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Get Started Free →HarnessX Critic (Tier 2.1) — adversarial review of evolved-skill proposals against trace evidence. Detects reward hacking and manifest/evidence contradictions. Out-of-band LLM counterpart to the in-loop deterministic critic in src/evolve/critic.rs.
.claude/skills/hashgraph-online-critic/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 6% | 0% |
> In-loop vs out-of-band. epic-harness forbids external LLM calls from > production, so the reflect loop ships a deterministic critic > (src/evolve/critic.rs) that gates seeding when reward hacking is > suspected. THIS skill is the out-of-band LLM version a meta-agent or > human runs during /evolve review for the cases the deterministic check > cannot catch (non-local effects, manifest/evidence nuance).
/evolve review of newly seeded skillsreward_hacking_suspected is true in metricsFor each proposal, ask: does the trace evidence support the predicted_impact?
| Excuse | Rebuttal | Do instead | |--------|----------|------------| | "The score went up, so it works" | Score can rise via metric gaming | Verify the outcome improved, not just the score | | "The seesaw passed, it's safe" | Seesaw is coarse; sub-threshold coupling evades it | Check dimension deltas, not just aggregate pass | | "It's just a prompt tweak" | Prompt edits have non-local effects on shared context | Trace the effect across skills, not just the target |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 12,790 | 16,047 | +25% | 1 | 1 | 0% | 924 | 2,515 | +172% | 0 | 0 | — |
case-02 | fail→fail | 21,067 | 23,133 | +10% | 1 | 1 | 0% | 2,728 | 3,231 | +18% | 0 | 0 | — |
case-03 | fail→fail | 14,004 | 15,062 | +8% | 1 | 1 | 0% | 1,461 | 2,317 | +59% | 0 | 0 | — |
case-04 | fail→pass | 25,137 | 22,408 | -11% | 1 | 1 | 0% | 3,125 | 4,650 | +49% | 0 | 0 | — |
case-05 | fail→pass | 25,323 | 12,227 | -52% | 1 | 1 | 0% | 2,831 | 1,719 | -39% | 0 | 0 | — |
case-06 | pass→pass | 17,515 | 15,377 | -12% | 1 | 1 | 0% | 2,426 | 2,245 | -7% | 0 | 0 | — |
case-07 | pass→pass | 10,510 | 13,495 | +28% | 1 | 1 | 0% | 1,480 | 1,951 | +32% | 0 | 0 | — |
case-08 | fail→fail | 10,396 | 13,070 | +26% | 1 | 1 | 0% | 753 | 1,911 | +154% | 0 | 0 | — |
case-09 | pass→pass | 9,527 | 10,538 | +11% | 1 | 1 | 0% | 681 | 1,713 | +152% | 0 | 0 | — |
case-10 | fail→pass | 21,450 | 10,238 | -52% | 1 | 1 | 0% | 1,385 | 1,717 | +24% | 0 | 0 | — |
case-11 | pass→pass | 18,591 | 13,713 | -26% | 1 | 1 | 0% | 2,000 | 1,933 | -3% | 0 | 0 | — |
case-12 | pass→pass | 19,427 | 12,547 | -35% | 1 | 1 | 0% | 1,903 | 2,083 | +9% | 0 | 0 | — |
case-13 | pass→pass | 15,644 | 5,057 | -68% | 1 | 1 | 0% | 1,618 | 1,605 | -1% | 0 | 0 | — |
case-14 | pass→pass | 12,328 | 10,930 | -11% | 1 | 1 | 0% | 1,180 | 1,517 | +29% | 0 | 0 | — |
case-15 | pass→pass | 11,161 | 6,034 | -46% | 1 | 1 | 0% | 975 | 1,692 | +74% | 0 | 0 | — |
case-16 | pass→pass | 8,402 | 10,413 | +24% | 1 | 1 | 0% | 1,242 | 1,632 | +31% | 0 | 0 | — |
case-17 | fail→pass | 7,092 | 3,166 | -55% | 1 | 1 | 0% | 1,129 | 1,233 | +9% | 0 | 0 | — |
case-18 | fail→pass | 17,259 | 6,955 | -60% | 1 | 1 | 0% | 1,874 | 1,978 | +6% | 0 | 0 | — |
case-19 | fail→pass | 10,299 | 14,041 | +36% | 1 | 1 | 0% | 1,495 | 2,378 | +59% | 0 | 0 | — |
case-20 | pass→pass | 5,143 | 6,215 | +21% | 1 | 1 | 0% | 703 | 1,764 | +151% | 0 | 0 | — |
case-21 | fail→pass | 9,669 | 3,247 | -66% | 1 | 1 | 0% | 1,320 | 1,214 | -8% | 0 | 0 | — |
case-22 | fail→pass | 15,824 | 5,930 | -63% | 1 | 1 | 0% | 1,752 | 1,778 | +1% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.