am-command-ponytail-gain
Skill27 starsChanged 2 months agoMITView on GitHub
Show ponytail's measured impact scoreboard (less code, cost, time)
In this repository167 other files it gives its agents.
- AGENTS.mdAGENTS.md
- src/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- add-agent-targetSkill
- add-global-mode-targetSkill
- am-agent-code-debuggerSkill
- am-agent-code-documenterSkill
- am-agent-code-refactorSkill
- am-agent-code-reviewerSkill
- am-agent-code-security-auditorSkill
- am-agent-code-standards-enforcerSkill
- am-agent-security-auditorSkill
- am-agent-test-engineerSkill
- am-agent-test-runnerSkill
- am-agent-typescript-developerSkill
- am-command-buildSkill
- am-command-code-simplifySkill
- am-command-commitSkill
- am-command-planningSkill
- am-command-planSkill
- am-command-ponytail-auditSkill
- am-command-ponytail-debtSkill
- am-command-ponytail-helpSkill
- am-command-ponytail-reviewSkill
- am-command-ponytailSkill
- am-command-reviewSkill
- am-command-shipSkill
- am-command-specSkill
- am-command-testSkill
- api-and-interface-designSkill
- browser-testing-with-devtoolsSkill
- changelog-generatorSkill
- ci-cd-and-automationSkill
- code-review-and-qualitySkill
- code-reviewerSkill
- code-simplificationSkill
- context-engineeringSkill
- debugging-and-error-recoverySkill
- deprecation-and-migrationSkill
- docs-and-examplesSkill
- documentation-and-adrsSkill
- doubt-driven-developmentSkill
- frontend-ui-engineeringSkill
- git-workflow-and-versioningSkill
- grillingSkill
- grill-meSkill
- handoffSkill
- homebrew-packagingSkill
- idea-refineSkill
- incremental-implementationSkill
- interview-meSkill
- lessonsSkill
- library-testingSkill
- package-release-engineeringSkill
- performance-optimizationSkill
- planning-and-task-breakdownSkill
- ponytail-auditSkill
- ponytail-debtSkill
- ponytail-gainSkill
- ponytail-helpSkill
- The first 60 shown.
The file
Files
Show the ponytail gain scoreboard. One shot, change nothing: do not switch mode, write flag files, or persist anything. Render the published benchmark medians (5 everyday tasks; models Haiku, Sonnet, Opus; source benchmarks/ and the README) as plain ASCII bars: Lines of code, no-skill 100% vs ponytail 6-20% (down 80-94%); Cost, no-skill 100% vs ponytail 23-53% (down 47-77%); Speed, ponytail 3-6x faster. The bar length shows the measured range, the label carries the exact figure. These are benchmark medians, not this repo. NEVER print a per-repo savings number: the unbuilt version was never written, so there is no real baseline to subtract from in a live repo. For real per-repo figures, point to /ponytail-debt (the counted shortcut ledger) and /ponytail-audit (what is still cuttable). Report only.
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.