adversarial-case-builder

Adversarial Case Builder — the strongest evidence-bound objection

Build the single strongest evidence-bound case to reject $ARGUMENTS, then defend it point-by-point. Emit adversarial-case-builder.memo.md. Run LAST, AFTER /evidence-ledger (so claims.json exists) and AFTER the auditor skills (so the merged *.findings.json exist).

🔒 Run once per input change; never wrap in /loop, /schedule or CronCreate (${CLAUDE_PLUGIN_ROOT}/support/references/run-cadence.md). Re-run only when the ledger, the auditors' findings or the paper change.

Adapted from ARIS kill-argument, with one deliberate downgrade: memo-only. In a forensics pipeline the headline-attack is most useful as a synthesis of already-anchored evidence, not a free-floating LLM critique — that free-floating mode is exactly the "LLM slop grading LLM slop" failure this repo exists to refuse. So here every attack point must cite an existing ledger claim_id or finding_id, and the skill never emits verdict-bearing findings: tools/adjudicate_findings.py lists adversarial-case-builder in MEMO_ONLY_SKILLS and caps anything from it at info. The deterministic adjudicator owns the verdict; this skill owns the memo.

Read reference/rationale.md for why this skill exists (the committed single-paragraph rejection a balanced auditor list never writes, fenced to anchored evidence).

Core principle

MEMO-ONLY · evidence-bound · two fresh cross-model threads · reviewer ≠ adjudicator · honest-null. The reviewer proposes an attack and a defense; the executor validates that every kept point is anchored to a real ledger claim (verbatim span) or a real sibling finding; the deterministic tools/adjudicate_findings.py owns the verdict and caps this skill at info (references/integrity-forensics-contract.md rules 1–2, 7–8; references/reviewer-independence.md Layer 2). This skill computes no verdict — by design.

Deliberate exception to "no prior-dimension findings." Reviewer-independence normally forbids leaking the executor's hunches and forbids one fresh per-dimension audit seeing another's conclusions. This skill is the explicit synthesis stage: the anchored, machine-validated findings are its input. It still receives only structured artifacts — claims.json + the *.findings.json files — never a Claude-authored digest or opinion about the paper, so the spirit of the rule (no executor judgment leaks into the reviewer) holds.