Once Ponytail started trending, people naturally went looking for alternatives. The one that comes up most often is Caveman, a much smaller project by developer JuliusBrussee with a tagline that tells you exactly what it does: "why use many token when few token do trick." It rewrites the agent's own internal reasoning into short, blunt, caveman-style prose to save tokens.
Both tools are trying to solve the same complaint — AI agents write too much, think too loudly, and cost too much doing it. They just disagree on the fix.
Two different theories of the problem
Caveman's theory is that a lot of wasted tokens come from the model's own verbose reasoning and commentary, so if you strip that down to terse, caveman-style sentences, you save cost without touching the actual code output much.
Ponytail's theory is different. It argues the real waste isn't in how the model talks to itself, it's in what the model decides to build in the first place — the unnecessary library, the extra wrapper, the feature nobody asked for. Fix the decision-making step with a strict ladder of questions, and the token savings follow as a side effect, not the goal.
What the numbers actually show
The most recent agentic benchmark ran the same twelve-ticket, real-repo test used to evaluate Ponytail, comparing three arms against a plain baseline with no skill installed:
| Approach | Lines of code | Tokens | Cost | Time | Safety score |
|---|---|---|---|---|---|
| Ponytail | -54% | -22% | -20% | -27% | 100% |
| Caveman (terse-prose control) | -20% | +7% | +3% | +2% | 100% |
| "YAGNI + one-liners" prompt | -33% | -14% | -21% | -30% | 95% |
That table is worth sitting with for a second. In this particular agentic test, the terse-prose approach actually came out slightly more expensive on tokens, cost, and time than doing nothing at all, even though it did produce less code. A model spending its reasoning budget trying to sound terse isn't automatically a model doing less work. Ponytail's own maintainers are careful to point this out too: a model that spends thinking tokens deliberating over strict rungs of a ladder can, on some models, end up costing more, not less.
The one-liner prompt approach cut cost nicely but gave up something in return — its safety score dropped to 95%, meaning validation, error handling, or security checks were more likely to get skipped somewhere in the twelve tasks.
What to actually take from this if you're choosing one
Don't pick a token-saving skill off a headline percentage. Ask two questions instead: what is it actually changing, the model's decisions or its writing style, and does the safety score hold up once you check. A skill that saves 20% on tokens but quietly loosens validation on one task in twelve is not a good trade for anything running against a real codebase. If you want the deeper walkthrough of how Ponytail's rungs work in practice, we cover that in our Ponytail explainer.
