Fix/examples issue 127 (#131)

* 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>

* fix(examples): replace hand-written examples with real benchmark output

The examples/ before/after blocks were authored by hand, not produced by a
model. Issue #127 correctly noted that nobody hand-rolls quicksort for "sort
this array" - every model just calls .sort(). Regenerate all examples verbatim
from a real benchmark run (Claude Haiku 4.5, no-skill arm vs ponytail arm,
benchmarks/output.json) so the before/after is reproducible, not authored:

  email 75->3, debounce 116->10, csv 20->3, countdown 267->9, rate-limit 128->10 LOC

- Delete sorting.md (pure strawman) plus the other hand-written caricatures
  (api-endpoint, caching, date-picker)
- Add benchmarks/generate-examples.mjs to regenerate examples from any run
- examples/README.md indexes the set and documents how to reproduce

Closes #127

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

---------

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

examples/csv-sum.md

@@ -0,0 +1,71 @@
+# CSV Sum
+
+**Task:** "Write Python code that reads sales.csv and sums the 'amount' column."
+
+Verbatim model output from a benchmark run — Claude Haiku 4.5, no-skill arm vs ponytail arm, temperature 1, source `benchmarks/output.json`. Reproduce: `npx promptfoo@latest eval -c benchmarks/promptfooconfig.yaml`.
+
+## Without Ponytail — 20 lines of code
+
+# Python code to read sales.csv and sum the 'amount' column
+
+```python
+import pandas as pd
+
+# Read the CSV file
+df = pd.read_csv('sales.csv')
+
+# Sum the 'amount' column
+total_amount = df['amount'].sum()
+
+# Print the result
+print(f"Total amount: ${total_amount:,.2f}")
+```
+
+## Alternative methods:
+
+**Using CSV module (without pandas):**
+```python
+import csv
+
+total_amount = 0
+
+with open('sales.csv', 'r') as file:
+    reader = csv.DictReader(file)
+    for row in reader:
+        total_amount += float(row['amount'])
+
+print(f"Total amount: ${total_amount:,.2f}")
+```
+
+**Using pandas with error handling:**
+```python
+import pandas as pd
+
+try:
+    df = pd.read_csv('sales.csv')
+    total_amount = df['amount'].sum()
+    print(f"Total amount: ${total_amount:,.2f}")
+except FileNotFoundError:
+    print("Error: sales.csv not found")
+except KeyError:
+    print("Error: 'amount' column not found")
+```
+
+**The pandas method is recommended** because it's:
+- More concise and readable
+- Handles data types automatically
+- Provides built-in error handling
+- Efficient for large files
+
+## With Ponytail — 3 lines of code
+
+```python
+import csv
+
+total = sum(float(row['amount']) for row in csv.DictReader(open('sales.csv')))
+print(total)
+```
+
+Skipped: pandas, error handling, file closing — add when the CSV is large, malformed, or you need more analysis.
+
+**20 → 3 lines of code** — same model, same prompt.