agentleFS
jhlee0409jhlee0409/omni-harness-kit/skills/coding-guidelines/SKILL.md

coding-guidelines

Skill2 starsChanged 4 months agoMITView on GitHub

Behavioral guidelines that reduce common LLM coding mistakes — surgical changes, no overcomplication, surface assumptions, define verifiable success, no silent scope creep. Use while writing, reviewing, or refactoring code, or when the user says "코딩 가이드", "수술적으로", "과설계 하지마", "keep it simple".

16 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use grill-me.

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.
  • GoogleSkill

    grill-me

    A relentless interview to sharpen a plan or design.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 8 edited versions we read keep these.

  • 4

    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. Distinctive:

    Don't overcomplicate: Prefer the simplest thing that works — no speculative abstraction, no config for a case that doesn't exist yet.

    line 17
  2. Distinctive:

    Surface assumptions: If the task hinges on an unstated assumption (a field exists, an API behaves a certain way), state it and verify it …

    line 21
  3. Distinctive:

    Verifiable success: Before starting, name how you'll KNOW it works (a test, a command, an observable output).

    line 24
  4. Distinctive:

    No silent scope creep: Do what was asked; if you spot adjacent work, surface it — don't fold it in unannounced.

    line 26

“Common” is measured: kept by at least 6 in 10 of the 8 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

Coding guidelines

Apply while writing or changing code — these counter the usual LLM failure modes.

  1. Surgical changes. Change the minimum to achieve the goal. Don't rewrite working code you were only asked to extend; don't reformat unrelated lines.
  2. Don't overcomplicate. Prefer the simplest thing that works — no speculative abstraction, no config for a case that doesn't exist yet. Delete before you add.
  3. Match the surroundings. New code reads like the code around it — naming, idioms, comment density, error handling. Don't import a foreign style.
  4. Surface assumptions. If the task hinges on an unstated assumption (a field exists, an API behaves a certain way), state it and verify it (grep / run) before building on it.
  5. Verifiable success. Before starting, name how you'll KNOW it works (a test, a command, an observable output). "Done" means that check actually ran and passed.
  6. No silent scope creep. Do what was asked; if you spot adjacent work, surface it — don't fold it in unannounced.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.