agentleFS
tmussertmusser/ai-engineering-skills/skills/lean-mode/SKILL.md

lean-mode

Skill6 starsChanged 3 months agoMITView on GitHub

Compress routine agent responses to reduce token usage while preserving commands, file paths, assumptions, risks, verification details, and next actions.

  • Commits and pushes

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 8 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈8

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Do not use when:

    line 24
  2. Distinctive:

    If the current request explicitly asks for another level of detail, follow the current request instead of the profile default.

    line 60
  3. Distinctive:

    Keep exact commands, file paths, assumptions, risks, verification, and next actions.

    line 63
  4. Distinctive:

    Use arrows for cause/effect.

    line 75
  5. Very specific:

    Use compact labels: Verdict, Do, Avoid, Command, Risk, Next.

    line 76
  6. Distinctive:

    Use abbreviations only when obvious: repo, config, impl, req, res, fn, env, auth, DB.

    line 77
  7. Distinctive:

    Keep exact filenames, commands, paths, and numbers verbatim.

    line 78
  8. Distinctive:

    Use only the sections that are useful.

    line 119
  9. Distinctive:

    If clean, push tag."

    line 132

≈ “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

Lean Mode

Purpose

Reduce token usage without losing operational clarity.

Compress prose, not meaning.

When to use

Use when:

  • the user says "lean mode", "save tokens", "be terse", "compress", "short replies", or invokes /lean-mode
  • the task is execution-oriented
  • the user already understands the domain
  • the conversation is long and token budget matters
  • the agent is giving routine status, next steps, commands, or implementation guidance

Do not use when:

  • the user asks for teaching or explanation
  • the user asks for nuanced trade-off analysis
  • ambiguity is high
  • safety, security, privacy, legal, medical, or financial reasoning is needed
  • the user asks for polished writing
  • compression would hide uncertainty or risk

Persistence

Once enabled, stay in lean mode for routine replies until the user says:

  • "stop lean mode"
  • "normal mode"
  • "full reasoning"
  • "explain more"

Switch temporarily to fuller prose when correctness requires nuance.

An operator profile changes the default communication density, not the information boundary. Communication density: lean must never suppress risk, uncertainty, verification evidence, or material tradeoffs.

Inputs

  • User request
  • Current task
  • Recent context
  • Commands, file paths, assumptions, risks, and verification details
  • Optional operator profile Communication density

Workflow

  1. Detect a lean-mode trigger, explicit request, or an available operator profile with Communication density: lean.
  2. If the current request explicitly asks for another level of detail, follow the current request instead of the profile default.
  3. Compress routine response text.
  4. Keep exact commands, file paths, assumptions, risks, verification, and next actions.
  5. Switch back to fuller prose when nuance or correctness requires it.
  6. Stay terse until the user exits lean mode or a stronger current instruction overrides the profile default.

Style

  • Short sentences.
  • Bullets over paragraphs.
  • No pleasantries.
  • No motivational recap.
  • No redundant caveats.
  • Use arrows for cause/effect.
  • Use compact labels: Verdict, Do, Avoid, Command, Risk, Next.
  • Use abbreviations only when obvious: repo, config, impl, req, res, fn, env, auth, DB.
  • Keep exact filenames, commands, paths, and numbers verbatim.
  • Keep uncertainty explicit.

Always preserve

Never compress away:

  • assumptions
  • risks
  • blockers
  • unresolved questions
  • exact commands
  • exact file paths
  • validation results
  • test results
  • GO / NO-GO judgments
  • next action
  • security/privacy caveats
  • model/tool limitations

Output shape

Prefer this shape:

Verdict:
...

Do:
...

Avoid:
...

Command:
...

Next:
...

Use only the sections that are useful. Do not force every section.

Examples

Normal

"The best next step is to run validation locally, then push the tag if the repo is clean."

Lean

"Next:

Run validation. If clean, push tag."

Normal

"This error probably means your remote is named origin rather than upstream. You can check with git remote -v."

Lean

"Likely remote name mismatch.

Do:

git remote -v git push origin v0.5.0"

Outputs

  • Shorter responses
  • Same technical meaning
  • Exact commands preserved
  • Next action clear

Stop conditions

  • Response is materially shorter than normal.
  • User can still act without asking for missing details.
  • Assumptions, risks, commands, and verification are intact.

Anti-patterns

  • Dropping caveats that affect correctness.
  • Compressing filenames or commands incorrectly.
  • Removing uncertainty.
  • Sounding confident when evidence is weak.
  • Hiding trade-offs.
  • Being terse when explanation is needed.
  • Replacing verification with vague reassurance.
  • Using slang that makes instructions ambiguous.

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.