docs: correct cost claim to 42-75% from 30-rep re-verification (#129)

* docs: correct cost claim to 42-75% from 30-rep re-verification

Re-ran the cost benchmark at 30 reps per cell on Claude (Haiku/Sonnet/Opus):
ponytail is 42-75% cheaper than no-skill, not the previously published 47-77%.
The direction holds, both ends came in a few points lower. Updates the README
headline and body, the benchmark chart subtitle, and the benchmarks/README cost
table, and adds a dated results doc with full method.

Also adds the OpenAI (gpt-4.1-mini/gpt-5.4-mini/gpt-5.5) and Gemini configs. On
OpenAI reasoning models ponytail costs more, not less, so the claim stays
Claude-scoped. Gemini run pending a fresh-quota day.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: scope the body claim to Claude models

"on every model" read as cross-provider, but the 30-rep verification shows
the cost win reverses on OpenAI reasoning models. Match the caption and
benchmarks/README, which already say Claude.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: reframe the pitch as the discipline, not token savings

The cost/code/latency numbers vary by model and on some (terse reasoning
models like GPT-5.5) ponytail costs more, so leading with them as a universal
win was misleading. Adds model-variance to the headline caption and a paragraph
making the stated point the mental model: write only what the task needs,
safety kept, maintainable code. Savings are a model-dependent side effect.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: name the ladder's reasoning cost

The ladder is a deliberation step: on reasoning models the agent spends
thinking tokens working through the rungs before it saves any output, which
together with the always-on ruleset can outweigh the shorter code. Makes the
GPT-5.5 cost increase legible rather than just stating it.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: state the single-shot limitation honestly

The benchmark is single-shot (one prompt, one completion); it does not measure
a real multi-turn agent session, where the ruleset re-injects and the ladder
deliberates every turn. Adds that caveat to the README, and corrects the
benchmarks/README note that claimed caching widens the gap "in ponytail's
favor" (unverified, and a measured agentic A/B in #121 found the opposite can
happen). Per-session cost can land either way.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* docs: fix run count in caption (cost is 30 runs, not 10)

Cost was re-verified at 30 reps; code and latency are still the original 10.
The headline caption said "10 runs" across the board, which undersold the cost
verification. Now states the split, matching benchmarks/README.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

benchmarks/README.md

@@ -33,7 +33,7 @@ models where the multi-step decision ladder isn't reliably followed.
 
 Tasks: email validator, JS debounce, CSV sum, React countdown, FastAPI rate-limit (see `promptfooconfig.yaml`). Single-shot completions, default temperature.
 
-## Median results (10 runs, 2026-06-13)
+## Median results (10 runs, 2026-06-13; cost re-verified at 30 runs, 2026-06-17)
 
 **Code (lines)**
 
@@ -43,13 +43,13 @@ Tasks: email validator, JS debounce, CSV sum, React countdown, FastAPI rate-limi
 | caveman | 116 | 120 | 67 |
 | **ponytail** | **39** | **44** | **51** |
 
-**Cost (USD, 5 tasks)**
+**Cost (USD, 5 tasks; 30 runs, 2026-06-17)**
 
 | arm | Haiku | Sonnet | Opus |
 |---|--:|--:|--:|
-| baseline (no skill) | 0.032 | 0.141 | 0.135 |
-| caveman | 0.014 | 0.045 | 0.075 |
-| **ponytail** | **0.010** | **0.032** | **0.071** |
+| baseline (no skill) | 0.030 | 0.137 | 0.137 |
+| caveman | 0.014 | 0.046 | 0.072 |
+| **ponytail** | **0.011** | **0.035** | **0.079** |
 
 **Latency (seconds, 5 tasks)**
 
@@ -59,7 +59,7 @@ Tasks: email validator, JS debounce, CSV sum, React countdown, FastAPI rate-limi
 | caveman | 14.9 | 34.7 | 23.1 |
 | **ponytail** | **9.9** | **20.1** | **18.0** |
 
-Versus baseline, ponytail writes **80-94% less code**, costs **47-77% less**, and runs **3-6x faster**, on every model.
+Versus baseline, ponytail writes **80-94% less code**, costs **42-75% less**, and runs **3-6x faster**, on every Claude model. Cost re-verified at 30 reps, with OpenAI and Gemini arms, in [results/2026-06-17-cost-verification.md](results/2026-06-17-cost-verification.md).
 
 ## Metrics
 
@@ -79,5 +79,5 @@ Running the benchmark requires **Python 3**, **pandas**, and **Node.js** (18+).
 ## Notes
 
 - Caveman is a prose-compression skill (it leaves code "normal"), so it lands between baseline and ponytail on code size and wins mainly on prose tokens.
-- Cost reflects single-shot calls that re-send the skill every time. In real sessions the skill is injected once and prompt-cached, so the cost gap widens further in ponytail's favor.
+- Cost reflects single-shot calls (one prompt, one completion), not real multi-turn agent sessions. In a session the ruleset re-injects and the ladder deliberates every turn across many turns, so per-session cost can come out higher or lower than these numbers. Prompt caching offsets some of the re-injection, but a measured agentic A/B ([#121](https://github.com/DietrichGebert/ponytail/issues/121)) found ponytail can also raise tool calls and cost on completion-forced tasks. Treat these as generation numbers, not a session-cost promise.
 - These are everyday tasks. For production-grade specs, where an unconstrained agent bloats much harder, see the writeups in `results/`.