pstack is a collection of plain Agent Skills and engineering principles that guide AI-assisted software development across coding agents such as Claude Code, Codex, Pi, Cursor, and OpenCode. It is for developers who want reusable practices for tasks including testing, technical writing, debugging, review, and coordinating agent work. The catalogue entries are the project's skills, agents, and plugin.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add backnotprop/pstack --skill architectgit clone --depth 1 https://github.com/backnotprop/pstackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/backnotprop/pstack/architect)<a href="https://agentmods.dev/skills/backnotprop/pstack/architect"><img src="https://agentmods.dev/badge/skills/backnotprop/pstack/architect/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/backnotprop/pstack/architect"><img src="https://agentmods.dev/badge/skills/backnotprop/pstack/architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.01174 |
| Opus 5.5 | $0.00021 | $0.00470 |
| Sonnet 5.5 | $0.00010 | $0.00235 |
| Haiku 4.5 | $0.00005 | $0.00117 |
Grade A, and why
architect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to architect — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architect
Sketch types, function signatures, class shapes, and module boundaries with not implemented bodies and pseudocode. Synthesize across multiple model perspectives, then fill in code against the chosen sketch.
Start
Open a todolist with one entry per phase before starting.
- Ground
- Sketch
- Agree
- Implement
- Scrap
Phase A: Ground the problem
Build a real mental model of every system the new code touches. Run the how skill over the relevant subsystems. Naming a file isn't grounding. Produce the architectural explanation how prescribes. If the design redefines ownership or layering, also run the why skill on the existing shape so the rationale becomes a constraint, not a guess.
Skip Phase A only when the work is genuinely greenfield with no surrounding system to integrate.
Phase B: Sketch
Run the arena skill with the design-sketch task and the Phase A grounding artifacts. Pass references/runner-prompt.md as each runner's prompt.
Take the runners from the architect runners line in the pstack settings file (~/.cursor/rules/pstack-models.mdc in Cursor, ~/.claude/rules/pstack-models.md in Claude Code, ~/.agents/pstack-models.md in other harnesses), in place of the arena runners line. If the file or that line is missing, use claude-opus-5-5-xhigh and grok-4.7-xhigh-fast. Alias and rejected entries follow the runner rules in the arena skill's Phase A.
Design it twice. Require at least two structurally distinct candidates before synthesis, even when the first looks sufficient. This is the exhaust-the-design-space principle skill made concrete. Whole-shape alternatives, not point fixes inside one shape.
Screen every candidate against references/design-red-flags.md before synthesis. Assume the next contributor is an agent that sees only the files it opened, copies the nearest example, and takes the shortest path that compiles. Prefer the design where a change that looks right from one file is right for the whole repo.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed 8b06915a5e72
- 9d ago Changed · -4 lines 1a1c56ec1fe3
- 13d ago Changed cb2507870a6a
- 17d ago Changed 0d10a7ddc7e0
- 1mo ago Changed 897a59beaa3c
- 1mo ago First seen · 84 lines · 52 tokens per session scan A 585d7a9e03c0
architect is a skill published in the GitHub repository backnotprop/pstack (2,498 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,174 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to architect, differing in 23 lines, and is treated as a copy.
Other skills, from other repositories
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analyze-tensegrity-system
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assess-context
AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether…