adversarial-case-builder

name	adversarial-case-builder
description	Synthesize the single strongest EVIDENCE-BOUND reviewer case to reject a paper, built ONLY from the evidence ledger (claims.json) + the other auditors' confirmed findings — never free-floating LLM critique. Two fresh cross-model codex threads: an attack writes the ~200-word rejection paragraph (every accusation tagged to an existing claim_id/finding_id), a defense decomposes it and rules each point against the anchored evidence. MEMO-ONLY: emits adversarial-case-builder.memo.md (fed to the adjudicator via --memo) and carries NO verdict weight — tools/adjudicate_findings.py lists it in ZERO_WEIGHT_SKILLS and caps it at info. Honest-null allowed (the paper may survive). Run LAST. Detect-only. Adapted from ARIS kill-argument. Triggers: "adversarial case", "strongest objection", "rejection memo", "kill argument", "最强拒稿点".
argument-hint	["paper-dir | claims.json"]
allowed-tools	Bash(*), Read, Write, mcp__codex__codex

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).

🔒 Do not wrap this skill in /loop, /schedule, or CronCreate. It runs LAST and synthesizes the ledger + the other auditors' findings into one memo. Even though it is memo-only (the adjudicator caps it at info, so it adds no verdict weight), the no-new-signal cadence rule still applies: its output changes only when the ledger / the findings / the paper change, never with the wall clock. Schedule the work that precedes it — ledger + auditors done → run this once. (Mirrors ARIS's external-cadence doctrine.)

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 ZERO_WEIGHT_SKILLS and caps anything from it at info. The deterministic adjudicator owns the verdict; this skill owns the memo.

Why this exists

The standard auditors (consistency-audit, citation-forensics, …) fan out and each flags discrepancies in its own dimension. They produce a balanced list — each discrepancy at its own severity, none committing to "this is the one that sinks the paper." That misses a specific failure mode: the single most damaging paragraph a senior area chair would write in a rejection. A balanced reviewer lists "scope-overclaim" as one major among several and never commits; an adversarial reviewer must commit — their whole job is to convince the AC to reject in ~200 words.

This skill runs that adversarial pass deliberately, then forces a second fresh reviewer to decompose the attack and rule each point against the evidence. The forensics twist over ARIS kill-argument: the attack is fenced to anchored evidence. A reviewer who can cite anything tends to manufacture a confident kill out of nothing — the exact dynamic that makes "AI reviews AI" feel like noise. By forcing every accusation to cite an existing ledger claim_id or finding_id, the memo can only be as strong as the evidence the deterministic layers already graded. If that evidence is weak, the correct output is an honest null: the paper survives. Manufacturing a kill from thin evidence is a failure, not a success.

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.

How this differs from the other auditors (route correctly)

Auditor	Question it answers	Level	Verdict weight
consistency-audit	Does the paper contradict ITSELF / described method = evaluated method?	L0	yes (via adjudicator)
experiment-forensics	Are reported numbers what the code computes? (fake GT, self-norm, phantom)	L2	yes
baseline-comparison-audit	Right baselines present, tuned, "SOTA" earned?	L0 stated / L2 verified	yes
citation-forensics	Do cited papers exist and support the claim made?	L0	yes
presentation-signals	Surface "AI-flavor" hints (auxiliary)	L0	surface-class label
adversarial-case-builder (this)	Strongest anchored rejection memo + defense	any (inherits anchors' level)	none (memo-only, zero verdict weight)

This skill detects nothing new. It does not re-read the paper to invent objections, does not assign severities the upstream auditors didn't already license, and does not originate a substantive flag. It narrates the worst case the existing evidence supports and stress-tests it. A new discrepancy belongs to the auditor that owns it (code/result fraud → experiment-forensics at L2; existence/context → citation-forensics; "SOTA/first" → baseline-comparison-audit), not here.

How this differs from ARIS kill-argument (the parent)

	ARIS kill-argument	adversarial-case-builder (forensics)
Reviewer may cite	any file:line / equation in the paper	ONLY existing claim_id / finding_id (evidence-bound)
Output	KILL_ARGUMENT.{md,json} with a 6-state PASS/WARN/FAIL verdict	adversarial-case-builder.memo.md — no verdict
Who decides	the skill maps per-point counts → verdict	the deterministic adjudicator, which caps this skill at info
Weak evidence	still writes the sharpest attack it can	returns an honest null (paper survives)
Position in flow	run once before submission	run LAST in the pipeline (needs ledger + upstream findings)

The attack-then-defense, two-fresh-threads, ~200-word-commit structure is kept exactly, because asking one model to "write the rejection memo" produces qualitatively sharper feedback than "review and grade" — the former forces commitment, the latter encourages hedging.

Constants & Reviewer Calling Convention

REVIEWER_MODEL      = gpt-5.6-sol            # different family from executor (Claude)
REVIEWER_REASONING  = xhigh             # always; effort never lowers reviewer quality
REVIEWER_SANDBOX    = read-only         # detect-only; never mutate the paper
REVIEWER_CWD        = <paper-dir>       # so it reads claims.json + *.findings.json directly
THREAD_POLICY       = TWO fresh mcp__codex__codex calls (Thread 1 attack, Thread 2 defense);
                      NEVER mcp__codex__codex-reply, and NEVER carry Thread 1's codex history into Thread 2
CONCURRENCY         = serial            # Codex MCP hangs on concurrent calls — Thread 2 waits for Thread 1
ATTACK_LENGTH       = ~200 words (NEVER exceed 250); ONE committed argument, not a list
DEFENSE_POINTS      = 3-7 atomic anchored points (empty array under honest-null)
CLASSIFICATION      = already_addressed | partially_addressed | unresolved
ANCHOR_UNIVERSE     = ledger claim_ids (claims.json) + finding_ids (sibling *.findings.json, skill-prefixed)
DISPOSITION         = kill_constructed | partial_case | honest_null   # informational, NOT the report verdict
TAXONOMY_VERSION    = 0.5               # references/hack-pattern-taxonomy.md
MEMO_FILE           = adversarial-case-builder.memo.md          # canonical output (fed to --memo)
FINDINGS_FILE       = adversarial-case-builder.findings.json    # info-only mirror (or []), zero verdict weight
TRACE_POLICY        = forensic (never silently dropped)
TRACE_DIR           = .aris/traces/adversarial-case-builder/<YYYY-MM-DD>_run<NN>/

• Executor (Claude) owns none of the judgment: it locates the ledger + the merged findings, passes paths + the ledger + the findings (+ the attack memo to Thread 2) to each reviewer, validates every anchor the reviewers return, and renders the memo. It never summarizes the paper, pre-judges, or leaks an opinion into a prompt (references/reviewer-independence.md).
• Reviewer (codex / gpt-5.6-sol, xhigh, read-only) reads claims.json + the *.findings.json from its cwd, writes the attack (Thread 1) and the defense (Thread 2), and self-reports honestly. It is the case-builder, not the judge.
• Two fresh threads, no codex-reply. Attack and Defense are independent mcp__codex__codex calls; Thread 2 receives the attack memo as text, never Thread 1's codex context (the bias guard). codex-reply is intentionally absent from allowed-tools.

---

Step 0 — Preconditions: locate the ledger + the upstream findings, read the level

This skill reasons over the ledger + the other auditors' findings — never the raw PDF. Resolve them and read the observability level L and paper_id (each Bash block is self-contained — shell state does not persist between calls, so re-derive paths every time):

ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
# $ARGUMENTS is a paper-dir OR a claims.json path:
LEDGER="$ARGUMENTS"; [ -d "$LEDGER" ] && LEDGER="$LEDGER/claims.json"
# Only the NO-ARGUMENT case defaults to the CWD ledger. An EXPLICIT argument that
# resolves to a missing claims.json must NOT silently fall back to $(pwd) — that
# could audit the wrong paper; let the NO_LEDGER check below fire instead.
[ -z "$ARGUMENTS" ] && LEDGER="$(pwd)/claims.json"
python3 - "$LEDGER" <<'PY'
import json, sys, os, glob
p = sys.argv[1]
if not os.path.isfile(p):
    sys.exit("NO_LEDGER: claims.json not found. Run /evidence-ledger FIRST, then the "
             "auditor skills, THEN this skill (it synthesizes their output, run LAST).")
d = json.load(open(p, encoding="utf-8")); D = os.path.dirname(os.path.abspath(p)) or "."
sib = [os.path.basename(f) for f in sorted(glob.glob(os.path.join(D, "*.findings.json")))
       if not os.path.basename(f).startswith("adversarial-case-builder")]
print("LEDGER         =", os.path.abspath(p))
print("PAPER_DIR      =", D)
print("PAPER_ID       =", d.get("paper_id", "?"))
print("RUN_LEVEL_L    =", d.get("observability_level", 0))
print("CLAIMS         =", len(d.get("claims", [])))
print("FINDINGS_FILES =", sib if sib else "(none — auditors have not run)")
PY

Failure handling.

• NO_LEDGER printed → stop. Tell the user to run /evidence-ledger (then the auditor skills) first — this skill is the last step and never re-reads the raw PDF to invent structure (contract rule 1).
• FINDINGS_FILES = (none …) → strongly prefer to stop and run the auditors first. With no findings the attack may cite only ledger claim_ids, and this skill must NOT originate a substantive objection from a lone claim (that is detection + a needs_external_check the auditors own — not synthesis). If you proceed anyway, honest_null is the expected, correct outcome; do not manufacture a kill from claims alone.
• Carry PAPER_DIR, PAPER_ID, RUN_LEVEL_L (=L), the absolute LEDGER, and the FINDINGS_FILES list forward — every later step needs them.

Create the trace directory now (forensic; written before any reviewer call):

PAPER_DIR="<abs PAPER_DIR from Step 0>"
DATE=$(date +%F); N=1
while [ -d "$PAPER_DIR/.aris/traces/adversarial-case-builder/${DATE}_run$(printf %02d $N)" ]; do N=$((N+1)); done
TRACE="$PAPER_DIR/.aris/traces/adversarial-case-builder/${DATE}_run$(printf %02d $N)"
mkdir -p "$TRACE"; echo "TRACE = $TRACE"

Write "$TRACE/run.meta.json" (via Write) = {"skill":"adversarial-case-builder","paper_id":"<PAPER_ID>","run_level_L":<L>,"findings_files":[…],"generated_at":"<UTC ISO-8601>"}.

Step 1 — Attack (Thread 1, fresh codex, evidence-bound ~200 words)

Open a fresh mcp__codex__codex thread (NOT codex-reply). The reviewer reads claims.json + the sibling *.findings.json from its cwd and writes the single strongest rejection paragraph — every accusation anchored inline. Substitute the <…> placeholders from Step 0 and send EXACTLY:

mcp__codex__codex:
  model: gpt-5.6-sol
  config: {"model_reasoning_effort": "xhigh"}
  sandbox: read-only
  cwd: <absolute PAPER_DIR from Step 0>
  prompt: |
    You are a senior NeurIPS/ICLR/ICML area chair writing the SINGLE STRONGEST case to
    reject this paper. This is an EVIDENCE-BOUND adversarial pass inside an
    integrity-forensics pipeline: you do NOT roam the paper to invent objections — you
    build the rejection ONLY from evidence that other auditors already anchored.

    INPUTS — read these directly from your working directory:
      - claims.json — the evidence ledger: every checkable claim, span-anchored
        {claim_id (e.g. C012), type, text_span (VERBATIM source text), location, value?}.
        This is the authoritative structure; do not invent claims that are not in it.
      - the other auditors' confirmed findings (read each; do NOT read
        adversarial-case-builder.findings.json):
        <paste the FINDINGS_FILES list from Step 0, one filename per line>
        Each finding = {finding_id (e.g. F003), skill, severity, false_positive_risk,
        observability_level_required, evidence:[{claim_id, span}], pattern_id?}.
    RUN OBSERVABILITY LEVEL L = <L from Step 0>.

    HARD RULES — a memo that breaks any of these is worthless:
    1. EVIDENCE-BOUND. EVERY accusation must cite >=1 EXISTING id inline, in square
       brackets right after the sentence it supports: a ledger claim [C012], or a
       finding [F003] (use the skill-qualified form [citation-forensics:F003] when a
       bare id could be ambiguous across files). You may NOT introduce an accusation
       not anchored to a real claim_id / finding_id. Uncited rhetoric is DELETED
       downstream — do not waste words on it.
    2. QUOTE FOR CLAIMS. A point that cites a claim_id must rest on a VERBATIM
       substring of that claim's text_span (no paraphrase). A point that cites a
       finding INHERITS that finding's severity and observability ceiling.
    3. OBSERVABILITY CEILING — DO NOT UPGRADE EVIDENCE. A cited finding marked
       minor / high false_positive_risk, or whose observability_level_required exceeds
       L (e.g. a code/result-level signal on an L0/L1 run), CANNOT be turned into a
       decisive kill. Build the strongest case the GRADED evidence licenses — not the
       case you wish you had.
    4. DISCREPANCY, NOT MISCONDUCT. Argue the headline is UNSUPPORTED / OVERCLAIMED /
       INTERNALLY INCONSISTENT on the anchored evidence. Do NOT allege fabrication or
       write "the authors faked X". This is a merits rejection, not an accusation.
    5. COMMIT. ~200 words (NEVER exceed 250). ONE coherent line of attack — select and
       fuse at most the two most damaging axes; do NOT enumerate a balanced list, do
       NOT hedge ("the authors might respond" — the defense gets the next pass).
    6. HONEST NULL IS A VALID OUTPUT. If the anchored evidence does NOT license a
       strong rejection (the only anchored findings are minor / high-FP / info, or
       every decisive one declares a level above L), SAY SO plainly in <=120 words: "the
       anchored evidence does not support a strong rejection because …". Do NOT
       manufacture a kill from weak evidence — an honest null is the correct, expected
       answer in that case.

    OUTPUT: just the memo text — the ~200-word committed rejection WITH inline [id]
    anchors, OR the honest-null paragraph. Nothing else (no JSON, no preamble, no code
    fence).

Persist immediately, then carry forward. Save the raw response verbatim to "$TRACE/001-attack.response.md" (Write); also write "$TRACE/001-attack.request.json" (the exact prompt + paths sent — the independence audit trail) and "$TRACE/001-attack.meta.json" ({"model":"gpt-5.6-sol","reasoning":"xhigh","thread_id":"<id>","sandbox":"read-only"}). Keep threadId as attack_thread_id; do NOT pass it to Thread 2.

Failure handling.

• MCP stall / hang (common in long sessions): re-invoke the identical prompt as a fresh mcp__codex__codex call (gpt-5.6-sol, xhigh) — never codex-reply.
• Empty response: re-ask once. If still empty, record status: ERROR in the memo header and stop — do not hand-author an attack.

Step 1.5 (optional, — effort: beast): multi-axis attack fan-out

Default OFF. The deliverable is one committed paragraph; forcing a single commitment yields sharper feedback than a balanced list. Fan-out widens the evidence the commitment draws on, never the commitment itself (mirrors ARIS fan-out-pattern.md: fan out the evidence, not the verdict — and here there is no verdict at all). Under beast: run up to six axes (headline-number / method-scope / baseline / citation / experiment / scope-evidence) as separate fresh mcp__codex__codex probes, each asked for its strongest ~120-word anchored thrust on that axis alone; then a final fresh-codex synthesis commits to one ~200-word anchored paragraph (the Step-1 output). These are NOT Claude subagents and there is deliberately no Agent grant — the adversary must be cross-model (non-Claude), and Codex MCP is serial (concurrent calls hang), so the probes run sequentially (Tier-3 in the fan-out ladder). This is exactly why allowed-tools lists no Agent: this skill spawns nothing; it threads codex calls. Record each probe's threadId and the synthesis threadId in the trace; only the committed attack feeds Step 2.

Step 2 — Defense (Thread 2, fresh codex, decompose + classify)

Open a second, independent fresh mcp__codex__codex thread (still NOT codex-reply). Paste the attack memo from Step 1 verbatim into the marked slot. Send EXACTLY:

mcp__codex__codex:
  model: gpt-5.6-sol
  config: {"model_reasoning_effort": "xhigh"}
  sandbox: read-only
  cwd: <absolute PAPER_DIR from Step 0>
  prompt: |
    You are an INDEPENDENT defense reviewer — a checker, NOT the adjudicator
    (deterministic code owns the verdict). A hostile reviewer wrote the
    rejection memo below, citing claim_ids / finding_ids. Read it point-by-point
    against the SAME anchored evidence and rule, honestly, how much of the case
    actually STANDS. You are NOT the paper's defender and NOT the attacker — you check
    whether each ANCHORED accusation holds up on the evidence.

    INPUTS — read directly from your working directory:
      - claims.json — the evidence ledger (claim_id -> VERBATIM text_span).
      - the other auditors' confirmed findings (do NOT read
        adversarial-case-builder.findings.json):
        <paste the FINDINGS_FILES list from Step 0, one filename per line>
    RUN OBSERVABILITY LEVEL L = <L from Step 0>.

    ## The hostile reviewer's rejection memo (the "attack")
    <paste the attack memo VERBATIM from Thread 1, with its inline [id] anchors>

    ## Your task
    Decompose the attack into its atomic rejection points (3-7). If the attack
    concluded the evidence does NOT support a rejection (honest null), return an EMPTY
    array []. For EACH point:
      - record the anchor id(s) it cites (claim_id and/or finding_id);
      - VERIFY the anchor actually supports the point. If the attack over-reads a
        minor / high-FP / observability-demoted finding into something decisive, or
        cites a claim that does not say what the attack claims, say so and classify the
        point already_addressed — the evidence does not license it;
      - classify it:
          already_addressed   — the anchored evidence does NOT sustain this objection
                                (over-read, demoted at L, FP-prone, or the
                                ledger/findings already account for it);
          partially_addressed — a real but bounded issue; not by itself decisive;
          unresolved          — the anchored evidence genuinely leaves the headline
                                unsupported and nothing in the ledger/findings rescues it.
      - OBSERVABILITY: if a point is decidable only at L2 (needs code/results) and
        L < 2, you CANNOT call it refuted OR proven from text. Do NOT classify it
        unresolved (at L<2 the evidence cannot leave the headline "unsupported");
        classify it partially_addressed, set observability_level_required = 2, and
        frame reviewer_action as "verify at L2" — never as an assertion of fabrication.
      - AUTHOR-CHOSEN POSITIONS (a deliberate scope choice, a labelled pilot, a stated
        omission): classify partially_addressed with a note that the position is
        intentional, AND say whether it is sustainable under the attack. Do NOT
        auto-grade already_addressed merely because it is intentional.

    HONESTY: do not inflate to unresolved without a real anchor; do not minimize a
    genuinely evidence-backed objection. Discrepancy-framing only — reviewer_action is
    what a human should CHECK or ASK, never "reject" / "fabricated".

    OUTPUT: a single JSON array and NOTHING ELSE (no prose, no code fence). Each
    element EXACTLY these keys:
      {
        "id": "P1",
        "label": "short label",
        "attack_claim": "the specific accusation, ~30 words",
        "anchors": [{"ref": "C012", "span": "verbatim substring of C012"},
                    {"ref": "citation-forensics:F003"}],
        "classification": "already_addressed | partially_addressed | unresolved",
        "residual_severity": "critical | major | minor",
        "observability_level_required": 0,
        "defense_evidence": "~40 words: does the anchored evidence sustain this point?",
        "reviewer_action": "what a human should CHECK or ASK — never 'reject'"
      }

Persist immediately. Save the raw response to "$TRACE/002-defense.response.md" (Write), plus "$TRACE/002-defense.request.json" (the exact prompt sent — attack memo + paths, no Claude digest) and "$TRACE/002-defense.meta.json" (same shape as Step 1). Keep defense_thread_id.

Failure handling.

• Returns prose, not a JSON array: Step 3 extracts the outermost [...]. If there is none, re-ask once: "Output ONLY the JSON array, nothing else." Do not hand-author the decomposition.
• MCP hang: re-invoke the identical prompt as a fresh call; never codex-reply.

Step 3 — Validate + anchor, then render the memo (the anti-slop gate)

Everything the reviewers proposed is now validated deterministically by the executor (claim anchors require a verbatim span; finding anchors require the id to exist in a sibling findings file). The adjudicator independently re-applies the full gate stack (span-anchor → observability → FP → memo → surface) as the authoritative verdict, so this memo can never out-rank it. A claim anchor is valid only if its claim_id exists in the ledger AND its span is a verbatim, whitespace-normalized substring of that claim (span in text_span, never the reverse — appending hallucinated text to a real claim must fail). A finding anchor is valid if its id (bare Fxxx or skill-qualified skill:Fxxx) exists in the sibling findings. A point with no valid anchor is uncited rhetoric and is dropped from the load-bearing case. The informational disposition (NOT a verdict) is kill_constructed only when an unresolved point rests on a finding the upstream auditors already graded critical, false_positive_risk: low, AND decidable at the run's level L — the evidence-bound guarantee made literal. This single command writes both deliverables:

LEDGER="<abs path to claims.json from Step 0>"
ATTACK="<abs path to $TRACE/001-attack.response.md>"
DEFENSE="<abs path to $TRACE/002-defense.response.md>"
python3 - "$LEDGER" "$ATTACK" "$DEFENSE" "<ATTACK_THREAD_ID>" "<DEFENSE_THREAD_ID>" <<'PY'
import json, re, sys, os, glob
ledger_path, attack_path, defense_path = sys.argv[1], sys.argv[2], sys.argv[3]
attack_tid = sys.argv[4] if len(sys.argv) > 4 else ""
defense_tid = sys.argv[5] if len(sys.argv) > 5 else ""

def nw(s): return " ".join((s or "").split())

# ---- evidence universe: ledger claims + sibling findings (never our own output) ----
led = json.load(open(ledger_path, encoding="utf-8"))
D = os.path.dirname(os.path.abspath(ledger_path)) or "."
L = int(led.get("observability_level", 0))
PID = led.get("paper_id", "?")
claims = {c["claim_id"]: c for c in led.get("claims", []) if c.get("claim_id")}

findings, n_findings, bad_findings = {}, 0, []
for fp in sorted(glob.glob(os.path.join(D, "*.findings.json"))):
    if os.path.basename(fp).startswith("adversarial-case-builder"):
        continue                                   # never let the memo cite itself
    try:
        arr = json.load(open(fp, encoding="utf-8"))
    except Exception as e:                          # forensic: never SILENTLY drop input
        print("WARN: unreadable findings file skipped: %s (%s) — evidence universe "
              "reduced; fix it and re-run" % (os.path.basename(fp), e), file=sys.stderr)
        bad_findings.append(os.path.basename(fp))
        continue
    if isinstance(arr, dict): arr = arr.get("findings", [])
    for it in (arr or []):
        if not isinstance(it, dict) or not it.get("finding_id"):
            continue
        fid, sk = it["finding_id"], it.get("skill", "")
        olr = it.get("observability_level_required")
        meta = {"skill": sk, "severity": it.get("severity", "info"),
                "fpr": it.get("false_positive_risk", "high"),
                "olr": olr if (type(olr) is int and 0 <= olr <= 3) else None}
        findings[fid] = meta                        # bare id (last wins on collision)
        if sk: findings["%s:%s" % (sk, fid)] = meta # skill-qualified (globally unique)
        n_findings += 1

# ---- attack prose: strip a stray code fence; flag dangling inline citations ----
attack = re.sub(r"^```[a-zA-Z]*\n|\n```$", "", open(attack_path, encoding="utf-8").read().strip()).strip()
cited = {t.strip() for t in re.findall(r"\[([A-Za-z][\w:.\-]*\d[\w:.\-]*)\]", attack)}
dangling = sorted(t for t in cited if t not in claims and t not in findings)

# ---- defense JSON points ----
draw = open(defense_path, encoding="utf-8").read()
m = re.search(r"\[.*\]", draw, re.S)               # tolerate prose / code-fence wrapping
try:
    points = json.loads(m.group(0) if m else draw)
except Exception:
    sys.exit("DEFENSE_PARSE_FAILED: re-run Step 2 with 'Output ONLY the JSON array, nothing else.'")
if isinstance(points, dict): points = points.get("points", [])

CLS = {"already_addressed", "partially_addressed", "unresolved"}
SEV = {"critical", "major", "minor"}

def valid_anchor(a):
    if not isinstance(a, dict): return None
    ref = a.get("ref") or a.get("id") or ""
    span = nw(a.get("span", ""))
    if ref in claims:                              # claim anchor REQUIRES a verbatim span
        if span and span in nw(claims[ref].get("text_span", "")):
            return {"ref": ref, "kind": "claim", "span": span}
        return None
    if ref in findings:                            # finding id alone is a valid anchor
        f = findings[ref]
        return {"ref": ref, "kind": "finding", "severity": f["severity"],
                "fpr": f["fpr"], "olr": f["olr"]}
    return None

kept, dropped = [], []
for i, p in enumerate(points, 1):
    if not isinstance(p, dict): continue
    good = [v for v in (valid_anchor(a) for a in (p.get("anchors") or [])) if v]
    cls = p.get("classification"); cls = cls if cls in CLS else "already_addressed"  # unknown -> not load-bearing
    rs = p.get("residual_severity"); rs = rs if rs in SEV else "minor"
    olr = p.get("observability_level_required")
    olr = olr if (type(olr) is int and 0 <= olr <= 3) else 0
    rec = {"id": p.get("id") or ("P%d" % i), "label": p.get("label", ""),
           "attack_claim": nw(p.get("attack_claim", "")), "classification": cls,
           "residual_severity": rs, "observability_level_required": olr,
           "defense_evidence": nw(p.get("defense_evidence", "")),
           "reviewer_action": nw(p.get("reviewer_action", "")), "anchors": good}
    (kept if good else dropped).append(rec)
for j, p in enumerate(kept, 1): p["id"] = "P%d" % j   # stable renumber

def decidable_crit(p):
    # INFORMATIONAL heuristic (NOT a verdict): flag a "constructed" kill only if an
    # UNRESOLVED point rests on a finding the upstream auditor DECLARED critical, FP
    # low, and decidable at the run level L. This filter is what keeps a minor /
    # high-FP / above-run-level finding out of an unresolved case; the adjudicator
    # re-applies the anchor check and owns the summary.
    return p["classification"] == "unresolved" and any(
        a["kind"] == "finding" and a.get("severity") == "critical"
        and a.get("fpr") == "low" and type(a.get("olr")) is int and a["olr"] <= L
        for a in p["anchors"])

if any(decidable_crit(p) for p in kept):
    disp = "kill_constructed"
elif any(p["classification"] in ("unresolved", "partially_addressed") for p in kept):
    disp = "partial_case"
else:
    disp = "honest_null"
unresolved = [p for p in kept if p["classification"] == "unresolved"]
counts = {c: sum(1 for p in kept if p["classification"] == c) for c in CLS}

# ---- render the memo (markdown; embedded verbatim under the adjudicator's memo H2) ----
def anchstr(a):
    if a["kind"] == "claim":
        loc = claims[a["ref"]].get("location", {}) or {}
        where = loc.get("section") or os.path.basename(str(loc.get("file", ""))) or ""
        return ("claim `%s`" % a["ref"]) + ((" (%s)" % where) if where else "") + \
               ((": “%s”" % a["span"]) if a.get("span") else "")
    f = findings.get(a["ref"], {})
    return "finding `%s`" % a["ref"] + ((" (%s, %s)" % (f.get("skill", ""), f.get("severity", ""))) if f else "")

BADGE = {"already_addressed": "✅ already_addressed",
         "partially_addressed": "\U0001f7e1 partially_addressed",
         "unresolved": "\U0001f534 unresolved"}
out = []
out.append("**Adversarial Case — %s** (evidence-bound, MEMO-ONLY — no verdict weight)." % PID)
out.append("Reviewer: gpt-5.6-sol xhigh, two fresh threads (no codex-reply)  ·  Run level: L%d  "
           "·  Attack thread: %s  ·  Defense thread: %s" % (L, attack_tid or "—", defense_tid or "—"))
out.append("Disposition (informational, NOT the report verdict): **%s**  ·  Evidence universe: "
           "%d ledger claims · %d confirmed findings" % (disp, len(claims), n_findings))
if dangling:
    out.append("⚠ Dangling attack citations dropped (not in the ledger/findings): %s" % ", ".join(dangling))
if bad_findings:
    out.append("⚠ Unreadable findings file(s) SKIPPED (evidence universe reduced): %s" % ", ".join(bad_findings))
out += ["", "### Strongest case to reject (attack, verbatim)", ""]
out += [("> " + ln) if ln.strip() else ">" for ln in (attack or "_(empty attack)_").splitlines()]
out += ["", "### Point-by-point adjudication (evidence-bound)", ""]
order = (unresolved + [p for p in kept if p["classification"] == "partially_addressed"]
         + [p for p in kept if p["classification"] == "already_addressed"])
if not order:
    out += ["_No anchored objection survived validation._", ""]
for p in order:
    out.append("#### %s — %s  ·  %s" % (p["id"], p["label"] or "(unlabeled)",
                                                  BADGE.get(p["classification"], p["classification"])))
    if p["attack_claim"]: out.append("- **Attack:** %s" % p["attack_claim"])
    if p["anchors"]:      out.append("- **Anchors:** " + " ; ".join(anchstr(a) for a in p["anchors"]))
    if p["defense_evidence"]: out.append("- **Defense / evidence:** %s" % p["defense_evidence"])
    if p["classification"] == "unresolved":
        out.append("- **Residual severity (descriptive, not a verdict):** %s · decidable at L%d"
                   % (p["residual_severity"], p["observability_level_required"]))
    if p["reviewer_action"]: out.append("- **Reviewer action:** %s" % p["reviewer_action"])
    out.append("")
out += ["### Unresolved questions for the human reviewer", ""]
out += ([ "- (%s) %s" % (p["id"], p["reviewer_action"] or p["attack_claim"]) for p in unresolved]
        or ["- _None — no objection went unresolved on the anchored evidence._"])
out.append("")
if disp == "honest_null":
    out += ["### Honest null", "",
            "On the anchored evidence at L%d, the strongest evidence-bound case does **not** sustain a "
            "rejection: every anchored objection is `already_addressed` or only `partially_addressed` "
            "(minor / high-FP / observability-demoted signals, or claims already accounted for). The "
            "paper **survives** this adversarial pass at this level. No kill was manufactured — this "
            "is a valid, expected result." % L, ""]
if dropped:
    out += ["### Dropped (uncited rhetoric — no valid anchor, excluded from the case)", ""]
    out += ["- %s: %s" % (p["label"] or "(no label)", p["attack_claim"]) for p in dropped]
    out.append("")
out += ["### Anchoring audit", "",
        "- points kept: %d  ·  dropped (uncited): %d  ·  dangling attack citations: %d"
        % (len(kept), len(dropped), len(dangling)),
        "- classification: already_addressed %d · partially_addressed %d · unresolved %d"
        % (counts["already_addressed"], counts["partially_addressed"], counts["unresolved"]), ""]
out += ["---",
        "_Informational only. `tools/adjudicate_findings.py` lists `adversarial-case-builder` in "
        "`ZERO_WEIGHT_SKILLS` and caps every finding it could emit at `info`, so this memo contributes "
        "**no verdict weight**. The deterministic adjudicator owns the verdict._"]
memo_path = os.path.join(D, "adversarial-case-builder.memo.md")
open(memo_path, "w", encoding="utf-8").write("\n".join(out) + "\n")

# ---- info-only findings mirror: one per unresolved point; MEMO gate caps at info anyway ----
import os as _aris_os
RESOLVED_MODEL = _aris_os.environ["ARIS_RESOLVED_MODEL"]          # exported by the executor from the call that ACTUALLY ran
RESOLVED_REASONING = _aris_os.environ["ARIS_RESOLVED_REASONING"]  # (fail LOUD if unset — never stamp a target default)
acb = []
for k, p in enumerate(unresolved, 1):
    ev = [{"claim_id": a["ref"], "span": a["span"],
           "location": claims[a["ref"]].get("location", {}),
           "artifact_hash": claims[a["ref"]].get("evidence_anchor", "")}
          for a in p["anchors"] if a["kind"] == "claim" and a.get("span")]
    acb.append({
        "finding_id": "ACB%03d" % k, "skill": "adversarial-case-builder",
        "title": (p["label"] or "adversarial objection")[:120],
        "description": (p["attack_claim"] + ((" — " + p["defense_evidence"]) if p["defense_evidence"] else "")).strip(),
        "severity": "info",                        # memo-only: never verdict-bearing
        "observability_level_required": p["observability_level_required"],
        "evidence": ev,                            # may be [] (schema permits empty evidence for info)
        "verdict_local": "warn", "requires_external_check": False, "false_positive_risk": "high",
        "recommended_reviewer_action": p["reviewer_action"] or ("Press the authors on: " + p["attack_claim"]),
        "reviewer": {"model": RESOLVED_MODEL, "reasoning": RESOLVED_REASONING, "deterministic": False, "thread_id": defense_tid},
    })
find_path = os.path.join(D, "adversarial-case-builder.findings.json")
json.dump(acb, open(find_path, "w", encoding="utf-8"), indent=2, ensure_ascii=False)

print("disposition=%s (informational, NOT a verdict) | kept=%d (unresolved=%d, partially_addressed=%d, "
      "already_addressed=%d) | dropped_uncited=%d | dangling=%d | bad_findings=%d"
      % (disp, len(kept), counts["unresolved"], counts["partially_addressed"],
         counts["already_addressed"], len(dropped), len(dangling), len(bad_findings)))
print("memo     ->", memo_path)
print("findings ->", find_path, "(%d info-only)" % len(acb))
PY

Scope of this gate: anchor validation (verbatim-span for claims, id-existence for findings), classification hygiene, uncited-rhetoric dropping, the informational disposition, and rendering. It computes no verdict — the memo is informational and the adjudicator decides.

Failure handling.

• DEFENSE_PARSE_FAILED → re-run Step 2 with the strict-JSON reminder, then re-run this command.
• All attack citations dangling AND the attack made strong accusations (not an honest null) → the reviewer hallucinated its anchors; re-run Step 1 once (it must cite real ids). Do not ship a confident memo built on nothing.
• All points already_addressed / dropped / empty defense → disposition=honest_null. This is a correct output; the memo says the paper survives. Do not re-run to force a kill.

Step 4 — Trace (forensic; never silently dropped)

After the run, the trace dir from Step 0 must contain both reviewer calls (this repo ships no save_trace.sh, so the files were written directly in Steps 0–2):

.aris/traces/adversarial-case-builder/<date>_run<NN>/
  run.meta.json                # {skill, paper_id, run_level_L, findings_files, generated_at}
  001-attack.request.json      # the exact prompt + paths sent (independence audit trail)
  001-attack.response.md       # FULL raw attack memo (input to Step 2 + the memo)
  001-attack.meta.json         # {model, reasoning, thread_id, sandbox}
  002-defense.request.json     # the exact prompt sent (attack memo pasted verbatim, paths only)
  002-defense.response.md      # FULL raw defense JSON (input to Step 3)
  002-defense.meta.json        # {model, reasoning, thread_id, sandbox}
  # (beast only) 0NN-axis-<name>.{request.json,response.md,meta.json} per probe + synthesis

Each request.json must show the executor sent only paths + the ledger/findings (+ the attack memo for Thread 2) — never a Claude-authored digest or opinion about the paper (the reviewer-independence audit trail). The response.md files are the immutable inputs that Step 3 consumes.

Step 5 — Hand off, or adjudicate standalone

Within /anti-autoresearch, stop here: the orchestrator globs every *.findings.json, runs the adjudicator, and passes this memo via --memo "$(cat adversarial-case-builder.memo.md)", where it renders under "Adversarial memo (informational — no verdict weight)". Running this skill alone is unusual (it normally synthesizes the other auditors), but you may produce the report yourself — --ledger is required (it anchors every above-info finding; without it everything fails closed to info):

ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
LEDGER="<abs path to claims.json>"; D="$(dirname "$LEDGER")"
python3 "$ROOT/tools/adjudicate_findings.py" \
    --findings "$D"/*.findings.json \
    --ledger "$LEDGER" \
    --paper-id "<PAPER_ID>" --observability-level <L> --taxonomy-version 0.5 \
    --memo "$(cat "$D/adversarial-case-builder.memo.md")" \
    --out "$D/report.json" --md "$D/REPORT.md"

The adjudicator applies its gates in order (ANCHOR → OBSERVABILITY → FP-RISK → MEMO → SURFACE) and computes overall_verdict ∈ {CLEAN_GIVEN_EVIDENCE, SOFT_FLAGS, HARD_FLAGS} from the other auditors' findings. The MEMO gate caps every adversarial-case-builder finding at info, so this skill cannot move the verdict — by design. No model is in the final decision.

Output contract

This skill always writes, into the ledger's directory:

• adversarial-case-builder.memo.md — canonical. Attack (verbatim) + per-point adjudication + unresolved questions + honest-null/dropped sections + anchoring audit. Carries no verdict weight; fed to the adjudicator via --memo.
• adversarial-case-builder.findings.json — info-only mirror (one entry per unresolved point; possibly []), conforming to schemas/finding.schema.json, "skill":"adversarial-case-builder", every entry severity:"info". Written even when empty for predictable *.findings.json globbing; the adjudicator's MEMO gate makes it a no-op confirmation. It sets no pattern_id — this skill originates no detection; the patterns live on the findings it cites.
• .aris/traces/adversarial-case-builder/<date>_run<NN>/ — both raw reviewer calls.

It writes no verdict and no report of its own — report.json / REPORT.md come only from tools/adjudicate_findings.py (Step 5 / the orchestrator).

Key rules

• No verdict — by design. This skill never raises the report's verdict. The adjudicator's ZERO_WEIGHT_SKILLS gate caps it at info, and it is excluded from dimension_verdicts. The memo informs; the deterministic rules decide.
• Evidence-bound. Every attack point cites an existing claim_id / finding_id; claim citations carry a verbatim span. Step 3 deletes uncited rhetoric. span in claim, whitespace-normalized — never claim in span. This is exactly what separates the skill from generic LLM paper-bashing.
• No upgrading evidence. A finding's severity / FP-risk / observability ceiling are inherited, never raised. A minor / high-FP / observability-demoted finding cannot become a decisive kill — the informational kill_constructed heuristic requires an unresolved point resting on a finding the upstream auditor graded critical (FP low) and decidable at L; the adjudicator independently re-applies the full gate stack as the authoritative verdict.
• Synthesis, not detection. Raise no new discrepancy here — only synthesize what the ledger + the other auditors already anchored. New flags belong to the auditor that owns them.
• Honest null. If the anchored evidence is weak, the memo says the paper survives — it does not manufacture a kill. Expected output, not a failure.
• Two fresh threads, serial, cross-model. Attack and Defense are separate mcp__codex__codex calls (gpt-5.6-sol xhigh, read-only), never codex-reply, never concurrent. Reviewer ≠ executor ≠ adjudicator.
• Observability honesty. The memo cannot assert code/result-level fraud above the run level L; findings demoted by observability cannot be resurrected as a "kill" (references/observability-levels.md).
• Discrepancy, not accusation. Argue unsupported / overclaimed / inconsistent; questions are CHECK/ASK, never "reject" / "fabricated" / "the authors faked X".
• Detect-only. Never edit the audited paper; reviewer sandbox is read-only.
• Reproducible. Same ledger + same upstream findings + same reviewer outputs → same validated memo + same disposition.

When NOT to use this skill

• No claims.json yet → run /evidence-ledger first; this skill never invents structure from the raw PDF.
• Auditors haven't run → run /consistency-audit (and the others that apply) first; with no findings the case is claims-only and weak. This skill runs LAST.
• You need to DETECT a new discrepancy (a number contradiction, a bad citation, a missing baseline, code-level fraud) → use the owning detector (consistency-audit / citation-forensics / baseline-comparison-audit / experiment-forensics). This skill only narrates what they found.
• You want a verdict from this skill → impossible by design; it carries zero verdict weight and is reported at info. Read REPORT.md's overall_verdict from the adjudicator instead.
• You want an AI-text / "looks machine-written" verdict → out of scope; surface hints live in /presentation-signals (auxiliary, surface-class). This repo is not an AI-text classifier.
• On a timer → never /loop / /schedule / CronCreate this skill; re-fire only when the ledger / findings / paper change (see the fence at the top).

Review tracing

Trace policy is forensic (never silently skipped) — see Step 4 for the exact layout: run.meta.json + per-call NNN-<purpose>.{request.json,response.md,meta.json} for the attack, the defense, and (under beast) each axis probe + synthesis. Every request.json must contain only the paths + ledger/findings (and, for Thread 2, the attack memo text) that were sent — the reviewer-independence audit trail.