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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.
epicsagas/epic-harness · ★ 18 · AI & Automation · score 76
Install: claude install-skill epicsagas/epic-harness
# Critic — Adversarial Proposal Review > **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). ## When to Trigger - During `/evolve` review of newly seeded skills - When `reward_hacking_suspected` is true in metrics - After a seesaw-regression round, before re-proposing ## Process ### 1. Gather the proposal + evidence - Read the evolved skill proposal(s) from this round - Read the EditManifest (edit_type, target, intended_effect, predicted_impact) - Read the round's TaskDigests (outcome, failure_categories, evidence_excerpts) - Read recent score_history dimension_averages (tool_success, output_quality, execution_cost) ### 2. Falsify the manifest (paper §4.3, Table 9) For each proposal, ask: does the trace evidence support the `predicted_impact`? - If the manifest claims "Lift avg_score_with" but output_quality is regressing → **Reject** - If the manifest claims a tool fix but the implicated component shows no change → **Warn** - If the evidence corroborates the predicted effect → **Approve** ### 3. Reward-hacking cross-check - Is execution_cost rising while output_quality falls across the window? - Could the s