Imported from wenhaochai/claude-plugins (anti-autoresearch/skills/anti-autoresearch/SKILL.md). Install upstream with npx skills add wenhaochai/claude-plugins --skill anti-autoresearch. Copyright stays with the author.

/anti-autoresearch — the orchestrator

Run a full substantive-integrity forensic pass on $ARGUMENTS and hand a human reviewer / area chair an evidence-first Integrity Forensics Report.

🔒 External cadence: the verdict is not a poll. The pipeline is the verdict-producing path — its output changes only when the paper / repo / ledger changes, not with the clock. Do not wrap /anti-autoresearch (or any auditor sub-skill) in /loop / /schedule / CronCreate to "re-check". A heartbeat may only wait on the external steps that precede the verdict (an arXiv download, a citation web lookup) — never re-fire the adjudicated verdict, and never "decide the paper is fine now." Re-run the sweep when the inputs change; that is the only honest trigger.

🛡️ The dual of ARIS. ARIS ships an internal audit stack so its own autoresearch output stays honest; Anti-Autoresearch is that same audit DNA pointed outward at a third party's submission. This is decision support for a human — it surfaces span-anchored discrepancies to investigate. It is not an AI-text detector and it does not judge misconduct (DESIGN.md §1; references/).

Why this exists

A machine-driven research pipeline (or rushed human) writes the abstract, the tables, the method section, the bibliography, and the appendix in separate passes and never reconciles them. The result is a paper that disagrees with itself, cites papers that do not exist or argue the opposite, claims SOTA while omitting the obvious baseline, or reports numbers the code never computed. Six LLMs each re-reading the PDF would hallucinate six different structures and invite the obvious dismissal — "an LLM grading another LLM's paper is just slop."

This orchestrator answers that structurally. One deterministic pass turns the paper into a hashed, span-anchored evidence ledger; six auditors read only that ledger and propose findings; a deterministic adjudicator decides the verdict by fixed rules with no model in the loop; and observability levels make it impossible to shout "fraud" from a PDF. Same artifacts → same ledger → same verdict.

Pipeline role (what this run does, and never does)

[0] ingest          arxiv-id | pdf | dir  →  working dir (+ pdftotext text for L0)
        │
        ▼
[1] /evidence-ledger   tools/build_manifest.py + tools/build_claim_ledger.py
        │              → artifact_manifest.json (derives observability level L)
        │              → claims.json   (span-anchored, hashed; the ONLY structure auditors read)
        ▼
[2] fan out auditors (each reads the ledger, emits <skill>.findings.json):
        consistency-audit          (always · flagship · deterministic arithmetic + semantic)
        citation-forensics         (if ≥1 citation claim)
        baseline-comparison-audit  (if ≥1 comparison / SOTA scope claim)
        experiment-forensics       (always · L0/L1 = info "could-not-verify" · L2 = full code audit)
        presentation-signals       (always · AUXILIARY · capped at minor)
        ai-style-impressions       (always · AIS track · NOT integrity · zero verdict weight)
        proof-derivation-forensics (if ≥1 theorem/proof/derivation claim · verdict-bearing · dim=proof · L1 source, CAN reach HARD_FLAGS; L0 PDF-only → info)
        eval-design-forensics      (if ≥1 comparison/eval claim · family H · dim=evaluation · L0/L1 stated-tells: leakage / judge-validity / selective-reporting)
        │
        ▼
[3] advisory memos (each reads the ledger + merged findings · NO verdict weight):
        /adversarial-case-builder      → adversarial-case-builder.memo.md       (strongest evidence-bound objection)
        /novelty-duplication-advisory  → novelty-duplication-advisory.memo.md   (if ≥1 contribution claim · MEMO-ONLY · prior-work overlap · capped at info)
        │
        ▼
[4] tools/adjudicate_findings.py  --ledger REQUIRED  → report.json + REPORT.md
        │   gates (in order): ANCHOR → OBSERVABILITY → FP-RISK → MEMO → SURFACE
        │   overall_verdict ∈ {CLEAN_GIVEN_EVIDENCE, SOFT_FLAGS, HARD_FLAGS}  (rules, no model)
        ▼
[5] present REPORT.md to the human (verdict + level first; state what could NOT be checked)

• Auditors propose; the adjudicator decides. No auditor (and not this orchestrator) ever computes overall_verdict; only tools/adjudicate_findings.py does, by fixed rules (references/reviewer-independence.md Layer 2; DESIGN.md §3).
• Detect-only. No step ever edits the audited paper or repo. This is a third-party forensics tool, never a co-author (no Edit is granted; the reviewer sandbox is read-only).
• Observability caps everything. L0 (PDF-only) / L1 (source) cannot assert code/result-level fraud — those need L2 (repo + results). The adjudicator demotes any finding above the run's level (references/observability-levels.md; DESIGN.md §4).

Constants & conventions

REVIEWER_MODEL     = gpt-5.5  (via mcp__codex__codex) — a DIFFERENT model family from the executor (Claude)
REVIEWER_REASONING = xhigh    — always; the effort knob never lowers reviewer quality
REVIEWER_SANDBOX   = read-only — detect-only; the reviewer never mutates the paper
THREAD_POLICY      = FRESH mcp__codex__codex per dimension / per cited key; NEVER codex-reply across them
                     (codex-reply is deliberately NOT in allowed-tools — the bias guard)
RUN_THREADS        = serial   — one codex thread at a time; concurrent codex MCP calls can hang
LEDGER_VERSION     = 0.1       (stamped by tools/build_claim_ledger.py)
TAXONOMY_VERSION   = 0.5       (references/hack-pattern-taxonomy.md; 46 integrity patterns A–H + 13 AIS impressions + 2 ADV advisory; stamped into every report)
OBSERVABILITY      = derived   L0 (pdf/text) · L1 (latex, no results) · L2 (repo + results); L3 NEVER (no reproduction)
ADJUDICATOR        = deterministic-rules-v0   (tools/adjudicate_findings.py — the ONLY verdict source)
DETECT_ONLY        = true · EMITS_VERDICT = true (computed by code, not by a model)
REAL TOOLS (resolve ROOT="${CLAUDE_PLUGIN_ROOT}"):
   tools/build_manifest.py · tools/build_claim_ledger.py · tools/check_numeric_consistency.py
   · tools/check_presentation.py · tools/adjudicate_findings.py   (confirm flags with --help; never invent flags)
OUTPUTS (in PAPER_DIR) — artifact_manifest.json · claims.json · <skill>.findings.json (+ *.deterministic.findings.json)
   · adversarial-case-builder.memo.md · report.json · REPORT.md · .aris/traces/<skill>/<date>_run<NN>/

Overrides (pass after —): effort: balanced (default) | max (auditors fan the checklist into more fresh threads for breadth) · enrich: true (default; the ledger's additive semantic pass) | false · human checkpoint: false (default) | true (pause after Step 1 to confirm L, and before Step 5). Example: /anti-autoresearch ~/papers/submission — effort: max, human checkpoint: true.

⚠️ Shell state does not persist between Bash calls (cwd + env reset each call). Every block below re-derives ROOT and PAPER_DIR at its top and reads L / PAPER_ID from claims.json (the authoritative source after Step 1). Never rely on a variable set in an earlier block, and never cd into the paper dir. Keep PAPER_DIR space-free (simplest way to stay safe across the inline *.tex / *.findings.json globs).

Execution modes & the Reviewer Calling Convention

Pick the mode automatically — both produce the identical file set, and Step 4 (the verdict) is byte-deterministic given those files + the ledger.

• Delegation (default — if the Skill tool is available): invoke /evidence-ledger, the six auditors, and the two advisory memos (/adversarial-case-builder + /novelty-duplication-advisory). Each sub-skill runs its own deterministic tools + its exact codex reviewer prompt + its own anchor validation, and writes its <skill>.findings.json. The orchestrator validates the emitted files and adjudicates. This mirrors ARIS /research-pipeline delegating all GPT review to /auto-review-loop rather than prompting GPT itself.
• Inline (fallback — only shell + Read/Write + codex MCP): run the deterministic tools yourself with the exact commands below; for each cross-model dimension open a fresh mcp__codex__codex thread, send the verbatim reviewer-prompt block — the fenced prompt: / checklist inside that sub-skill's ## Step … — Cross-model … section in skills/<dim>/SKILL.md (Read it — the single source of truth; fill its [...] inputs from claims.json), save the raw reply, then run the shared anchor gate (Step 2) to produce the validated <dim>.findings.json.

The orchestrator's job is to guarantee the envelope and the independence invariants every reviewer call obeys:

• Envelope (every auditor instantiates this exact shape):

  mcp__codex__codex:
    model: gpt-5.5
    config: {"model_reasoning_effort": "xhigh"}
    sandbox: read-only
    cwd: <absolute PAPER_DIR>          # so the reviewer reads claims.json + sources directly
    prompt: |
      <the auditor's per-dimension checklist + the SIX HARD RULES below>

• The executor passes only structured inputs — paths + the ledger (claims.json) + the per-dimension checklist + the run level L. It never summarizes the paper, pre-judges, leaks a hunch, or whispers "this is probably AI-generated" (references/reviewer-independence.md Layer 1; the tool is agnostic to authorship).
• Six hard rules the orchestrator requires in every auditor's prompt (a finding that breaks one is structurally worthless): (1) ANCHOR — every above-info finding carries ≥1 evidence{claim_id, span} where claim_id exists in claims.json and span is a verbatim, whitespace-normalized substring of that claim's text_span (span in claim, never claim in span); (2) DISCREPANCY, NOT ACCUSATION — describe what to check/ask, never "reject"/"fabricated"; (3) OBSERVABILITY — set observability_level_required to the lowest tier at which the discrepancy is decidable (code/result confirmation ⇒ 2); (4) HONEST FP RISK — set false_positive_risk truthfully (legit "best config", labeled pilots, rounding, deterministic metrics are common FPs); (5) HAND OFF EXTERNAL CLAIMS — "first / SOTA" the text cannot settle ⇒ verdict_local: needs_external_check + requires_external_check: true; (6) pattern_id ∈ taxonomy v0.4 only.
• Reviewer ≠ adjudicator. The reviewer proposes findings; only tools/adjudicate_findings.py decides the verdict (Layer 2). The model is demoted from judge to evidence-extractor.
• Fresh thread per dimension, serial. A new mcp__codex__codex call per auditor (and per cited key inside citation-forensics); never codex-reply carrying one dimension's conclusions into another (the bias guard). Keep the calls serial — concurrent codex threads can hang; fan-out buys breadth of dimensions, not parallelism. On a stall, re-invoke the identical prompt in a fresh thread (never codex-reply); if it still fails, write [] and continue — a dead reviewer must never become a fabricated finding.

Re-entrancy: resuming a partial / re-run sweep

The pipeline is a pure function of PAPER_DIR's contents — every step writes a predictable file, so a crashed or repeated run resumes by probing what already exists. There is no run-state file and no "accepted vs done" gate, because Step 4 recomputes the verdict deterministically from whatever findings are present every time. Probe completeness and staleness before redoing work:

PAPER_DIR="<from Step 0>"
python3 - "$PAPER_DIR" <<'PY'
import json, os, glob, sys
D = sys.argv[1]
def is_array(p):
    try: return isinstance(json.load(open(p, encoding="utf-8")), list)
    except Exception: return False
def is_ledger(p):
    try:
        d = json.load(open(p, encoding="utf-8")); return isinstance(d, dict) and "claims" in d
    except Exception: return False
def newest_source(d):
    s = []
    for ext in ("*.tex", "*.txt", "*.bib"):
        s += glob.glob(os.path.join(d, ext)) + glob.glob(os.path.join(d, "**", ext), recursive=True)
    return max((os.path.getmtime(p) for p in s), default=0.0)
led = os.path.join(D, "claims.json"); have = is_ledger(led)
stale = have and os.path.getmtime(led) < newest_source(D)
print(f"STEP1 ledger      : {'present' if have else 'MISSING'}{'  ⚠ STALE → rebuild (sources changed)' if stale else ''}")
for f in ("consistency-audit.deterministic", "consistency-audit", "citation-forensics",
          "baseline-comparison-audit", "experiment-forensics",
          "presentation-signals.deterministic", "presentation-signals",
          "proof-derivation-forensics", "eval-design-forensics",
          "ai-style-impressions.deterministic", "ai-style-impressions"):
    p = os.path.join(D, f + ".findings.json")
    print(f"STEP2 {f:<32}: {'ok' if (os.path.isfile(p) and is_array(p)) else 'todo'}")
print(f"STEP3 adversarial memo            : {'present' if os.path.isfile(os.path.join(D,'adversarial-case-builder.memo.md')) else 'todo'}")
print(f"STEP3 novelty advisory memo       : {'present' if os.path.isfile(os.path.join(D,'novelty-duplication-advisory.memo.md')) else 'todo'}")
print(f"STEP4 report.json : {'present' if os.path.isfile(os.path.join(D,'report.json')) else 'todo'}")
PY

Rule: a stale ledger forces a full rebuild (Step 1 → re-fan Step 2 → re-adjudicate) — stale findings anchored to an old ledger are worse than none. If the ledger is fresh, skip Step 1 and only re-run the auditors marked todo. Always re-run Step 4 (cheap, deterministic, the only verdict source). Re-running from scratch is always safe — same inputs → same outputs.

Step 0 — Ingest: resolve the input to a working dir

Turn $ARGUMENTS (a directory, a PDF, or an arXiv id) into a single working directory (PAPER_DIR) that contains the best source the level allows. Prefer LaTeX (stable file:line spans → L1) over PDF text (best-effort → L0); leave any code/ + results/ in place so the manifest can derive L2.

ROOT="${CLAUDE_PLUGIN_ROOT}"
test -f "$ROOT/tools/adjudicate_findings.py" || { echo "FATAL: not inside the Anti-Autoresearch checkout (point ROOT at it)."; exit 1; }
ARG="$ARGUMENTS"

if [ -d "$ARG" ]; then                       # ---- DIRECTORY ----
  PAPER_DIR="$(cd "$ARG" && pwd)"
  # if only a PDF is present, extract text so the ledger has an L0 source
  if ! ls "$PAPER_DIR"/*.tex >/dev/null 2>&1 && ! ls "$PAPER_DIR"/*.txt >/dev/null 2>&1; then
    P=$(ls "$PAPER_DIR"/*.pdf 2>/dev/null | head -1)
    [ -n "$P" ] && pdftotext -layout "$P" "$PAPER_DIR/paper.txt"
  fi

elif [ -f "$ARG" ] && case "$ARG" in *.pdf) true;; *) false;; esac; then   # ---- PDF FILE ----
  PAPER_DIR="$(cd "$(dirname "$ARG")" && pwd)"
  pdftotext -layout "$ARG" "$PAPER_DIR/paper.txt" \
    || echo "WARN: pdftotext failed/missing — try: mutool draw -F txt, or pip install pdfminer.six (pdf2txt.py)."

else                                          # ---- ARXIV ID (e.g. 2401.01234) ----
  ID=$(printf '%s' "$ARG" | grep -oE '[0-9]{4}\.[0-9]{4,5}(v[0-9]+)?' | head -1)
  [ -n "$ID" ] || { echo "FATAL: '$ARG' is not a dir, a .pdf, or an arXiv id."; exit 1; }
  PAPER_DIR="$(pwd)/aar-$ID"; mkdir -p "$PAPER_DIR"
  # LaTeX source first (best spans → L1). e-print may be a tarball OR a single gzipped .tex.
  if curl -fsSL "https://arxiv.org/e-print/$ID" -o "$PAPER_DIR/src.tgz"; then
    tar -xzf "$PAPER_DIR/src.tgz" -C "$PAPER_DIR" 2>/dev/null \
      || gunzip -c "$PAPER_DIR/src.tgz" > "$PAPER_DIR/main.tex" 2>/dev/null
  fi
  if ! ls "$PAPER_DIR"/*.tex >/dev/null 2>&1; then     # fallback: PDF → text (L0)
    curl -fsSL "https://arxiv.org/pdf/$ID.pdf" -o "$PAPER_DIR/paper.pdf" \
      && pdftotext -layout "$PAPER_DIR/paper.pdf" "$PAPER_DIR/paper.txt"
  fi
fi

echo "PAPER_DIR = $PAPER_DIR"
ls -1 "$PAPER_DIR"/*.tex "$PAPER_DIR"/*.bib "$PAPER_DIR"/*.txt "$PAPER_DIR"/*.pdf 2>/dev/null
ls -d  "$PAPER_DIR"/code "$PAPER_DIR"/src "$PAPER_DIR"/results "$PAPER_DIR"/outputs 2>/dev/null   # L2 candidates

Validation gate. PAPER_DIR must now contain anchorable text — at least one *.tex or *.txt. A bare *.pdf is not enough: the ledger builders accept only --latex / --pdf-text, so Step 0 must have extracted paper.txt. If extraction failed or no source is present, stop and report exactly what was searched — there is nothing to anchor spans against, so there can be no ledger (see Failure handling below).

Failure handling.

• No pdftotext → fall back to mutool draw -F txt in.pdf out.txt or pip install pdfminer.six && pdf2txt.py in.pdf > out.txt. If text extraction is impossible, you cannot build an L0 ledger from a PDF — ask the user for the LaTeX.
• arXiv unreachable (network gated / proxy needed) → say so and ask the user to drop the source or PDF into a local dir, then re-invoke with that paper-dir. Never fabricate a ledger from an absent paper. (A heartbeat may wait on this download — a pre-verdict external step — but only the sweep, run once the source lands, produces the verdict.)
• arXiv .tex extracted but no obvious main.tex → fine; /evidence-ledger globs *.tex. (PAPER_ID is set authoritatively by the ledger in Step 1 — do not hand-pick it here.)

Step 1 — Build the evidence ledger (always first; the spine)

Delegate to /evidence-ledger, which runs the real deterministic tools (tools/build_manifest.py → artifact_manifest.json + observability level L; tools/build_claim_ledger.py → claims.json) and, by default, one additive span-anchored semantic-enrichment pass. Everything downstream reads these two files.

/evidence-ledger "<PAPER_DIR>"            # add "— enrich: false" to ship the deterministic backbone alone

Then read L and PAPER_ID back from the ledger — it is the source of truth for every later block (re-derive, never persist):

PAPER_DIR="<from Step 0>"
test -f "$PAPER_DIR/claims.json" || { echo "FATAL: /evidence-ledger did not produce claims.json — re-run Step 1."; exit 1; }
L=$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["observability_level"])' "$PAPER_DIR/claims.json")
PAPER_ID=$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["paper_id"])' "$PAPER_DIR/claims.json")
python3 - "$PAPER_DIR/claims.json" "$PAPER_DIR/artifact_manifest.json" <<'PY'
import json, sys, collections
L = json.load(open(sys.argv[1], encoding="utf-8")); M = json.load(open(sys.argv[2], encoding="utf-8"))
assert L["observability_level"] == M["observability_level"], "ledger level != manifest level"
t = collections.Counter(c["type"] for c in L["claims"])
print(f"PAPER_ID={L['paper_id']}  L={L['observability_level']}  claims={len(L['claims'])}  types={dict(t)}")
PY

Validation gate.

• claims.json exists and claims is non-empty. Zero claims on a paper that visibly has numbers/citations means the ingest source was wrong (e.g. a PDF with no usable text, or a dir with no .tex/.txt) → fix Step 0 and re-run /evidence-ledger.
• The ledger's observability_level matches the artifacts present (a PDF-only run MUST be L=0; never hand a higher level to anything because a repo "exists somewhere else"). The level is the honesty contract for the whole run — carry this L forward; it caps every finding's severity in Step 4.
• — human checkpoint: true → present L, the claim-type histogram, and the source list, and pause for confirmation before fanning out.

Failure handling. If /evidence-ledger is unavailable, build the ledger directly with the real tools (exact flags — confirm via --help):

ROOT="${CLAUDE_PLUGIN_ROOT}"; PAPER_DIR="<from Step 0>"
SLUG=$(basename "$PAPER_DIR" | tr -cs 'A-Za-z0-9.' '-')
TXT=(); [ -f "$PAPER_DIR/paper.txt" ] && TXT=(--pdf-text "$PAPER_DIR/paper.txt")
python3 "$ROOT/tools/build_manifest.py" --paper-id "$SLUG" --dir "$PAPER_DIR" "${TXT[@]}" --out "$PAPER_DIR/artifact_manifest.json"
L=$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["observability_level"])' "$PAPER_DIR/artifact_manifest.json")
if ls "$PAPER_DIR"/*.tex >/dev/null 2>&1; then
  python3 "$ROOT/tools/build_claim_ledger.py" --paper-id "$SLUG" --latex "$PAPER_DIR"/*.tex --observability-level "$L" --out "$PAPER_DIR/claims.json"
else
  python3 "$ROOT/tools/build_claim_ledger.py" --paper-id "$SLUG" --pdf-text "$PAPER_DIR/paper.txt" --observability-level "$L" --out "$PAPER_DIR/claims.json"
fi

Step 2 — Fan out the six auditors (breadth; the verdict is NOT decided here)

Decide which auditors apply from the ledger's claim types, then run each applicable skill. Each reads claims.json, opens a fresh codex thread per dimension (a different model family from the executor), proposes span-anchored findings, validates its own spans, and writes <skill>.findings.json (the deterministic auditors also write <skill>.deterministic.findings.json). The orchestrator only sequences these calls and enforces the Reviewer Calling Convention above — it authors no finding.

PAPER_DIR="<from Step 0>"
python3 - "$PAPER_DIR/claims.json" <<'PY'
import json, re, sys, os
d = json.load(open(sys.argv[1], encoding="utf-8")); cl = d.get("claims", [])
paper_dir = os.path.dirname(os.path.abspath(sys.argv[1])) or "."
CMP = re.compile(r"state[- ]of[- ]the[- ]art|\bSOTA\b|outperform\w*|\bbest\b|surpass\w*|"
                 r"beats?|superior|first to|compared? (?:to|with)|baseline|prior (?:work|art)", re.I)
has_cite = any(c.get("type") == "citation" for c in cl)
has_cmp  = any(c.get("type") in ("scope", "comparison", "baseline", "caption") and CMP.search(c.get("text_span","")) for c in cl)
# proofs: scan the SOURCE for theorem/proof/derivation markers (mirrors /proof-derivation-forensics's
# own HAS_PROOFS gate). The skill self-guards (writes [] if it finds none), so this gate is a
# budget hint, not a correctness gate — running proof-derivation-forensics unconditionally is safe.
TH  = re.compile(r"\\begin\{(theorem|lemma|proposition|corollary|claim|conjecture|"
                 r"proof|definition|assumption)\*?\}", re.I)
EQ  = re.compile(r"\\begin\{(equation|align|gather|multline|eqnarray)\*?\}|\\\[", re.I)
TXT = re.compile(r"\b(Theorem|Lemma|Proposition|Corollary|Proof|Q\.?E\.?D\.?)\b")
ph = 0
for s in d.get("source_files", []):
    sp = s.get("path", ""); kind = s.get("kind", "")
    cand = sp if os.path.isabs(sp) else os.path.join(paper_dir, sp)
    if not os.path.isfile(cand): cand = sp
    try: t = open(cand, encoding="utf-8", errors="replace").read()
    except OSError: continue
    ph += (len(TH.findall(t)) + len(EQ.findall(t))) if kind == "latex" else len(TXT.findall(t))
if ph == 0:                                            # L0 fallback: theorem/proof words inside ledger spans
    ph = sum(1 for c in cl if TXT.search(c.get("text_span", "") or ""))
has_proof = ph > 0
# contribution claims (Step 3 novelty advisory anchor universe): scope/method/comparison OR abstract/intro
CONTRIB_SECT = {"abstract", "intro", "introduction"}
has_contrib = any((c.get("type") in ("scope", "method", "comparison")
                   or ((c.get("location") or {}).get("section", "") or "").lower() in CONTRIB_SECT)
                  and c.get("claim_id") and c.get("text_span") for c in cl)
print("RUN (always): /consistency-audit  /experiment-forensics  /presentation-signals  /ai-style-impressions")
print(f"RUN /citation-forensics            : {has_cite}   (≥1 citation claim)")
print(f"RUN /baseline-comparison-audit     : {has_cmp}    (≥1 comparison/SOTA scope claim)")
print(f"RUN /proof-derivation-forensics    : {has_proof}   (≥1 theorem/proof/derivation marker; self-guards → [] if none)")
print(f"RUN /eval-design-forensics         : {has_cmp}    (≥1 comparison/eval claim · leakage / judge-validity / selective-reporting; self-guards → [] if no eval protocol)")
print(f"RUN /novelty-duplication-advisory  : {has_contrib}   (Step 3 memo · ≥1 contribution claim · MEMO-ONLY)")
PY

Run the applicable skills (pass PAPER_DIR so each locates the ledger). Keep the reviewer calls serial. Each row's Writes column is the exact filename the Step-4 glob expects; the Owns column is the taxonomy-v0.4 patterns that skill may emit:

Notes the orchestrator must honor:

• experiment-forensics always runs, but at L0/L1 it emits only info-level "could-not-verify" signals (observability_level_required: 2) — never a fraud flag from a PDF. Its full line-by-line code audit only fires at L2; the eval file:line is description detail, the anchor is the paper claim.
• presentation-signals is auxiliary: every finding is false_positive_risk: high and the adjudicator caps it at minor (by SURFACE_ONLY_SKILLS AND by SURFACE_PATTERNS pattern_id), so the surface dimension can contribute at most SOFT_FLAGS, never HARD_FLAGS. It is not an AI-text classifier (references/hack-pattern-taxonomy.md §F). Most papers ⇒ [].
• proof-derivation-forensics is verdict-bearing (dimension: proof), but unlike the L2 code dimensions it decides from the LaTeX proof source (L1) — a span-anchored critical family-G flaw (a circular proof; an invalid step / smuggled assumption / inverted symbol the headline theorem depends on) can reach HARD_FLAGS with no repo or results, but needs the L1 source: PDF-extracted math is unreliable, so at an L0 (PDF-only) run family-G findings surface as info only. There is no deterministic companion (proof validity is semantic), so it writes the single proof-derivation-forensics.findings.json. It self-guards: if the paper has no theorem/proof/derivation markers (HAS_PROOFS=no) it writes [] and never calls the reviewer — so running it unconditionally is safe; the decision block only gates it for budget. It emits only the five family-G patterns; abstract-vs-theorem scope drift stays with consistency-audit (HP-THEOREM-SCOPE-DRIFT), not here (references/hack-pattern-taxonomy.md §G).
• A skill that finds nothing — or an optional auditor that does not apply — still writes [] to its predictable path. Silent skip is forbidden (so the run records that the dimension ran and was clean, not silently absent): echo '[]' > "$PAPER_DIR/citation-forensics.findings.json".
• No double-counting. Each auditor writes its own filename, so the *.findings.json glob enumerates each file exactly once; the deterministic and semantic passes stay in separate files. (Finding-id prefixes: NUM###/HL### numeric, PRES### presentation, EF### experiment, F### other semantic. Ids may repeat across semantic files; that is cosmetic — the report keys on skill + file, not id.)

Inline mode only — the shared anchor gate. When delegation is unavailable, run each applicable dimension's codex call yourself (envelope + verbatim sub-skill prompt above; save the raw reply to $PAPER_DIR/.aris/<dim>.response.md), then convert that raw reply into a validated <dim>.findings.json with this gate. It enforces invariant #3 exactly as tools/adjudicate_findings.py re-binds it (span in claim, never claim in span). SURFACE=1 only for presentation-signals:

ROOT="${CLAUDE_PLUGIN_ROOT}"; PAPER_DIR="<from Step 0>"
LEDGER="$PAPER_DIR/claims.json"; SKILL="<dimension>"; SURFACE="<0 or 1>"
mkdir -p "$PAPER_DIR/.aris"   # the fresh-thread raw reply is saved here before this gate runs
RAW="$PAPER_DIR/.aris/$SKILL.response.md"; OUT="$PAPER_DIR/$SKILL.findings.json"
python3 - "$LEDGER" "$RAW" "$OUT" "$SKILL" "$SURFACE" <<'PY'
import json, re, sys
ledger_p, raw_p, out_p, skill, surface = sys.argv[1:6]
surface = surface == "1"
nw = lambda s: " ".join((s or "").split())
SEV={"critical","major","minor","info"}; VL={"fail","warn","clean","needs_external_check"}
FPR={"low","medium","high"}; ABOVE={"critical","major","minor"}
claims = {c["claim_id"]: c for c in json.load(open(ledger_p, encoding="utf-8")).get("claims", []) if c.get("claim_id")}
raw = open(raw_p, encoding="utf-8").read(); m = re.search(r"\[.*\]", raw, re.S)
prop = json.loads(m.group(0) if m else raw)
if isinstance(prop, dict): prop = prop.get("findings", [])
kept = []; n = 0; demoted = 0; capped = 0
for f in prop:
    if not isinstance(f, dict): continue
    n += 1; f["finding_id"] = f"F{n:03d}"; f["skill"] = skill
    if f.get("severity") not in SEV: f["severity"] = "info"
    if f.get("verdict_local") not in VL: f["verdict_local"] = "warn"
    if f.get("false_positive_risk") not in FPR: f["false_positive_risk"] = "high" if surface else "medium"
    if f["verdict_local"] == "needs_external_check": f["requires_external_check"] = True
    if surface:                                   # surface signals: forced high-FP, capped at minor, L0-decidable
        f["false_positive_risk"] = "high"; f["observability_level_required"] = 0
        if f["severity"] in ("critical", "major"): f["severity"] = "minor"; capped += 1
    anchored = []
    for ev in (f.get("evidence") or []):
        cid = ev.get("claim_id"); span = nw(ev.get("span", "")); c = claims.get(cid)
        if c and span and span in nw(c.get("text_span", "")):   # span IN claim, NOT claim IN span
            ev.setdefault("location", c.get("location", {}))
            ev.setdefault("artifact_hash", c.get("evidence_anchor", ""))
            anchored.append(ev)
    f["evidence"] = anchored
    if f["severity"] in ABOVE and not anchored: f["severity"] = "info"; demoted += 1
    # observability_level_required is passed through verbatim — a missing/invalid one is
    # left as-is so the adjudicator's OBSERVABILITY gate fail-closes it to info.
    f["reviewer"] = {"model": "gpt-5.5", "reasoning": "xhigh", "deterministic": False}
    kept.append(f)
json.dump(kept, open(out_p, "w", encoding="utf-8"), indent=2, ensure_ascii=False)
print(f"{skill}: validated {len(kept)} ({demoted} ->info unanchored, {capped} surface-capped) -> {out_p}")
PY

For dimension-specific extras (citation must anchor to a type:"citation" claim; baseline pattern-ownership + cross-row delta dedup; presentation surface allow-list), prefer the sub-skill's own Validate + anchor step — it is a strict superset of this gate. The deterministic passes have no LLM step — run their tools directly:

ROOT="${CLAUDE_PLUGIN_ROOT}"; PAPER_DIR="<from Step 0>"
python3 "$ROOT/tools/check_numeric_consistency.py" --ledger "$PAPER_DIR/claims.json" \
    --out "$PAPER_DIR/consistency-audit.deterministic.findings.json"
python3 "$ROOT/tools/check_presentation.py" --ledger "$PAPER_DIR/claims.json" \
    --out "$PAPER_DIR/presentation-signals.deterministic.findings.json"

Validation gate — files exist + parse, and every above-info finding is anchored. This independently re-checks invariant #3 (it mirrors the adjudicator; it is report-only here — the adjudicator is the binding gate and fail-closes anything unanchored to info):

PAPER_DIR="<from Step 0>"
python3 - "$PAPER_DIR" <<'PY'
import json, glob, os, sys
D = sys.argv[1]
claims = {c["claim_id"]: " ".join((c.get("text_span") or "").split())
          for c in json.load(open(f"{D}/claims.json", encoding="utf-8"))["claims"] if c.get("claim_id")}
nw = lambda s: " ".join((s or "").split()); ABOVE = {"critical", "major", "minor"}
mandatory = ["consistency-audit.deterministic", "consistency-audit", "experiment-forensics",
             "presentation-signals.deterministic", "presentation-signals",
             "ai-style-impressions.deterministic", "ai-style-impressions"]
missing = []
for f in mandatory:
    p = f"{D}/{f}.findings.json"; ok = os.path.isfile(p)
    if ok:
        try: ok = isinstance(json.load(open(p, encoding="utf-8")), list)
        except Exception: ok = False
    if not ok: missing.append(f)
total_bad = 0
for p in sorted(glob.glob(f"{D}/*.findings.json")):
    if p.endswith(".proposed.findings.json"): continue   # defensive: never adjudicate a raw/intermediate file
    try: arr = json.load(open(p, encoding="utf-8"))
    except Exception: print(f"BAD JSON  {os.path.basename(p)}"); continue
    bad = sum(1 for f in arr if f.get("severity") in ABOVE and not any(
        ev.get("claim_id") in claims and nw(ev.get("span")) and nw(ev["span"]) in claims[ev["claim_id"]]
        for ev in (f.get("evidence") or [])))
    total_bad += bad
    print(f"{os.path.basename(p):<46} findings={len(arr):<3} above-info-unanchored={bad}")
if missing: print("MISSING/BAD mandatory:", ", ".join(missing), "-> re-run those auditors")
print(f"TOTAL unanchored above-info (adjudicator will demote to info): {total_bad}")
PY

Failure handling. A missing mandatory file → re-invoke that skill. A codex stall inside a sub-skill → it re-invokes the identical prompt in a fresh thread (never codex-reply); if it still fails it writes [] and the run continues. Unanchored above-info findings are not a stop condition (the adjudicator demotes them); a large count signals a thin ledger/source — note it in the report's limitations. Any pattern_id present MUST be in references/hack-pattern-taxonomy.md (v0.4); never patch findings by hand.

Step 3 — Advisory memos (last; memo-only, no verdict weight)

After the auditors (so the ledger + the merged findings exist), run the two advisory skills. Both are memo-only: the adjudicator lists each in MEMO_ONLY_SKILLS and its MEMO gate pins any finding either emits to info, so neither can move the verdict — they hand a human area chair context to weigh, nothing more.

3a — Adversarial case builder. Synthesize the single strongest evidence-bound objection:

/adversarial-case-builder "<PAPER_DIR>"          # → adversarial-case-builder.memo.md

Every point in the memo must cite an existing ledger claim_id or finding finding_id — no new uncited accusations. It is passed to the adjudicator via --memo and shown as informational with no verdict weight (the MEMO_ONLY_SKILLS cap pins any adversarial-case-builder finding to info, even if it also emits a findings.json). If the anchored evidence does not support a strong rejection, the honest memo says the paper survives.

3b — Novelty & duplication advisory (run if has_contrib from the Step-2 decision block — ≥1 contribution claim). This is the only skill that reaches outside the paper: it retrieves candidate prior work (DBLP fuzzy-title + boolean · WebSearch · WebFetch) from the paper's own title + contribution spans and lays the overlap side-by-side for the two advisory taxonomy signals — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合) and ADV-DUPLICATE-PUBLICATION (repackaged submission):

/novelty-duplication-advisory "<PAPER_DIR>"      # → novelty-duplication-advisory.memo.md (+ info-only findings mirror)

It never rules "trivial" or "duplicate" (a reviewer judgment, not decidable at any observability level), and absence of a retrieved match is NOT evidence of originality. Unlike 3a it does not flow through --memo (the adjudicator has a single memo slot): its human-facing novelty-duplication-advisory.memo.md is surfaced in Step 5, and it also writes an info-only novelty-duplication-advisory.findings.json mirror that the Step-4 glob picks up and the MEMO_ONLY_SKILLS gate caps at info (recorded, never verdict-bearing). With no contribution claim, or when retrieval surfaces nothing, it writes an honest-null memo + [] — "no candidate overlap found" still says nothing about novelty. It is the literature-facing complement to citation-forensics: that audits works the paper does cite; this surfaces prior work it may not have cited at all.

Both memos are non-blocking — if either is skipped, Step 4 still runs (an empty --memo for the adversarial case; an absent novelty mirror simply contributes nothing).

Step 4 — Adjudicate (deterministic — this is the verdict)

The single decider. Pass every auditor's *.findings.json (the glob now also enumerates the verdict-bearing proof-derivation-forensics.findings.json and the info-only novelty-duplication-advisory.findings.json mirror — the latter capped at info by the MEMO gate), the required ledger, the run level, the taxonomy version, and the adversarial memo. --ledger is what re-verifies each finding quotes a verbatim ledger span; argparse makes it mandatory — and without a ledger every above-info finding fails closed to info (a missing ledger silently neuters the audit), which is why it is non-optional here.

ROOT="${CLAUDE_PLUGIN_ROOT}"; PAPER_DIR="<from Step 0>"
L=$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["observability_level"])' "$PAPER_DIR/claims.json")
PAPER_ID=$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["paper_id"])' "$PAPER_DIR/claims.json")

# Build the findings list portably (no `mapfile` — macOS ships bash 3.2 without it).
# Defensive: never adjudicate a raw/intermediate *.proposed.findings.json, should a sub-skill ever stage one.
FINDINGS=()
for p in "$PAPER_DIR"/*.findings.json; do
  [ -f "$p" ] || continue
  case "$p" in *.proposed.findings.json) continue;; esac
  FINDINGS+=("$p")
done
[ ${#FINDINGS[@]} -gt 0 ] || { echo "FATAL: no findings files in $PAPER_DIR — did Step 2 run?"; exit 1; }
printf '  findings: %s\n' "${FINDINGS[@]##*/}"

python3 "$ROOT/tools/adjudicate_findings.py" \
    --findings "${FINDINGS[@]}" \
    --ledger "$PAPER_DIR/claims.json" \
    --paper-id "$PAPER_ID" --observability-level "$L" --taxonomy-version 0.5 \
    --memo "$(cat "$PAPER_DIR/adversarial-case-builder.memo.md" 2>/dev/null)" \
    --limitation "Run observability level L$L — see references/observability-levels.md for what this tier can and cannot decide." \
    --out "$PAPER_DIR/report.json" --md "$PAPER_DIR/REPORT.md"
# prints e.g.: verdict=SOFT_FLAGS crit=0 maj=0 min=1 -> .../report.json, .../REPORT.md

• --observability-level "$L" is the run level: it auto-demotes any finding whose observability_level_required exceeds L (e.g. an L2 code-fraud pattern on an L0 run → info, counted under downgraded_for_observability).
• The adjudicator auto-writes level-derived limitations at L0/L1 (and an anchoring note if --ledger anchoring ever fails); Step 4 also always passes an explicit --limitation (above), so the report's limitations is never empty on this path — honesty is part of the contract. Each extra --limitation (repeatable) is added alongside the auto-written ones. For a byte-reproducible eval run add --generated-at "<fixed ISO8601>"; omit it normally (a harmless utcnow DeprecationWarning may print to stderr; exit 0).

It applies, in order (each gate fail-closed and logged per finding): ANCHOR (above-info without a verbatim ledger span → info; counted under unanchored_demoted) → OBSERVABILITY (observability_level_required missing/invalid or > run L → info; counted under downgraded_for_observability) → FP-RISK (high caps at minor, medium caps at major) → MEMO (adversarial-case-builder + novelty-duplication-advisory → info) → SURFACE (presentation-signals skill OR a SURFACE_PATTERNS pattern_id → minor). Then, over the surviving severities:

any surviving critical          → HARD_FLAGS
else any surviving major/minor  → SOFT_FLAGS
else                            → CLEAN_GIVEN_EVIDENCE   (= "nothing checkable at L is broken", NOT "honest")

Validation gate. Confirm the report is well-formed:

PAPER_DIR="<from Step 0>"
python3 - "$PAPER_DIR/report.json" <<'PY'
import json, sys
r = json.load(open(sys.argv[1], encoding="utf-8"))
assert r["overall_verdict"] in {"CLEAN_GIVEN_EVIDENCE", "SOFT_FLAGS", "HARD_FLAGS"}, r["overall_verdict"]
assert r["adjudicator"] == "deterministic-rules-v0" and r["human_review_required"] is True
assert r["anchoring_verified"] is True, "ledger anchoring did not run — --ledger missing?"
assert r["limitations"], "limitations must always be populated (the honesty contract)"
c = r["counts"]
print(f"verdict={r['overall_verdict']}  L={r['observability_level']}  taxonomy=v{r['taxonomy_version']}")
print(f"counts: crit={c['critical']} maj={c['major']} min={c['minor']} info={c['info']} "
      f"obs-demoted={c['downgraded_for_observability']} unanchored-demoted={c.get('unanchored_demoted',0)}")
PY

The verdict is reproducible: same findings + same L → same verdict, with no model in the final decision.

Step 5 — Present

Show the human REPORT.md. Lead with the verdict + the observability level, then the evidence-first table, then detail, then the advisory memos (adversarial + novelty/ duplication), then — always — the limitations (what could not be checked at this level):

PAPER_DIR="<from Step 0>"; sed -n '1,60p' "$PAPER_DIR/REPORT.md"

Present in this shape (fill from report.json / REPORT.md):

🔬 Integrity Forensics — <PAPER_ID>
   Verdict: <CLEAN_GIVEN_EVIDENCE | SOFT_FLAGS | HARD_FLAGS>   Observability: L<L>   Taxonomy: v0.4
   Findings above info: critical <n> · major <n> · minor <n>   (demoted: obs <n>, unanchored <n>)
   Top flags: <ID> <severity> <pattern_id> — <one-line, where>
   Could NOT check at L<L>: <one line from limitations — e.g. "code/result-level patterns need L2">
   Advisory (no verdict weight): adversarial objection + novelty/duplication overlap — <PAPER_DIR>/novelty-duplication-advisory.memo.md
   Report: <PAPER_DIR>/REPORT.md   ·   Machine-readable: <PAPER_DIR>/report.json
   This is decision support for a human reviewer/AC — it flags discrepancies to investigate, not misconduct.

State plainly, in the user's words, what the level did and did not permit: e.g. "L0 run — I checked internal self-consistency, arithmetic, citation existence/context, and stated scope/baselines; I could NOT verify code/result-level integrity (fake GT, self-normalization, phantom results) — that needs the repo + result files (L2)." CLEAN_GIVEN_EVIDENCE means "nothing checkable at L is broken," NOT "the paper is honest." human_review_required is always true.

— human checkpoint: true → pause here for the user before treating the run as closed.

Deterministic-only fallback (no model in the loop)

When no cross-model reviewer is available (offline / no codex), you can still produce a real, reproducible report from the deterministic core alone — the load-bearing, eval-gated part of the repo (100% recall on the three deterministic patterns HP-DELTA-ERROR / HP-NUM-INFLATE / HP-DUP-TABLE, zero clean false-positives; README.md Status). All real tools, exact flags:

ROOT="${CLAUDE_PLUGIN_ROOT}"; PAPER_DIR="<from Step 0>"
TXT=(); [ -f "$PAPER_DIR/paper.txt" ] && TXT=(--pdf-text "$PAPER_DIR/paper.txt")
python3 "$ROOT/tools/build_manifest.py"      --paper-id mypaper --dir "$PAPER_DIR" "${TXT[@]}" --out "$PAPER_DIR/artifact_manifest.json"
L=$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["observability_level"])' "$PAPER_DIR/artifact_manifest.json")
if ls "$PAPER_DIR"/*.tex >/dev/null 2>&1; then            # L1/L2 source path
  python3 "$ROOT/tools/build_claim_ledger.py"  --paper-id mypaper --latex "$PAPER_DIR"/*.tex \
      --observability-level "$L" --out "$PAPER_DIR/claims.json"
else                                                       # L0 text path (no *.tex)
  python3 "$ROOT/tools/build_claim_ledger.py"  --paper-id mypaper --pdf-text "$PAPER_DIR/paper.txt" \
      --observability-level "$L" --out "$PAPER_DIR/claims.json"
fi
python3 "$ROOT/tools/check_numeric_consistency.py" --ledger "$PAPER_DIR/claims.json" \
    --out "$PAPER_DIR/consistency-audit.deterministic.findings.json"
python3 "$ROOT/tools/check_presentation.py"        --ledger "$PAPER_DIR/claims.json" \
    --out "$PAPER_DIR/presentation-signals.deterministic.findings.json"
python3 "$ROOT/tools/adjudicate_findings.py" \
    --findings "$PAPER_DIR"/*.deterministic.findings.json \
    --ledger "$PAPER_DIR/claims.json" --paper-id mypaper --observability-level "$L" \
    --taxonomy-version 0.5 \
    --limitation "Deterministic-only run (no cross-model reviewer): semantic + code-level dimensions were NOT run." \
    --out "$PAPER_DIR/report.json" --md "$PAPER_DIR/REPORT.md"

The verdict reflects only the deterministic patterns; the report's limitations must say the semantic / code-level dimensions were not run. Run python3 "$ROOT/eval/run_eval.py" (and python3 "$ROOT/tests/test_adjudicator.py") any time to prove this core still catches the bundled injected defects and stays clean on the clean fixture (the CI gate).

Output contract

A completed run leaves, in PAPER_DIR (we never edit the paper itself):

• artifact_manifest.json (schemas/artifact_manifest.schema.json) — observable inputs (hashed) + the derived observability level.
• claims.json (schemas/claims.schema.json) — the span-anchored, hashed evidence ledger every auditor read.
• <skill>.findings.json for each auditor that ran, plus *.deterministic.findings.json for the consistency and presentation deterministic passes. Each conforms to schemas/finding.schema.json; [] is a valid, honest result, and every optional auditor's file is present even when empty.
• adversarial-case-builder.memo.md — the evidence-bound objection memo (informational).
• novelty-duplication-advisory.memo.md — the prior-work overlap advisory (informational, memo-only), present when the novelty advisory ran; alongside it the retrieval records novelty-duplication-advisory.profile.json / .candidates.json and the info-only novelty-duplication-advisory.findings.json mirror (capped at info by the MEMO gate).
• report.json (schemas/report.schema.json) + REPORT.md — the only files carrying overall_verdict, produced solely by tools/adjudicate_findings.py (adjudicator: deterministic-rules-v0, human_review_required: true).
• .aris/traces/<skill>/<date>_run<NN>/ — each sub-skill's raw reviewer calls (forensic), plus this orchestrator's own run trace (see Review tracing).

The orchestrator itself never computes a verdict, never edits the audited paper, and never hand-authors a finding — it ingests, sequences the skills, runs the deterministic adjudicator, and presents.

Key rules

• Ledger first, always; auditors propose, only adjudicate_findings.py decides. No auditor runs without claims.json, and no model is in the final decision; the verdict is reproducible (references/reviewer-independence.md Layer 2; DESIGN.md §3).
• No span → no severity. Every above-info finding must cite a ledger claim_id and quote a verbatim, whitespace-normalized substring of that claim (span in claim, never claim in span). The orchestrator's anchor sweep checks this; the adjudicator binds it (fail-closed to info). --ledger is therefore mandatory in Step 4.
• Observability honesty. Carry the ledger's L everywhere; it caps severity. L0/L1 cannot assert code/result-level fraud — those auto-demote to info on a sub-L2 run. Never present an L0 run as if it could see code; the report's limitations say what was unverifiable (references/observability-levels.md; DESIGN.md §4).
• Cross-model, fresh thread per dimension. Reviewer = gpt-5.5 xhigh (a different family from Claude); each auditor uses a new mcp__codex__codex thread and never codex-reply (deliberately absent from allowed-tools — the bias guard). The executor passes only paths + the ledger + the checklist, never a summary or a hunch.
• Detect-only. No step edits the audited paper or repo (no Edit granted; reviewer sandbox is read-only). Output describes a discrepancy to check/ask, never "reject" or "the authors faked X".
• Surface signals stay weak. presentation-signals is auxiliary, capped at minor (by skill and by pattern_id), default false_positive_risk: high. This repo is not an AI-text classifier — for authorship use a dedicated detector.
• Proof decides from the source; novelty never decides. proof-derivation-forensics is verdict-bearing (dimension: proof) and decides from the LaTeX proof source (L1) — a span-anchored critical family-G flaw reaches HARD_FLAGS with no repo/results, but needs the L1 source (PDF-extracted math is unreliable; an L0 PDF-only run → info); it emits only the five family-G patterns and self-guards to [] when no proof is present. novelty-duplication-advisory is memo-only (MEMO_ONLY_SKILLS, capped at info): it retrieves and lays out prior-work overlap but never rules trivial/duplicate, and absence of a retrieved match is not evidence of originality (the only auditor that consults an external corpus, which is exactly why it carries no verdict weight).
• pattern_id ∈ taxonomy v0.4 only; the taxonomy is a post-hoc mapping layer, not the detector (references/hack-pattern-taxonomy.md).
• The adjudicator ingests validated findings only. The *.findings.json glob feeds the adjudicator; defensively exclude any raw/intermediate *.proposed.findings.json a sub-skill might stage, so an unvalidated array can never reach the verdict.
• Silent skip is forbidden. A non-applicable / clean auditor still writes [].
• Large file handling. If Write fails on size, retry with a Bash heredoc (cat << 'EOF' > file) — do not ask permission, just do it. This is only ever for our own output files in PAPER_DIR; detect-only still holds (never write/edit the audited sources).
• Reproducible. Same artifacts → same ledger → same findings → same verdict; re-run only when the inputs change (the cadence fence at the top).

When NOT to use (and routing to standalone skills)

• You only want one dimension → call the auditor directly (/consistency-audit, /citation-forensics, /baseline-comparison-audit, /experiment-forensics, /presentation-signals, /proof-derivation-forensics); each can adjudicate itself with --ledger when run alone.
• You only want the prior-work overlap (trivial-combination / duplicate-publication signals) → /novelty-duplication-advisory (memo-only; it surfaces overlap but never rules novelty, and the absence of a match is not evidence of originality).
• You only need the ledger (extract claims, no verdict) → /evidence-ledger.
• You want an AI-text / "looks machine-written" verdict → out of scope by design; this tool audits integrity under limited evidence, not authorship (DESIGN.md §1).
• You want the paper auto-fixed → out of scope; this is a third-party forensics tool, not a co-author. (ARIS citation-audit etc. are the co-author-mode tools.)
• You want reproduction (re-run the code, L3) → out of scope in v0; we never claim reproduction. L2 verifies paper-number ↔ result-file match, not a re-run.
• On a timer → never /loop / /schedule / CronCreate the verdict; re-run only when the paper / repo / ledger changes (see the cadence fence at the top).

Typical run profile

Forensics is fast — the budget is reviewer calls, not GPU. Use this to set expectations and to choose — effort: max (more fresh threads per dimension) vs the deterministic-only fallback (zero reviewer calls).

A heartbeat may wait on the only external steps (Stage 0 download, Stage 2 citation web lookups, Stage 3 novelty prior-work retrieval) — it may never re-fire Stage 4 or "decide the paper is fine."

Review tracing

Each auditor saves its own raw reviewer calls under .aris/traces/<skill>/<date>_run<NN>/ (forensic policy — never silently dropped: run.meta.json + per-call request.json / response.md / meta.json, where request.json shows the executor sent only paths + the ledger + the checklist — the reviewer-independence audit trail). The orchestrator additionally writes a top-level run trace so the whole sweep is reproducible:

PAPER_DIR="<from Step 0>"; ARG="<original input arg, from Step 0>"; DATE=$(date +%Y-%m-%d); N=1
while [ -d "$PAPER_DIR/.aris/traces/anti-autoresearch/${DATE}_run$(printf %02d $N)" ]; do N=$((N+1)); done
RUNDIR="$PAPER_DIR/.aris/traces/anti-autoresearch/${DATE}_run$(printf %02d $N)"; mkdir -p "$RUNDIR"
python3 - "$PAPER_DIR" "$RUNDIR" "$ARG" <<'PY'
import json, glob, os, sys, hashlib, datetime
D, RUN, ARG = sys.argv[1], sys.argv[2], sys.argv[3]
def sha(p):
    try: return hashlib.sha256(open(p, "rb").read()).hexdigest()
    except Exception: return None
rep = json.load(open(f"{D}/report.json", encoding="utf-8")) if os.path.exists(f"{D}/report.json") else {}
meta = {
    "skill": "anti-autoresearch", "input_arg": ARG, "paper_dir": D,
    "paper_id": rep.get("paper_id"), "observability_level": rep.get("observability_level"),
    "ledger_sha256": sha(f"{D}/claims.json"),
    "findings_files": [os.path.basename(p) for p in sorted(glob.glob(f"{D}/*.findings.json"))
                       if not p.endswith(".proposed.findings.json")],
    "overall_verdict": rep.get("overall_verdict"), "counts": rep.get("counts"),
    "adjudicator": rep.get("adjudicator"), "taxonomy_version": rep.get("taxonomy_version"),
    "generated_at": datetime.datetime.utcnow().isoformat() + "Z",
}
json.dump(meta, open(f"{RUN}/run.meta.json", "w", encoding="utf-8"), indent=2, ensure_ascii=False)
print("wrote", f"{RUN}/run.meta.json", "->", meta["overall_verdict"])
PY

Traces are the reproducibility + independence audit trail: they prove the executor sent the reviewer only structured inputs and that the verdict came from the deterministic adjudicator, not a model.