agentleFS
gleicongleicon/devskills/skills/ds-zoom-out/SKILL.md

ds-zoom-out

Skill20 starsChanged 46 days agoMITView on GitHub

Step up a layer of abstraction and map how the current code fits the bigger picture.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    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:

    When invoked, the user is unfamiliar with this area of code and wants the broader context — not a line-by-line read.

    line 7
  2. Distinctive:

    Use the project's existing naming and vocabulary.

    line 16

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

When invoked, the user is unfamiliar with this area of code and wants the broader context — not a line-by-line read. This maps and explains; it renders no judgment. To turn the map into a critique and a sequenced refactoring roadmap, follow with /ds-architecture-plan.

Process

  • Identify the module or area in focus.
  • Locate before reading: search for the callers and neighbours rather than reading files wholesale. You need boundaries and relationships, not line-by-line internals.
  • Go up one layer: describe the role it plays in the system, not its internals.
  • Map the relevant neighbouring modules and the callers that depend on this one.
  • Name the data and control flow that crosses its boundary.
  • Use the project's existing naming and vocabulary.

Output

A concise map: the area's responsibility, its neighbours, its callers, and the boundaries between them. No code dumps.

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.