Imported from wanshuiyin/Anti-Autoresearch (skills/evidence-ledger/SKILL.md). Install upstream with npx skills add wanshuiyin/Anti-Autoresearch --skill evidence-ledger. Copyright stays with the author.

Evidence Ledger — the spine every auditor reads

Infrastructure skill, not an auditor. It produces the only structure the auditor skills are allowed to reason over, so they don't each re-read the PDF and hallucinate a different table and a different list of numbers. It emits no findings and no verdict — only artifact_manifest.json + claims.json. See references/integrity-forensics-contract.md §"The pipeline" (stages [1]–[2]).

Build the ledger for: $ARGUMENTS

🔁 Not verdict-bearing — but not a polling skill either. The deterministic backbone (Steps 1–2) is a pure function of the hashed sources: same source bytes → byte-identical claims.json. Re-run it only when the sources change, never on a wall-clock timer. The only non-deterministic part is the optional enrichment pass (Step 3), which is additive and skippable. Do not wrap this skill in /loop / /schedule / CronCreate; there is no verdict to re-fire and no external event to wait on.

Why this exists

Five language-model auditors each independently parsing a PDF = five different hallucinated tables and five different number lists, none reproducible — and the obvious dismissal, "an LLM grading another LLM's paper is just slop." The structural answer is one deterministic pass that turns the paper into:

• artifact_manifest.json — what was observable; this fixes the observability level L, the ceiling on every downstream finding's severity, and
• claims.json — a list of span-anchored, hashed, checkable claims (schemas/claims.schema.json).

Every downstream finding must cite a claim_id from this ledger and quote a verbatim span of it. No ledger claim → no finding (the single most important integrity rule of the repo, enforced again by tools/adjudicate_findings.py). That is what makes the difference between "a model said so" and "here is the exact sentence, its file, and its content hash" (DESIGN.md §2).

Role in the pipeline (what this skill does and does NOT do)

This skill is stage [1]–[2] only. It states what the paper says, never whether it is right. The finding.schema.json skill enum technically lists evidence-ledger for completeness, but this skill never writes a finding object. If you came here for a PASS/FAIL, you want /anti-autoresearch (the orchestrator), not this skill.

Core principle

Deterministic first; the model may only add, never invent, never judge.

1. The numeric/citation/table backbone comes from code (tools/), not a model — that is what makes the whole pipeline reproducible and defensible.
2. The optional LLM pass is an additive claim-extractor: it may add a span-anchored semantic claim whose text_span is a verbatim substring of a hashed source file; it may never introduce a number, alter an extracted value, propose a finding, or assign a severity/verdict. The executor validates every added span (Step 4) and rejects anything it cannot locate. Its surface is deliberately broad — beyond method/scope/baseline it also captures theorem statements (with their assumptions), definitions, proof/derivation steps, equations, conclusions, the motivation span, and reproducibility-artifact references — but every one of those rides on an existing claims.schema.json type (scope/method/comparison/artifact_ref); enrichment widens what content is anchored, never the type vocabulary, the Step-4 gate, or the deterministic backbone.
3. No span → no claim. Every text_span in the ledger must be locatable in a hashed source. This is the same gate the adjudicator enforces on findings, applied one stage upstream.

Constants

• LEDGER_VERSION = 0.1 — stamped into claims.json by build_claim_ledger.py; never hand-edit.
• TAXONOMY_VERSION = 0.5 — the ledger is taxonomy-agnostic (it tags no pattern_id); patterns are applied post-hoc by auditors (references/hack-pattern-taxonomy.md, now 46 integrity patterns A–H + 13 AIS + 2 advisory). Never tag a claim with a pattern_id here. v0.5 migrated the pure-style patterns to the zero-weight AIS track; the ledger stays untagged, but its enrichment (Step 3) now surfaces the anchors those auditors quote.
• OBSERVABILITY = derived — L0 (PDF/text only) · L1 (LaTeX, no results) · L2 (repo + results). L3 is never emitted in v0 (we never promise reproduction). The rule is deterministic (references/observability-levels.md).
• EMITS_FINDINGS = false · EMITS_VERDICT = false — load-bearing. This skill produces a ledger, not judgments.
• DETECT_ONLY = true — never edits the audited paper; only reads sources and writes its own outputs (this is why Edit is absent from allowed-tools).
• ENRICH = true (default) — run the additive semantic pass (Step 3), which surfaces the semantic, proof/derivation, and structure spans the regex backbone misses (theorem statements, assumptions, definitions, proof steps, equations, conclusions, the motivation span, reproducibility-artifact references). Set false (or pass — enrich: false) to ship the deterministic backbone alone. Enrichment is non-blocking: if the Codex MCP is unavailable it is skipped and the deterministic ledger is the canonical output.
• REVIEWER (enrichment only) — model gpt-5.6-sol, model_reasoning_effort: xhigh, sandbox: read-only, different model family from the executor (references/reviewer-independence.md Layer 1). CONTEXT_POLICY = fresh: a new mcp__codex__codex thread per run, never mcp__codex__codex-reply. Told only source paths + the existing ledger, never the executor's opinions or any prior finding.
• OUTPUTS — artifact_manifest.json, claims.json (+ enrichment trace under .aris/traces/evidence-ledger/<date>_run<NN>/ when Step 3 runs), all written into the paper directory.

Resolve the repo root once and reuse it for every tool call: ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd). Shell variables do not persist between separate Bash calls, so Step 0 persists the resolved values to a run.env; every later block re-sources it (or run a Step's commands in one Bash call). Always use absolute paths.

Step 0 — Resolve the input & set up the run

$ARGUMENTS is a paper directory, a PDF path, or an arXiv id. Resolve it to one absolute PAPER_DIR + a stable PAPER_ID, extract PDF text if there is no LaTeX, and persist the values. Never fabricate inputs; if no source text can be produced, stop (a fake claims.json is worse than none).

ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
ARG="$ARGUMENTS"

if [ -d "$ARG" ]; then                                   # (a) paper directory
    PAPER_DIR="$(cd "$ARG" && pwd)"; PAPER_ID="$(basename "$PAPER_DIR")"
elif [ -f "$ARG" ] && printf '%s' "$ARG" | grep -qiE '\.pdf$'; then   # (b) lone PDF
    PAPER_ID="$(basename "${ARG%.*}")"
    PAPER_DIR="$(pwd)/.aa_work/$PAPER_ID"; mkdir -p "$PAPER_DIR"; cp "$ARG" "$PAPER_DIR/paper.pdf"
elif printf '%s' "$ARG" | grep -qE '^[0-9]{4}\.[0-9]{4,5}(v[0-9]+)?$'; then   # (c) arXiv id
    PAPER_ID="${ARG//./_}"
    PAPER_DIR="$(pwd)/.aa_work/$PAPER_ID"; mkdir -p "$PAPER_DIR"
    ( curl -fsSL "https://arxiv.org/e-print/$ARG" -o "$PAPER_DIR/src.tar" \
        && tar -xf "$PAPER_DIR/src.tar" -C "$PAPER_DIR" 2>/dev/null ) \
      || curl -fsSL "https://arxiv.org/pdf/$ARG.pdf" -o "$PAPER_DIR/paper.pdf"   # source preferred (better spans)
else
    echo "ERROR: cannot resolve '$ARG' (need a dir, a .pdf path, or an arXiv id like 2401.01234)"; exit 1
fi

# No LaTeX? Extract PDF text now (best spans available at L0). -layout preserves table columns.
if ! find "$PAPER_DIR" -name '*.tex' -not -path '*/.aris/*' | grep -q .; then
    # Deterministic primary-PDF pick (issue #11): asset-looking PDFs (figures/,
    # images/, supplement*/ dirs or fig-like basenames) demoted, paper-like basenames
    # promoted. A dir holding ONLY figure/supplement PDFs yields NO pick (exit 1) —
    # the run then stops honestly at "no source text" instead of building the whole
    # ledger from a figure PDF. Only exit 1 is tolerated; a selector CRASH must not
    # silently degrade the run to the stale-.txt fallback.
    PDF=$(python3 "$ROOT/tools/select_primary_pdf.py" "$PAPER_DIR"); rc=$?
    [ "$rc" -le 1 ] || { echo "ERROR: select_primary_pdf.py failed (rc=$rc)"; exit 1; }
    [ -n "$PDF" ] && { pdftotext -layout "$PDF" "$PAPER_DIR/paper.txt" 2>/dev/null \
      || mutool draw -F txt -o "$PAPER_DIR/paper.txt" "$PDF" 2>/dev/null \
      || python3 -c 'import sys,fitz;open(sys.argv[2],"w").write("\n".join(p.get_text() for p in fitz.open(sys.argv[1])))' "$PDF" "$PAPER_DIR/paper.txt" 2>/dev/null; }
    # extraction yielded nothing but a pre-extracted *.txt exists? adopt it as the L0 source
    if [ ! -s "$PAPER_DIR/paper.txt" ]; then
        TXT=$(find "$PAPER_DIR" -maxdepth 2 -name '*.txt' -not -path '*/.aris/*' ! -name paper.txt | head -n1)
        [ -n "$TXT" ] && cp "$TXT" "$PAPER_DIR/paper.txt"
    fi
fi

mkdir -p "$PAPER_DIR/.aris/evidence-ledger"
cat > "$PAPER_DIR/.aris/evidence-ledger/run.env" <<EOF
ROOT="$ROOT"
PAPER_DIR="$PAPER_DIR"
PAPER_ID="$PAPER_ID"
EOF
echo "PAPER_DIR=$PAPER_DIR  PAPER_ID=$PAPER_ID  ROOT=$ROOT"
find "$PAPER_DIR" \( -name '*.tex' -o -name '*.pdf' -o -name '*.txt' \) -not -path '*/.aris/*' | sort

Every later Bash block begins with source "<PAPER_DIR>/.aris/evidence-ledger/run.env" — substitute the absolute PAPER_DIR printed above.

Validation gate. "$PAPER_DIR" must now contain at least one *.tex or a non-empty paper.txt (the exact L0 source Step 2 Branch B reads). A bare *.pdf whose text never extracted is not enough — there are no spans to anchor.

Failure handling.

• No *.tex and no non-empty paper.txt (e.g. a PDF every extractor failed on) → STOP: report exactly what was searched; with no source text there are no spans to anchor, so there can be no ledger.
• arXiv curl failed (network/proxy) → STOP: report the exit and ask the caller for a local paper-dir or PDF. Do not fabricate a ledger.
• A near-empty / garbled paper.txt (scanned image, heavy math) — concretely, wc -c < "$PAPER_DIR/paper.txt" is implausibly small for the page count (rule of thumb: under ~1000 bytes for a multi-page paper) or is mostly non-alphanumeric → say so explicitly and treat the run as L0 with confidence: low throughout; do not silently proceed as if you had clean text.

Step 1 — Artifact manifest + observability level (deterministic)

Inventory what is available and derive L by the fixed rule — a tool, not a manual judgment. The level caps every downstream finding's severity, so it must be honest.

source "<PAPER_DIR>/.aris/evidence-ledger/run.env"
MAN_ARGS=(--paper-id "$PAPER_ID" --dir "$PAPER_DIR" --out "$PAPER_DIR/artifact_manifest.json")
[ -f "$PAPER_DIR/paper.txt" ] && MAN_ARGS+=(--pdf-text "$PAPER_DIR/paper.txt")
python3 "$ROOT/tools/build_manifest.py" "${MAN_ARGS[@]}"
# -> manifest: observability L1 (latex=1 pdf=0 bib=0 repo=False results=False) -> .../artifact_manifest.json

L=$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["observability_level"])' "$PAPER_DIR/artifact_manifest.json")
case "$L" in 0|1|2) : ;; *) echo "ERROR: unexpected level '$L' (v0 operates only at L0/L1/L2)"; exit 1;; esac
echo "L=$L" >> "$PAPER_DIR/.aris/evidence-ledger/run.env"   # carry L forward to Step 2
echo "observability level = L$L"

The derivation rule (references/observability-levels.md; build_manifest.py implements exactly this):

Validation gate — never over-state the level. If you only have a PDF, L MUST be 0; do not hand --observability-level 2 to Step 2 because a repo "exists somewhere else." build_manifest.py never sets repo.rerunnable: true (no L3 in v0) — do not edit it to true. Edge cases the rule handles correctly (L2 requires both a repo and result data files): an empty results/ dir (no *.json/*.csv) does not reach L2, and a repo present without result files does not reach L2 either — each stays at whatever the source gives (L1 if LaTeX is present, else L0).

Failure handling. build_manifest.py non-zero exit or empty/invalid JSON → STOP: without a derived level you cannot legally cap severity downstream; do not guess a level.

Step 2 — Extract the deterministic ledger (no LLM)

The numeric/citation/table backbone comes from code, not a model. LaTeX-first (stable spans + real line numbers); the PDF-text path is a lower-confidence fallback. Pass the same L derived in Step 1.

Pick the branch: if any *.tex exist use Branch A; otherwise use Branch B. The TEX array below makes that test explicit and survives spaces in paths.

Branch A — LaTeX present (L1/L2; preferred):

source "<PAPER_DIR>/.aris/evidence-ledger/run.env"
TEX=()   # space-safe + deterministic: one path per line, sorted for reproducible order
while IFS= read -r f; do [ -n "$f" ] && TEX+=("$f"); done \
  < <(find "$PAPER_DIR" -type f -name '*.tex' -not -path '*/.aris/*' | LC_ALL=C sort)
[ ${#TEX[@]} -gt 0 ] || { echo "no .tex found — use Branch B"; exit 1; }
python3 "$ROOT/tools/build_claim_ledger.py" --paper-id "$PAPER_ID" \
    --latex "${TEX[@]}" \
    --observability-level "$L" \
    --out "$PAPER_DIR/claims.json"
# -> ledger: 13 claims {'caption': 1, 'citation': 3, 'number': 7, 'table_cell': 2} -> .../claims.json

Branch B — no LaTeX (text-only spans; paper.txt from Step 0). Usually L0, but L2 when a repo + result files exist without any .tex, so pass the derived $L — never a hardcoded 0 (the gate below asserts the ledger level equals the manifest $L):

source "<PAPER_DIR>/.aris/evidence-ledger/run.env"
test -s "$PAPER_DIR/paper.txt" || { echo "ERROR: no extracted text"; exit 1; }
python3 "$ROOT/tools/build_claim_ledger.py" --paper-id "$PAPER_ID" \
    --pdf-text "$PAPER_DIR/paper.txt" --observability-level "$L" \
    --out "$PAPER_DIR/claims.json"

What it extracts (each claim carries claim_id, type, verbatim text_span, location{file,line,section}, evidence_anchor = sha256 of the source text, extractor, confidence; numeric claims also carry a parsed value):

The section tracker labels each span abstract | intro | method | experiments | table:N | figure:N | appendix | body (section names lowercased from \section{...}); the PDF-text path cannot track sections or lines and labels them unknown. Numeric claims carry value{raw,normalized,unit,metric,direction,aggregation} so the deterministic layer can do arithmetic (delta/coherence checks) downstream without a model. Omit --generated-at for byte-reproducible output (the eval harness does this); pass --generated-at "$(date -u +%Y-%m-%dT%H:%M:%SZ)" for run provenance — the claims are identical either way.

Worked example (clean fixture eval/fixtures/clean/sample_paper.tex as main.tex) — the stdout above, then two real claims (location.file mirrors the path you pass to --latex):

{ "claim_id": "C001", "type": "table_cell",
  "text_span": "Baseline \\cite{smith2024bar} & 73.1 \\\\",
  "location": {"file": "main.tex", "line": 36, "section": "table:1"},
  "value": {"raw":"73.1","normalized":73.1,"unit":null,"metric":null,"direction":"unknown","aggregation":"unspecified"},
  "evidence_anchor": "e6186efa…0460", "extractor": "table_parser", "confidence": "medium" }

{ "claim_id": "C003", "type": "number",
  "text_span": "FooNet reaches 78.0\\% accuracy, improving from a 73.1\\% baseline to 78.0\\% accuracy, a 6.7\\% relative improvement.",
  "location": {"file": "main.tex", "line": 9, "section": "abstract"},
  "value": {"raw":"78.0","normalized":78.0,"unit":"%","metric":"accuracy","direction":"unknown","aggregation":"unspecified"},
  "evidence_anchor": "e6186efa…0460", "extractor": "latex_regex", "confidence": "high" }

The ledger states, it does not judge. Run the extractor on the corrupted eval/fixtures/synthetic_corruptions/delta_inflate.tex (abstract says "16.7% relative improvement") and you get an identical 13-claim shape — only C003's verbatim text changes. Spotting that 16.7% contradicts 73.1→78.0 is consistency-audit's job (HP-DELTA-ERROR), not the ledger's; the ledger just captures the span faithfully.

Validation gate. Confirm the ledger is well-formed, the level matches, and report the claim mix:

source "<PAPER_DIR>/.aris/evidence-ledger/run.env"
python3 - "$PAPER_DIR/claims.json" "$L" <<'PY'
import json,sys
d=json.load(open(sys.argv[1],encoding="utf-8")); L=int(sys.argv[2])
for k in ("ledger_version","paper_id","observability_level","source_files","claims"):
    assert k in d, f"ledger missing top-level key: {k}"
assert d["observability_level"]==L, f"level drift: ledger={d['observability_level']} manifest={L}"
assert d["source_files"] and all(s.get("sha256") for s in d["source_files"]), "missing source hash"
for c in d["claims"]:
    assert {"claim_id","type","text_span","location"} <= c.keys(), f"claim {c.get('claim_id')} missing field"
    assert c["location"].get("file"), f"claim {c['claim_id']} has no location.file"
by={t:sum(1 for c in d["claims"] if c["type"]==t) for t in sorted({c['type'] for c in d['claims']})}
print(f"OK ledger L{L}: {len(d['claims'])} claims {by}")
PY

Failure handling.

• build_claim_ledger.py errors with "provide at least one --latex or --pdf-text" → your file glob matched nothing; re-check the branch (use Branch B when no *.tex were found, i.e. the TEX array is empty).
• 0 claims on a paper that visibly has numbers/citations → the wrong files were passed or the .tex is a stub. Re-inspect inputs (read the head of the .tex/.txt) and re-run; do not fabricate claims. A genuinely claim-free paper is rare — ship the empty ledger only after confirming inputs.
• A single malformed .tex can crash the extractor (build_claim_ledger.py has no per-file try/except) → drop that one path from the TEX array and re-run rather than abandoning the whole paper; note the dropped file.

Step 3 — Optional additive semantic enrichment (cross-model, fresh thread)

Skip entirely when ENRICH = false. This step adds claims; it never edits or removes a deterministic claim, never adds a number, never proposes a finding.

The regex backbone has high recall on the numeric/citation surface but misses the semantic, proof/derivation, and structure spans auditors need: the method-definition span, theorem statements with their assumptions, explicit scope sentences, the baseline list, comparison framings — and, for the families, the spans that families B (argument-chain / causal-leap), D (reproducibility) and G (proof & derivation) anchor to: definitions, proof / derivation steps, formulas / equations, stated assumptions, load-bearing conclusions, the motivation span, and reproducibility-artifact references (does the paper ship / promise code, prompts, configs?). These feed consistency-audit (HP-METHOD-DRIFT, HP-THEOREM-SCOPE-DRIFT, HP-ARGUMENT-CHAIN-BREAK, HP-CAUSAL-EVIDENCE-LEAP), experiment-forensics (HP-MISSING-REPRO-ARTIFACT), and proof-derivation-forensics (HP-PROOF-OBLIGATION-GAP, HP-PROOF-CIRCULARITY, HP-DERIVATION-INVALID, HP-SYMBOL-SEMANTIC-DRIFT, HP-ASSUMPTION-SMUGGLE). Make one cross-model call to add such span-anchored candidate claims. This is extraction help, not a review: the model proposes candidate claims (verbatim spans); the executor's deterministic substring gate (Step 4) decides what is admitted — nothing here is a finding or a verdict (references/reviewer-independence.md).

No new type vocabulary — broadened content on the existing schema types. Every new span rides on a claims.schema.json type the deterministic layer and Step 4 already allow, so the anti-hallucination gate is unchanged and claims.json stays schema-valid. The mapping — what new content the enrichment surfaces → which existing type carries it → which family/pattern anchors to it:

These are anchors, not findings. The ledger never says a proof is circular, an assumption is smuggled, a chain is broken, or an artifact is missing — it only captures the verbatim span so the family-B/D/G reviewer has a claim_id to quote. The judgment stays in the auditor; the verdict stays in tools/adjudicate_findings.py. Family-G recall is highest at L1: equation and theorem-statement spans carry stable line numbers, which lets proof-derivation-forensics scaffold per-theorem anchor candidates by line window — so the prompt below asks the reviewer to include line whenever it extracts from LaTeX.

Set up the trace run dir and list the exact source paths to hand the reviewer:

source "<PAPER_DIR>/.aris/evidence-ledger/run.env"
DATE=$(date -u +%Y-%m-%d); TB="$PAPER_DIR/.aris/traces/evidence-ledger"; mkdir -p "$TB"
NN=$(printf "%02d" $(( $(find "$TB" -maxdepth 1 -type d -name "${DATE}_run*" 2>/dev/null | wc -l) + 1 )))
RUNDIR="$TB/${DATE}_run${NN}"; mkdir -p "$RUNDIR"
echo "RUNDIR=\"$RUNDIR\"" >> "$PAPER_DIR/.aris/evidence-ledger/run.env"
echo "RUNDIR=$RUNDIR"
python3 -c 'import json,sys;[print(s["path"]) for s in json.load(open(sys.argv[1]))["source_files"]]' "$PAPER_DIR/claims.json"

Call the reviewer with a fresh mcp__codex__codex thread (never codex-reply), cwd = PAPER_DIR. Paste the source paths and the existing ledger's claim_id + text_span list into the prompt:

mcp__codex__codex:
  model: gpt-5.6-sol
  config: {"model_reasoning_effort": "xhigh"}
  sandbox: read-only
  cwd: <PAPER_DIR>
  prompt: |
    You are an ADDITIVE claim extractor for an evidence ledger. You are NOT a
    reviewer and NOT a judge: do not assess correctness, do not propose findings, do
    not assign severity or any verdict. Your ONLY job is to surface SEMANTIC claims a
    regex pass misses, each anchored to a VERBATIM span of a real source file.

    Source files (use these EXACT path strings in location.file):
    [list the paths from claims.json -> source_files[].path]

    The deterministic ledger already extracted these (do NOT duplicate them):
    [paste the claim_id + text_span list from claims.json]

    ADD claims ONLY of these SEVEN schema types (numbers and table cells are already
    covered by the deterministic layer — do NOT emit `number` or `table_cell`, and do
    NOT invent any new type string). Each type's CONTENT is broadened below to carry the
    proof/derivation + structure spans the family-B/D/G auditors anchor to:
      - method      : the sentence(s) that DEFINE the proposed method / its key
                      conditions (e.g. "no test-time labels", backbone, training data);
                      ALSO a formal **definition** of a symbol/operator/construct, a
                      **proof step / derivation transition**, or a **formula/equation**
                      stated symbolically (for family G — copy the math VERBATIM,
                      including every \command, subscript, superscript, and delimiter).
      - scope       : an explicit scope/generality/limitation sentence the regex missed;
                      ALSO a **theorem/lemma/proposition statement** (you MUST include
                      its stated assumptions/hypotheses in the span, not just the
                      conclusion), a standalone **stated assumption**, or the
                      **motivation / problem-framing** sentence the intro rests on
                      (for families B and G).
      - baseline    : the sentence or list naming the baselines compared against.
      - comparison  : a sentence ASSERTING a comparison ("our method outperforms X") —
                      the framing, not the numbers; ALSO a load-bearing **conclusion**
                      that asserts a causal / equivalence / "therefore" relation
                      ("A correlates with B, therefore A causes B") for family B.
      - citation    : a sentence whose citation is load-bearing for a specific claim.
      - caption     : a table/figure caption the extractor missed.
      - artifact_ref: a reference to a named result file / table / appendix item; ALSO a
                      **reproducibility-artifact reference** — code/repo/prompt/config
                      the paper ships or promises ("code at github.com/…", "we will
                      release", "prompts in App. C", "hyperparameters in Table 5") —
                      for family D. Capture the EXACT sentence; do NOT judge whether the
                      artifact is sufficient, present, or fake.

    HARD RULES (a violation gets your item silently dropped by the merger):
      - Use ONLY the seven types above. A new/unknown type string is dropped.
      - text_span MUST be copied CHARACTER-FOR-CHARACTER from the named file
        (including LaTeX markup like \cite{...}, \%, \le, \alpha, $...$). If unsure it
        is verbatim, OMIT it. Do NOT unescape, re-LaTeX, normalize, or "tidy" math.
      - NEVER introduce, alter, or "tidy" a number. Do NOT emit a `value` field.
      - For a theorem (`scope`), the span MUST include the stated assumptions, not just
        the claim. For an assumption anchor, prefer the span stating the hypotheses.
      - For a conclusion (`comparison`), include the inferential connective
        ("therefore"/"thus"/"hence"/"so") so the causal/equivalence leap is in the span.
      - For an artifact_ref, capture the presence/promise sentence verbatim; the ledger
        records that the reference EXISTS, it NEVER rules the artifact missing or fake.
      - Prefer to include `line` when the source is LaTeX, so the proof/structure
        auditors can scaffold per-theorem anchor candidates by line window.
      - location.file MUST be one of the source paths above.

    Output ONLY a strict JSON array (no prose, no markdown fence) of objects:
      {"type":"<one of the seven types>","text_span":"<verbatim>",
       "location":{"file":"<one of the listed paths>","line":<int — include when LaTeX>,
                   "section":"abstract|intro|method|experiments|theorem|proof|appendix|..."}}
    Output [] if you find nothing new.

Then, using the Write tool, save two files into the RUNDIR printed above — substitute that literal absolute path (the Write tool does not expand shell variables like $RUNDIR): the reviewer's full response verbatim to <RUNDIR>/codex_raw.md (forensic; never silently dropped) and the parsed JSON array to <RUNDIR>/enrichment_candidates.json (strip any code fence; if the reviewer returned [], write []).

Failure handling (non-blocking). If the Codex MCP hangs/stalls → re-invoke the same prompt as a fresh thread (still mcp__codex__codex, never codex-reply). If it fails twice, returns non-JSON, or the MCP is unavailable → skip enrichment, write [] to enrichment_candidates.json, note the skip in the trace, and ship the deterministic ledger. Enrichment is strictly additive and optional.

Step 4 — Validate + merge enrichment (the anti-hallucination gate)

Skip this step whenever Step 3 was skipped (ENRICH = false, or the reviewer was unavailable so no enrichment_candidates.json was written): no RUNDIR is set and the deterministic ledger from Step 2 is already the final output. Do not run the block below against an unset $RUNDIR.

When Step 3 ran, the executor validates every candidate before it enters the ledger — the same "no span → no claim" discipline the adjudicator applies to findings. This fails open (a missing/invalid candidates file leaves the deterministic ledger untouched).

source "<PAPER_DIR>/.aris/evidence-ledger/run.env"
python3 - "$PAPER_DIR/claims.json" "$RUNDIR/enrichment_candidates.json" "$RUNDIR/enrichment_rejects.json" <<'PY'
import json, re, sys, pathlib
ledger_p, cand_p, rej_p = sys.argv[1], sys.argv[2], sys.argv[3]
ledger = json.load(open(ledger_p, encoding="utf-8"))
src = {s["path"]: s for s in ledger["source_files"]}     # source_files[].sha256 = the text-hash anchor
norm = lambda t: re.sub(r"\s+", " ", t).strip()          # extractor collapses newlines to spaces; match likewise
srctext = {p: norm(pathlib.Path(p).read_text(encoding="utf-8", errors="replace")) for p in src}
ALLOWED = {"method", "scope", "baseline", "comparison", "citation", "caption", "artifact_ref"}  # never number/table_cell
try:
    cands = json.load(open(cand_p, encoding="utf-8")); cands = cands if isinstance(cands, list) else []
except (OSError, json.JSONDecodeError):
    cands = []                                           # fail open: no enrichment admitted
kept, rejected = [], []
for c in cands:
    f = (c.get("location") or {}).get("file"); span = c.get("text_span", ""); t = c.get("type")
    if t not in ALLOWED:                          rejected.append({"reason":"bad_type","candidate":c}); continue
    if f not in srctext:                          rejected.append({"reason":"file_not_in_ledger","candidate":c}); continue
    if not span or norm(span) not in srctext[f]:  rejected.append({"reason":"span_not_verbatim","candidate":c}); continue
    c.pop("value", None); c.pop("claim_id", None)        # never trust a model number; we re-id below
    c["evidence_anchor"] = src[f]["sha256"]              # anchor to the same hash deterministic claims use
    c["extractor"] = "manual"; c["confidence"] = "medium"
    kept.append(c)
merged = ledger["claims"] + kept                         # deterministic claims keep their order/ids; enrichment appended
for i, c in enumerate(merged, 1): c["claim_id"] = f"C{i:03d}"
ledger["claims"] = merged
json.dump(ledger, open(ledger_p, "w", encoding="utf-8"), indent=2, ensure_ascii=False)
json.dump(rejected, open(rej_p, "w", encoding="utf-8"), indent=2, ensure_ascii=False)
print(f"enrichment: kept {len(kept)}, rejected {len(rejected)} -> {ledger_p}")
PY

The gate enforces, all mandatory:

1. Type allow-list — only the seven semantic types (method, scope, baseline, comparison, citation, caption, artifact_ref); number/table_cell are rejected (the deterministic layer owns numbers). The proof/derivation + structure content (theorem statements, assumptions, definitions, proof steps, equations, conclusions, the motivation span, reproducibility-artifact references) rides on these same seven types (Step 3's mapping table), so this gate — and claims.json's schema enum — are unchanged.
2. Span is verbatim — text_span must be a substring of the source after whitespace-normalization (the extractor itself joins lines with single spaces, so normalize both sides). Paraphrase → reject.
3. No invented numbers — any value field is stripped; a number can only enter the ledger via Step 2.
4. Real source — location.file must be one of the ledger's source_files.

Re-id is append-only: deterministic claims keep their C001… ids; admitted enrichment claims continue the sequence, tagged extractor: "manual", confidence: "medium". The merge never removes or edits a deterministic claim. enrichment_rejects.json lands in the trace (the REJECT count is expected — it is the anchoring guard working).

Failure handling. Every candidate rejected (or []) → acceptable; the ledger is just the deterministic backbone. If a later self-check fails (non-contiguous ids, a value crept in), re-run the merger from the deterministic ledger; never hand-patch claims.json. If claims.json was already overwritten, rebuild from Step 2 then re-merge.

Step 5 — Self-check the ledger (it is the spine, so verify it)

Confirm claims.json is well-formed, the level still matches the manifest, ids are contiguous, and every still-present source is text-identical to extraction time (detect-only proof). The re-hash replicates build_claim_ledger.py's text hash (decode→re-encode UTF-8), not a raw-byte hash, so it matches source_files[].sha256; any source that has gone missing is reported, not silently passed.

source "<PAPER_DIR>/.aris/evidence-ledger/run.env"
python3 - "$PAPER_DIR/claims.json" "$PAPER_DIR/artifact_manifest.json" <<'PY'
import json, sys, hashlib, os
L = json.load(open(sys.argv[1], encoding="utf-8")); M = json.load(open(sys.argv[2], encoding="utf-8"))
assert {"ledger_version","paper_id","observability_level","source_files","claims"} <= L.keys(), "missing top-level field"
assert L["observability_level"] == M["observability_level"], "ledger level != manifest level"
ids = [c["claim_id"] for c in L["claims"]]
assert ids == [f"C{i:03d}" for i in range(1, len(ids)+1)], "claim_ids not unique/sequential"
for c in L["claims"]:
    assert {"claim_id","type","text_span","location"} <= c.keys(), f"{c.get('claim_id')} missing field"
    assert c["location"].get("file"), f"{c['claim_id']} has no location.file"
def text_sha(p):  # mirror build_claim_ledger.sha256_text
    return hashlib.sha256(open(p, encoding="utf-8", errors="replace").read().encode("utf-8")).hexdigest()
missing = [s["path"] for s in L["source_files"] if not os.path.exists(s["path"])]
for s in L["source_files"]:
    if os.path.exists(s["path"]):
        assert text_sha(s["path"]) == s["sha256"], f"source changed since extraction: {s['path']}"
if missing:
    print("WARNING: source(s) missing at self-check, hash unverified: " + ", ".join(missing))
by = {t: sum(1 for c in L["claims"] if c["type"]==t) for t in sorted({c["type"] for c in L["claims"]})}
print(f"== Evidence Ledger built ==  L{L['observability_level']}  {len(ids)} claims {by}  "
      f"({sum(1 for c in L['claims'] if c.get('extractor')=='manual')} from enrichment)")
PY
echo "Outputs: $PAPER_DIR/artifact_manifest.json  +  $PAPER_DIR/claims.json"

A non-empty, conformant claims.json is the green light for the auditor fan-out. Do not run any auditor, numeric check, or the adjudicator from here. Failure handling. A source-hash mismatch means a file changed mid-run — rebuild from Step 1 against the current files so the anchors are honest.

Output contract

Written into the paper directory (the paths every downstream auditor and the orchestrator expect):

• artifact_manifest.json — schemas/artifact_manifest.schema.json. Records the observable inputs (hashed) and the derived observability level that caps all downstream severity (repo.rerunnable is always false in v0).
• claims.json — schemas/claims.schema.json. The evidence ledger: span-anchored, hashed, deterministic backbone (+ any validated enrichment claims tagged extractor: manual, confidence: medium). The only structure auditors may reason over; source_files[] carry content hashes so every finding is reproducible against an immutable input. When Step 3 runs, the enrichment claims also carry the proof/derivation + structure anchors (theorem statements, assumptions, definitions, proof steps, equations, conclusions, the motivation span, reproducibility-artifact references) the family-B/D/G auditors quote — all on the existing schema types (no new vocabulary).
• .aris/traces/evidence-ledger/<date>_run<NN>/ — only when Step 3 ran: codex_raw.md (raw reviewer reply), enrichment_candidates.json (parsed array), enrichment_rejects.json (the anchoring guard's rejects).

Explicitly NOT emitted: any <skill>.findings.json, any overall_verdict, any pattern_id tagging, any accusation. Those belong to the auditors and tools/adjudicate_findings.py.

What consumes the ledger downstream (integration)

You normally reach these via /anti-autoresearch; the exact contracts are:

# consistency-audit's deterministic arithmetic layer (HP-DELTA-ERROR, HP-NUM-INFLATE):
python3 "$ROOT/tools/check_numeric_consistency.py" --ledger "$PAPER_DIR/claims.json" \
    --out consistency-audit.deterministic.findings.json

# presentation-signals' surface checks (HP-DUP-TABLE via table_cell claims, etc.) —
# AUXILIARY, surface-class label by the adjudicator, default false_positive_risk:high,
# NOT an AI-text classifier, never a standalone verdict:
python3 "$ROOT/tools/check_presentation.py" --ledger "$PAPER_DIR/claims.json" \
    --out presentation-signals.deterministic.findings.json

# the deterministic adjudicator — --ledger is REQUIRED:
python3 "$ROOT/tools/adjudicate_findings.py" --findings *.findings.json \
    --ledger "$PAPER_DIR/claims.json" --paper-id "$PAPER_ID" \
    --observability-level "$L" --taxonomy-version 0.5 --out report.json --md REPORT.md

adjudicate_findings.py requires --ledger: it re-verifies that each above-info finding quotes a verbatim ledger span; without it every such finding fails closed to info — a missing or wrong ledger silently neuters the whole audit. The ledger you build here is load-bearing for every verdict. This skill does not run any of these — stop at a validated ledger.

Key rules

• Deterministic first. The numeric/citation/table backbone comes from code, not a model — that reproducibility is the whole credibility argument. Same source bytes → byte-identical ledger (omit --generated-at).
• Spans are real. Every text_span is a verbatim substring of a hashed source. The executor rejects any enrichment span it cannot match (Step 4); the adjudicator rejects unanchored findings again downstream.
• Enrichment adds, never invents. Only the seven semantic types — even the broadened proof/derivation + structure content (theorem statements, assumptions, definitions, proof steps, equations, conclusions, the motivation span, reproducibility-artifact references) rides on those same types, never a new one; never a number, never an altered value, never a removed/edited deterministic claim. The numeric/citation backbone stays 100% deterministic, and the ledger captures these spans as anchors only — it never judges a proof, an assumption, a broken chain, or a missing artifact (that is the family-B/D/G auditors' job).
• Never over-state the level. L is derived from the artifacts present and caps all downstream severity. A PDF-only run is L0 — full stop. Never set repo.rerunnable: true (no L3 in v0).
• No judgment here. The ledger states what the paper says, never whether it is right. EMITS_FINDINGS = false, EMITS_VERDICT = false; no pattern_id tagging.
• Cross-model, fresh thread, no leakage. The one enrichment call is gpt-5.6-sol @ xhigh, read-only, a different family from the executor, a new mcp__codex__codex thread (never codex-reply), told only source paths + the ledger.
• Detect-only. Never edit the audited paper; only read sources and write this skill's own outputs (Step 5's hash check proves it).

When NOT to use (and limits)

• Not an auditor. No PASS/FAIL, no findings. For a verdict, run the auditor skills + tools/adjudicate_findings.py (or /anti-autoresearch).
• Don't skip it before an audit. Every auditor reads claims.json; running one without the ledger means it re-reads the PDF and hallucinates structure — exactly what this repo exists to prevent.
• Don't loop/schedule it. A deterministic transform, not a poller and not a verdict; re-run only when sources change.
• Recall, not certification. The extractor is best-effort regex: high recall on the checkable surface, not a guarantee every claim was found. The optional enrichment (Step 3) broadens recall to the proof/derivation + structure spans family B/D/G need, but it too is best-effort and span-gated — an uncaptured pure-symbol step simply yields no anchor (the honest outcome, not a defect; recall is materially higher at L1, where theorem/equation spans carry line numbers). Low-confidence (PDF/OCR) numbers are tagged confidence: low for the human and adjudicator to weight; the ledger is a foundation, not a proof of completeness.

Review tracing

Only Step 3 (the single model call) needs a trace: its RUNDIR (.aris/traces/evidence-ledger/<date>_run<NN>/) holds codex_raw.md, enrichment_candidates.json, and enrichment_rejects.json under forensic Policy C (fresh thread, full reply, never silently dropped — see references/integrity-forensics-contract.md §"Output contract per skill"), so a later reader can see which enrichment spans were admitted and why. When ENRICH = false or enrichment is skipped (reviewer unavailable), note the skip inline and ship the deterministic ledger — no trace dir is required. Steps 0–2 and 4–5 are deterministic; their stdout and the hashed source_files are the only record needed.