agentleFS
lengjingxulengjingxu/pstack-for-codex/skills/principle-minimize-reader-load/SKILL.md

principle-minimize-reader-load

Skill1 starsChanged 24 days agoMITView on GitHub

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.

46 other projects use a version of this, 44 of them edited.

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Projects with it also use ketchup-video, maintain-formal-verification-skill and 2 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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    Bring a project's formal verification back in line with the code after the code changes: find the commits that touched verified code, update the model, harness, or properties to describe the code as it is now, rerun, and say whether each failure is drift in the verification or a bug in the code. Use for /maintain-formal-verification-skill, \"is the model still true to the code\", or after merges and refactors that touched verified code without rechecking it.

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    Draw what an app's user sees as a Mermaid statechart from a TLA+ model. States are gray if they work, red if still wrong, and green if fixed. Each bug TLC finds is a numbered path. Use for /statechart, \"show the PM the bug\", or to turn an agreed chart into a TLA+ skeleton.

    Also in 15 projects that use this

  • JoshueOsunaSkill

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The file

We split this skill into the rules it gives an agent

3 rules. 3 are common, 0 are distinctive, and 0 are very specific to this project.

  • 3

    Common

    Most of the 46 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Common:

    Shrink state scope: prefer pure functions (returns over mutations), locals over fields, fields over module state, and module state over …

    line 13
  2. Common:

    Name the invariant at the boundary,: not in every consumer, so the reader learns it once.

    line 14
  3. Common:

    Before adding a layer or a piece of state, ask: does it reduce reader load elsewhere by at least as much?

    line 15

“Common” is measured: kept by at least 6 in 10 of the 46 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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.

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