agentleFS
Sign inStart free
pedroiff0pedroiff0/awesome-skills/skills/autonomous-ai-agents/context-mode/SKILL.md

context-mode

Skill1 starsChanged 46 days agoMITView on GitHub

Context optimization and compression routing rules for AI agents exploring large codebases, reading massive logs, searching symbols, and batching tool executions.

7 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

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

  • 0

    Common

    Most of the 6 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Targeted Line Range Views: Avoid dumping entire files.

    line 26
  2. Very specific:

    Grep Before View: Use ripgrep (grep_search) to locate exact function definitions or usages before loading the containing file.

    line 27

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

Context Mode & Token Optimization

This skill provides operational patterns for navigating massive repositories while conserving context tokens using indexed searches, AST filters, and structured outputs.

When to Use

  • Reading large log files (>500 lines) or analyzing multi-megabyte code repositories.
  • Performing symbol lookups across hundreds of files without flooding context.
  • Running high-volume batch executions or lint sweeps.

Operational Guidelines

  1. Targeted Line Range Views: Avoid dumping entire files; view slices using line offsets (view_file with StartLine and EndLine).
  2. Grep Before View: Use ripgrep (grep_search) to locate exact function definitions or usages before loading the containing file.
  3. Subagent Offloading: For deep exploratory research, invoke an isolated research subagent (invoke_subagent) to read docs and summarize findings back to parent context.

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