Add SafeSkill security badge (89/100 — Passes with Notes)#1
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RandomCoder-lab merged 1 commit intoMay 19, 2026
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Signed-off-by: SafeSkill Scanner <mk@oya.ai>
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v78 self-eval was binary + single-EMA + reactive only. v79 adds three layers of refined self-awareness: #1 Continuous insight scale [0, ~2]: insight = surprise_factor * real_word_factor * (1 - rep_factor) - surprise_factor: surprise / pi*log(phi), capped at 2 - real_word_factor: 1.0 if word, 0.3 if char - rep_factor: 1.0 if token in last F(7)=13 emissions, 0 if novel Replaces binary 0/1. #2 Two-tier momentum (tactical + strategic): momentum_short: 1/F(3)=0.5 weight EMA -- responds in 2 steps momentum_long: 1/F(7)=0.077 weight EMA -- responds in 13 steps Decisions split: short drives sharpen/flatten (per-token tactic), long drives reserve scaling (strategic frame). #3 Entropy override ("am I stuck?" signal): Local entropy of last F(5)=5 emissions. If H < log(2) ~ 0.69 -> force flatten regardless of momentum. The model detects its own repetition through entropy, not just momentum magnitude. Three layers of self-awareness: emission quality (continuous insight), temporal pattern (short + long momentum), and structural diversity (entropy override). All pure substrate (F-tier EMAs, log thresholds).
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