agentleFS
Sign inStart free
zedarvateszedarvates/frugal-skills/skills/cache/SKILL.md

cache

Skill1 starsChanged 30 days agoMITView on GitHub

Instructions for upstream project-scan caching with get_or_scan, an audit-report slot and a documented 24-hour TTL. Use when sequential agents may reuse a compatible scan; validate freshness and analysis inputs. Not for model-answer caching, agent-run caching or per-file fingerprint gates.

2 other projects use a version of this, 1 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    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:

    Trigger: When multiple agents work on the same project sequentially.

    line 25

≈ “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

Project Cache

Package scope and prerequisites

This catalog ships instructions, not the executable runtime. Python modules, scripts, local services and optional tools named below must be installed separately from the upstream project. Check their availability before running commands; if they are missing, explain the prerequisite instead of claiming execution or results. The host agent may use model tokens while following these instructions.

Commands, APIs and runtime behavior described below are conditional examples for a compatible upstream installation; they are not evidence of execution in this package.

When NOT to use

  • The cached object is a model answer rather than a project scan — use response_cache.
  • The cached object is an agent run — use agent-cache.
  • You only want unchanged code skipped inside one audit — use code_fingerprint. Avoids re-scanning between agents. First agent scans, subsequent agents read cache.

Trigger: When multiple agents work on the same project sequentially.

Usage:

from skills.cache import ProjectCache
cache = ProjectCache(project_root)
scan = cache.get_or_scan(lambda: scanner.scan())
cache.set_audit_report(report)

Module: skills/cache Storage: .botte-cache/ directory TTL: 24 hours

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.

Check your setup, free