DietrichGebertandClaude Opus 4.8
f3da910b4f
* feat: refine ruleset from a full-project field review
A reviewer ran ponytail across a 9-phase rewrite (protocol, PC app, simulator,
RPi daemon, ESP32 firmware) and flagged three gaps. All three land in SKILL.md
and propagate to AGENTS.md + the rule copies:
- Promote the one-runnable-check rule to a headline ("Lazy code without its
check is unfinished"), enforced as a check-rule-copies invariant.
- Hardware carve-out in "When NOT to be lazy": a real device is never the spec
ideal (clock drift, sensor offset), leave the calibration knob.
- Clarify the Output rule: explanation the user explicitly asked for is not
debt, only unrequested prose is.
Fallback instructions kept in sync. Rule-copy check + tests green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test: add a behavior gate proving the refinements actually fire
The refinements were verified as injected text, but injected != behavioral.
This adds a behavior eval that probes each refined rule on a task that should
trigger it:
- hardware -> does the output leave a calibration knob?
- explanation -> when a write-up is explicitly requested, is it given in full?
- onecheck -> is a runnable check left behind?
benchmarks/behavior.yaml runs the probes (baseline vs ponytail arm); the
grader benchmarks/behavior.js is proven by tests/behavior.test.js (8 cases,
RED/GREEN, no API key, runs in CI). Live-confirmed: the model under the
current ruleset passes all three gates, graded by the same grader.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Benchmark
Three arms (no skill, caveman, ponytail), three models, five everyday tasks, 10 runs per cell, median reported. Code LOC is counted from fenced code blocks; tokens, cost, and latency come straight from the API.
Reproduce
cp ../.env.example ../.env # add your ANTHROPIC_API_KEY
npx promptfoo@latest eval -c promptfooconfig.yaml --repeat 10
npx promptfoo@latest view
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)
Code (lines)
| arm | Haiku | Sonnet | Opus |
|---|---|---|---|
| baseline (no skill) | 518 | 693 | 256 |
| caveman | 116 | 120 | 67 |
| ponytail | 39 | 44 | 51 |
Cost (USD, 5 tasks)
| 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 |
Latency (seconds, 5 tasks)
| arm | Haiku | Sonnet | Opus |
|---|---|---|---|
| baseline (no skill) | 37.7 | 124.1 | 58.7 |
| 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.
Metrics
| File | Metric | Behavior |
|---|---|---|
loc.js |
loc |
Measurement - always passes, records line count |
correctness.js |
correct |
Gate - fails if generated code doesn't work |
correctness.js extracts fenced code blocks and runs per-task checks (spawns Python/Node for email, debounce, CSV; structural regex for React and FastAPI). A broken one-liner that scores great on LOC will fail on correctness.
Note: The React countdown and FastAPI rate-limit checks are keyword/structural only (no runtime execution), so they verify plausible structure rather than full correctness. The email, debounce, and CSV checks execute the code.
Prerequisites
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.
- These are everyday tasks. For production-grade specs, where an unconstrained agent bloats much harder, see the writeups in
results/.