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SlipItIn/.github/skills/slop-eval/scripts/score.py
Tim Krampitz 01046b01e4 Neue Skills, Referenzen & OpenWiki-Doku integriert
Umfangreiche Erweiterung der Skill-Bibliothek: Neue Skills für Humanisierung (Englisch/PT-BR), Design-Validierung, AI-SEO und Coolify-Deployment inkl. Regelwerke, Presets, Pattern-Referenzen, Testfälle und Automatisierungsskripte. Zusätzliche Skills für Revenue-Centric Design, Pier Cloud, OKF, Lebenslauf- und LinkedIn-Optimierung sowie zahlreiche Referenzdateien, Checklisten und YAML/JSON/Markdown-Templates. Einführung einer vollständigen OpenWiki-Dokumentation mit Architektur-, Domain- und Workflow-Beschreibungen, zentralem Index und automatisierten Updates. Modularer Aufbau, restriktive Lizenzen und umfassende Qualitäts- und Evaluationsmechanismen für alle neuen Inhalte.
2026-07-26 14:00:58 +02:00

118 lines
3.7 KiB
Python

#!/usr/bin/env python3
"""Deterministic scoring for a slop-eval report.
Usage:
score.py axis CRIT MAJOR MINOR [--cap N]
Axis score from confirmed tell counts:
max(0, 100 - 30*CRIT - 15*MAJOR - 5*MINOR), then min(score, cap).
Use --cap 40 for the Layout compounding rule (>=3 major layout tells).
score.py overall [--cap N ...] [--fail-below N] 1:80:2 2:65:2 ... 7:NA:3
One arg per axis, formatted axis:score:weight. Score NA (or N/A)
excludes the axis from both sums. Caps apply to the weighted
overall (signature gate: --cap 59; absolute-rule gate: --cap 69).
Prints overall, Slop Index (100 - overall), and grade.
--fail-below N exits non-zero when overall < N (CI gate).
"""
import sys
PENALTY = {"crit": 30, "major": 15, "minor": 5}
def grade(score: float) -> str:
if score >= 80:
return "A (Premium)"
if score >= 60:
return "B (Considered)"
if score >= 40:
return "C (Generic)"
if score >= 20:
return "D (Slop)"
return "F (Pure slop)"
def pop_flag(args: list, flag: str, repeat: bool = False):
vals = []
while flag in args:
i = args.index(flag)
try:
vals.append(float(args[i + 1]))
except (IndexError, ValueError):
sys.exit(f"{flag} requires a numeric value")
del args[i : i + 2]
if not repeat:
break
return vals
def cmd_axis(args: list) -> None:
caps = pop_flag(args, "--cap", repeat=True)
if len(args) != 3:
sys.exit("axis mode needs exactly: CRIT MAJOR MINOR counts")
try:
crit, major, minor = (int(a) for a in args)
except ValueError:
sys.exit("tell counts must be integers")
if min(crit, major, minor) < 0:
sys.exit("tell counts must be >= 0")
score = max(
0,
100 - PENALTY["crit"] * crit - PENALTY["major"] * major - PENALTY["minor"] * minor,
)
capped = min([score] + caps)
detail = f" (capped from {score:g})" if capped < score else ""
print(
f"tells: {crit} crit / {major} major / {minor} minor"
f" -> axis score = {capped:g}{detail}"
)
def cmd_overall(args: list) -> None:
caps = pop_flag(args, "--cap", repeat=True)
fail_below = pop_flag(args, "--fail-below")
if not args:
sys.exit(__doc__)
num = den = 0.0
na = []
for arg in args:
try:
axis, score, weight = arg.split(":")
except ValueError:
sys.exit(f"bad arg {arg!r}: expected axis:score:weight")
if score.strip().upper() in ("NA", "N/A"):
na.append(axis)
continue
s, w = float(score), float(weight)
if not 0 <= s <= 100:
sys.exit(f"axis {axis}: score {s} outside 0-100")
num += s * w
den += w
if den == 0:
sys.exit("no applicable axes")
raw = num / den
overall = min([raw] + caps)
capped = f" (capped from {raw:.2f})" if overall < raw else ""
print(f"applicable axes: {len(args) - len(na)} | NA: {', '.join(na) or 'none'}")
print(f"sum(score x weight) = {num:g} | sum(weight) = {den:g}")
print(f"overall = {overall:.2f}{capped}")
print(f"Slop Index = {100 - overall:.2f}")
print(f"grade: {grade(overall)}")
if fail_below and overall < fail_below[0]:
sys.exit(f"FAIL: overall {overall:.2f} below threshold {fail_below[0]:g}")
def main() -> None:
if len(sys.argv) < 2 or sys.argv[1] in ("-h", "--help"):
sys.exit(__doc__)
mode, args = sys.argv[1], sys.argv[2:]
if mode == "axis":
cmd_axis(args)
elif mode == "overall":
cmd_overall(args)
else:
sys.exit(f"unknown mode {mode!r}; use 'axis' or 'overall'\n\n{__doc__}")
if __name__ == "__main__":
main()