill does this job — it finds the mistakes you keep pointing out and fixes them in the strongest layer available.

By the same logic, automation comes last. Wiring bug reports straight into an AI that fixes them looks great, but bolt it on before the repository is mature and you just mass-produce sloppy pull requests. The video’s line for it: a Michelin kitchen, not a software factory — run a hundred agents before you can trust one and you get a hundred times the garbage.

The honest limits are in the author’s own history

Limit	Evidence
The gap shrinks as models improve	A 2026-09-23 commit is titled “remove 19 more pieces of knowledge Opus 5.5 doesn’t need”. The value is moving from teaching how to work to enforcing order and demanding evidence
It’s slower	In one case a 30-minute task took an hour — and caught the AI misreporting
Most of it is prose, not enforcement	Even “no merge without verification” is a sentence, not code — layer 4 on the ladder above

The third row is the most interesting thing in this piece. pstack teaches “move your nagging to a stronger layer” while living mostly in layer 4 itself. It is one more case of something this section keeps finding: the tool that breaks the claim is carried by the subject itself.

Verdict — not the bundle, one candidate

Subject	Verdict	Why
poteto-mode as a whole	Not adopted	The answer tags and “write down skipped steps” already exist here; the router collides with my existing workflow; always-a-PR doesn’t fit solo work
create-verification-skill	Candidate	The gap between “the code went in” and “it runs on screen” has actually been empty in my setup before, and the source’s evidence rules aim right at it
The five layers	Checklist only	For finding rules that sit only in a rules file (layer 3), keep getting broken, and could move down into a hook or gate (layer 2)

What I didn’t do

• I didn’t install pstack. The verdict comes from reading the video and the repository, not from running anything.
• I haven’t run the verification skill even once. By its own standard this verdict is still a draft — until it runs, it’s a candidate and no more.
• Usage per week, PRs per month and the “30 minutes to an hour” case are all someone else’s numbers. I didn’t reproduce them.
• The video’s captions were auto-generated and mangled most proper nouns. Names and numbers were corrected from the video description and the repository.

The line that stays

“A skill that has never been run is a draft, not a deliverable.” — the bar set in the verification skill’s own source, and it applies to this piece’s verdict too.