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 principle-minimize-reader-loadgit 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/principle-minimize-reader-load)<a href="https://agentmods.dev/skills/backnotprop/pstack/principle-minimize-reader-load"><img src="https://agentmods.dev/badge/skills/backnotprop/pstack/principle-minimize-reader-load/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/principle-minimize-reader-load"><img src="https://agentmods.dev/badge/skills/backnotprop/pstack/principle-minimize-reader-load.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 | $0.00044 | $0.00416 |
| Opus 5.5 | $0.00018 | $0.00166 |
| Sonnet 5.5 | $0.00009 | $0.00083 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
principle-minimize-reader-load 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 9d ago.
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
94% identical to principle-minimize-reader-load — 2 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.
What it actually says
Minimize Reader Load
Maintainability is the work a reader must do to understand code. Track two axes: layers to trace (indirections between the question and the answer) and state to hold (hidden or mutable context the reader must keep in their head). LOC, cyclomatic complexity, and "clean architecture" are proxies. Reader load is the thing that matters. The two axes are independent. A flat file with 50 globals can be as hard to reason about as a 6-layer adapter stack. Guard both. This is the human analog of Guard the Context Window. Working memory is finite for readers too.
- Collapse layers that cost more than they save: wrappers with one caller, adapters with no second implementation, speculative indirection that was never needed. Inline them.
- Make adjacent layers change the abstraction. A layer that repeats the same methods and arguments adds reader load without compression. Collapse pass-through layers.
- Demand interface compression. A broad interface that hides little complexity makes readers learn both the surface and the implementation. Prefer boundaries that hide meaningful decisions.
- Shrink state scope: prefer pure functions (returns over mutations), locals over fields, fields over module state, and module state over globals. Derive instead of sync.
- Name the invariant at the boundary, not in every consumer, so the reader learns it once.
- Before adding a layer or a piece of state, ask: does it reduce reader load elsewhere by at least as much?
The test: can a new reader answer "where does X come from?" and "what can change X?" in under 30 seconds? If not, cut layers or cut state.
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.
- 9d ago Changed · -5 lines 1ea2b7fef239
- 1mo ago Changed 42551c5dc74c
- 1mo ago First seen · 24 lines · 44 tokens per session scan A 480f4ddec29e
principle-minimize-reader-load is a skill published in the GitHub repository backnotprop/pstack (2,498 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 416 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to principle-minimize-reader-load, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
argumentation
Construct well-structured arguments using the hypothesis-argument-example triad. Covers formulating falsifiable hypotheses, building logical arguments (deductive, inductive, analogical, evidential), providing concrete examples, and steelmanning counterarguments. Use when writing or reviewing PR descriptions that…
cross-review-project
Conduct a structured cross-project code review between two Claude Code instances via the cross-review-mcp broker. Each agent reads its own codebase, reviews the peer's code, and engages in evidence-backed dialogue — with QSG scaling laws enforcing review quality through minimum bandwidth constraints and phase-gated…
create-pull-request
Create and manage pull requests using GitHub CLI. Covers branch preparation, writing PR titles and descriptions, creating PRs, handling review feedback, and merge/cleanup workflows. Use when proposing changes from a feature or fix branch for review, merging completed work into the main branch, requesting code review…
audit-pr
Audit a whole PR against the delivery contract and return MERGE-READY or evidenced blockers with the full URL. Consumes the current review-change REVIEW-PASS receipt instead of re-running review axes; posts a SHA-bound ready comment; never edits or merges. Triggers: "audit-pr", "is this PR ready", "merge gate".
fold-findings
Repair persisted fix-now findings in compatible atomic batches: root-cause fixes, green gate, commit/push, and per-row folded: yes updates. Never reclassify or substitute backlog notes. Triggers: "fold-findings", "fix the review findings", "repair audit blockers".
review-change
Review a change with only applicable internal axes, classify every finding, persist fix-now work, and return one evidence-backed decision. Findings only; --adversarial N uses isolated reviewers; --synthesize fuses supplied reviewer tables. Triggers: "review-change", "review this change", "adversarial review".