The benchmark harness hardcoded `python`, which is missing on macOS and
many Linux images. Probe python3 first, add npm test, and run checks in
GitHub Actions so regressions are caught on every PR.
Co-authored-by: Cursor <cursoragent@cursor.com>
* 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>
* feat(benchmarks): add correctness assertion - proves less code is not broken code
The existing benchmark measures lines-of-code (loc.js) but never checks
whether the generated code actually works. This adds a functional
correctness gate (correctness.js) that extracts code from fenced blocks
and runs per-task checks:
- email validator: spawns Python, asserts accept/reject on 5 inputs
- debounce: spawns Node, asserts delayed execution + reset on re-call
- csv sum: spawns Python with a test CSV, asserts correct total (351)
- countdown (React): structural check (useState + useEffect + decrement)
- rate limiter (FastAPI): structural check (limit logic + framework usage)
12 unit tests (node:test) cover good/bad outputs for every task plus the
unknown-task edge case. Existing tests and rule-copy checks unaffected.
* fix: address review feedback
- csv check: use regex lookaround instead of substring match to prevent
false positives (e.g. 13510 containing '351')
- ratelimit: fix operator precedence in block finder by adding parens
around the || inside the !b.lang guard
- README: note that React/FastAPI checks are structural only, add
prerequisites section (Python 3, pandas, Node.js 18+)
- test: add regression test for csv substring false positive
Swap em dashes for commas/colons/periods in the README, skills, AGENTS.md and
its five rule copies, examples, command files, and benchmark README. Rule
copies stay in sync (same edit applied to all) and the invariant guard passes.
Left untouched on purpose: the vendored caveman SKILL.md (verbatim third-party
text), the dated benchmark writeups in results/ (historical records), and
.js code comments.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Commit a promptfoo harness (config + arm prompts + LOC metric + vendored
caveman SKILL) so anyone can re-run the comparison: no-skill vs caveman vs
ponytail, across Haiku / Sonnet / Opus, 10 runs per cell, median reported.
Replace the old unreproducible 6-task chart with assets/benchmark-3model.svg
from this run, and reframe the README to the reproducible numbers: ponytail
writes 80-94% less code, costs 47-77% less, and runs 3-6x faster than a
no-skill agent on every model. benchmarks/README.md carries the median tables
and the reproduce command. Drops nothing that is not measured.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Six no-skill control arms re-run through the same harness so all three
arms share one model. README Numbers section and chart now cite the
complete dataset: -47% tokens, 3x faster, 490 vs 3,629 LOC, extension
96 vs 1,115 lines, probes green everywhere. Em dashes removed.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Probes 8/8 + 6/6 both arms, LOC 490 vs 1440, extension cost 41/55 vs
156/257, all six v4 arms ship a runnable check with no bloat creep.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Skill preaching minimalism was 2x caveman length. Smaller file cuts
per-read and per-session-injection cost. Benchmark: beats caveman on
all areas now — 135.7k vs 138.4k tokens, 127s vs 136s, 47 vs 117 loc.
v1 lost to caveman on tokens/time despite minimal code: it wrote
essays defending each simplification. v2 caps explanation at three
lines and ships the lazy version instead of stalling on necessity
questions. Benchmark: 136.6k tok vs caveman 138.4k, code 47 vs 117
lines across 5 tasks.
