MCPInstall

principle-minimize-reader-load

Apply when reviewing or shaping code that's hard to trace. Count layers between question and answer, and hidden state in the reader's head; collapse one-caller wrappers and shrink mutable scope.

Installs
5.4K
Last 24h
+250
Trending rank
#1084
Security
Pass

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/backnotprop/pstack --skill principle-minimize-reader-load
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads principle-minimize-reader-load automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·The skill provides architectural and coding principles focused on reducing cognitive load for readers. It contains no executable code, remote dependencies, or suspicious instructions.
Socket PassNo alerts
Snyk PassLOW ·Risk: LOW · No issues

SKILL.md

The instructions your agent reads when the skill is triggered.

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.

FAQ

What does the principle-minimize-reader-load skill do?

Apply when reviewing or shaping code that's hard to trace. Count layers between question and answer, and hidden state in the reader's head; collapse one-caller wrappers and shrink mutable scope.

How do I install principle-minimize-reader-load?

Run npx skills add https://github.com/backnotprop/pstack --skill principle-minimize-reader-load in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/principle-minimize-reader-load/ (personal) or .claude/skills/principle-minimize-reader-load/ (project).

Which AI agents can use principle-minimize-reader-load?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is principle-minimize-reader-load safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains principle-minimize-reader-load?

It is published by backnotprop in backnotprop/pstack. It has 5,433 recorded installs.

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