lean-gain
Skill8 starsChanged 3 months agoApache-2.0View on GitHub
Show Lean's measured impact as a compact scoreboard: less code, more speed, from the benchmark medians. One-shot display, not a persistent mode, and not a per-repo number. Trigger: /lean-gain, "lean gain", "what does lean save", "show lean impact", "lean scoreboard".
1 other project uses a version of this.
Adopted by
Most recently taken up by
- FareedKhan-dev/lean-agenttheir own version, first seen Oct 13, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- FareedKhan-dev/lean-agent★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository8 other files it gives its agents.
- .cursor/rules/ponytail.mdcCursor rule
- compressSkill
- lean-auditSkill
- lean-commitSkill
- lean-debtSkill
- lean-helpSkill
- lean-reviewSkill
- leanSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
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
- Very specific: line 45
No cost bar is shown by design, not omission: on single-shot tasks Lean's cost is roughly flat (~100–108% of baseline in latest.json) …
≈ “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.
Files
Lean Gain
Display this scoreboard when invoked. One-shot: do NOT change mode, write flag files, or persist anything.
The figures are Prompt-Studio's own benchmark medians, measured — not computed
from the current repo. Source: benchmarks/results/latest.json and
benchmarks/results/2026-08-12-lean-vs-ponytail.md.
Scoreboard
Render plain ASCII bars. The bar length shows the measured range; the label carries the exact figure:
lean gain measured · gpt-4o-mini · n=3 · 7 tasks
Lines of code baseline ████████████████████ 100%
lean ██████·············· 25–35% ▼ 65–75%
Speed lean ▸ ~4× faster (3.5–4.3×, full = 4.25×)
Cost not shown by design — see Honesty boundary.
This repo: /prompt-studio:lean-stats (real tokens, this session)
/prompt-studio:lean-debt (counted shortcut ledger)
/prompt-studio:lean-audit (what's still cuttable)Honesty boundary
These are benchmark medians, not this repo. NEVER print a per-repo savings number ("you saved X lines/tokens here"): the unbuilt version was never written, so there is no real baseline to subtract from in a live repo.
No cost bar is shown by design, not omission: on single-shot tasks Lean's
cost is roughly flat (~100–108% of baseline in latest.json) because the Lean
system-prompt input tokens offset the output-token savings. Claiming a cost win
would contradict the measured data. For real per-session cost use
/prompt-studio:lean-stats (actual token counts) and /prompt-studio:lean-debt
(counted ledger); for what's still cuttable, /prompt-studio:lean-audit.
Basis is a single model (gpt-4o-mini, n=3) — the footer says so. Do not present it as multi-model.
Boundaries
One-shot display. Edits nothing, changes no mode. "stop lean" or "normal mode": revert.
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
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.