Proof Engine

LLMs hallucinate facts and make reasoning errors. This skill overcomes both by offloading all verification to code and citations. Every fact is either computed by Python code anyone can re-run (Type A) or backed by a specific source, URL, and exact quote (Type B).

Produces four outputs: a re-runnable proof.py script, a reader-facing proof.md, a proof_audit.md with full verification details, and a proof_narrative.md plain-language narrative.

Gotchas

The highest-value lessons from field testing, grouped by area. Read before writing any proof code.

By proof type

Proof type	What to read
Empirical (any Type B fact, including Type S proofs with B-type corroboration)	All subsections below
Pure math (Type A only)	Skip "Citation handling" and "Source behavior & fetch failures". In "Methodology pitfalls," skip the verify_extraction() / data_values item.
Absence of evidence (Type S, no Type B corroboration)	Skip "Citation handling". In "Source behavior & fetch failures," only the blocked-domains item applies (search URLs can also be blocked). Pay particular attention to "Absence claims need search documentation" under "Verdict semantics."

Citation handling

• Handle Unicode in citations: Real web pages use en-dashes, curly quotes, ring-above vs degree, non-breaking spaces. verify_citations.py handles this automatically.
• WebFetch/WebSearch return summaries, not verbatim text — Never use text from WebFetch/WebSearch directly as the quote field in empirical_facts. Use these tools to identify sources, then obtain verbatim quotes via Python requests.get(), browser-captured snapshot, or Wayback archive. See environment-and-sources.md for the full workflow. If a citation returns partial/not_found on a source you know contains the finding, suspect paraphrasing — obtain raw page text and update the quote.
• Quotes must be VERBATIM, never paraphrased: The quote field in empirical_facts must be an exact substring copied from the source page — not recalled from memory, rephrased, condensed, or "cleaned up." If the page says "16 study participants" do not write "Sixteen participants." Paraphrased quotes are the #1 cause of citation verification failures (14 of 25 in field testing). When in doubt, copy a shorter exact substring rather than a longer paraphrased one. Workflow: fetch the URL via requests.get(), find the relevant sentence in the response, copy it character-for-character into quote.
• PDF sources need snapshots: When citing a PDF, use your native PDF reading (or PyMuPDF/pdfplumber) to extract the text during Step 2. Copy the verbatim quote and include the full extracted text as snapshot in empirical_facts. Without a snapshot, re-run verification requires pdfplumber to be installed — which is an optional dependency. The snapshot makes the proof self-contained.
• Quote selection for qualitative claims: Pick quotes that directly state the claim's core assertion, not tangential mentions. A source that says "the brain is remarkable" does not support "adult neurogenesis occurs." The quote must be specific enough that citation verification confirms the source actually addresses the claim.
• Citation presence ≠ citation entailment: verify_all_citations() confirms a quote exists on the page — not that it supports the claim's conclusion. Each Type B quote must name or describe the specific subject of the claim. A quote stating a generic principle (e.g., "falsifiability is a hallmark of science") does not entail a conclusion about a specific subject (e.g., "math washing is not valid science") without author reasoning to bridge the gap. If the bridge requires inference, document it in operator_note and note it as an entailment gap in proof.md's Claim Interpretation section.

Source behavior & fetch failures

• Handle verify_data_values() failures: If verify_data_values() returns found: false for a source (common with JS-rendered pages), do not use that source's data_values as the primary computation input. Use a verified source as primary, and note the unverified source's data as corroborating only. If both sources fail verification, search for a third source with static HTML. A cross-check between two unverified data_values sources is circular — it compares your authored strings against each other.
• Dynamic/JS-rendered sites: Many aggregators (officialdata.org, in2013dollars.com, inflationdata.com) render page chrome via JavaScript. Live fetch gets raw HTML — data tables may be static but page titles, headings, and navigation are often JS-rendered. Quote verification on page titles commonly fails even when data is correct. Use verify_data_values() as the primary verification for table data; treat quote verification as a bonus, not a requirement.
• Academic HTML degrades citation matches: PMC and journal pages embed inline reference markers ([1], superscripts) that inject noise after HTML stripping. If a real verbatim quote gets partial status, check whether the source is academic HTML before suspecting the quote itself. Use snapshot to capture clean text if needed.
• Domains that commonly block automated fetches: The following domains frequently return blocked, captcha, or degraded content for automated HTTP requests. Plan to use snapshot or snapshot_file for sources from these domains: pmc.ncbi.nlm.nih.gov (PubMed Central), nature.com, link.springer.com, sciencedirect.com (Elsevier), wiley.com. See the snapshot fallback pattern in the proof templates. The live→snapshot→Wayback fallback chain in verify_citations.py handles this automatically when a snapshot is provided.

JSON summary & FACT_REGISTRY

• Never create pseudo-quote fields for table data: Don't store table cell values in fields like cpi_1913_quote: "9.883". If the source evidence is a table cell or numeric grid, store it under data_values and verify with verify_data_values(). The validator will reject pseudo-quote fields containing bare numeric or date literals that are parsed as evidence.
• FACT_REGISTRY entries must be dicts, not strings: B/S-type: {"key": "source_a", "label": "..."}. A-type: {"label": "...", "method": None, "result": None}. Plain strings like {"B1": "source_a"} crash build_citation_detail() which calls .get("key") on each entry.
• JSON summary key is claim_natural, not claim: The publish toolchain reads proof_data.get("claim_natural"). Using "claim" as the key silently drops the claim text from the published proof.
• Use LaTeX delimiters for math in claim_natural and markdown sections: When the claim contains mathematical notation, use \(...\) for inline math and \[...\] for display math. Write \(\alpha_i\) not alpha_i, \(\pi^2/6\) not pi^2/6. Do NOT use $...$ delimiters — they collide with currency dollar signs in claims. Non-math claims need no delimiters. The same convention applies to proof.md, proof_audit.md, and proof_narrative.md.
• Use ProofSummaryBuilder for the JSON summary: Import from scripts.proof_summary — it builds v3 format with format_version: 3, validates against the JSON schema, and emits the === PROOF SUMMARY (JSON) === marker. See any template file for usage. The older emit_proof_summary() in computations.py is a legacy fallback that produces v2-shape JSON — do not use it for new proofs.

Computation API

• explain_calc() vs compute_*(): Use named functions (compute_percentage_change(), compute_age()) when they match your computation — they self-document. Use explain_calc() for ad-hoc expressions. Don't wrap a compute_*() call in explain_calc().
• cross_check() mode and tolerance: Use mode="absolute" for computed results that should match closely. Use mode="relative" for source-to-source comparisons. Tolerance heuristics for government statistics: expect 1-5% variation across aggregators due to rounding, month selection (annual avg vs December), and base-period differences. If sources disagree by more than 5%, investigate: find a third source, check if they use different base periods or date ranges, and document the discrepancy in adversarial checks. Don't silently ignore large disagreements — they may indicate one source is wrong.
• compute_percentage_change(mode="decline") is for purchasing-power decline only: It computes (1 - old/new) * 100 — the denominator is the new value. For standard year-over-year decline, use the default mode="increase" with (old_value, new_value) — the result will be negative when new < old.
• parse_percentage_from_quote(quote, fact_id) has no pattern kwarg: For pattern-based extraction, use parse_number_from_quote(quote, pattern, fact_id) instead. The two functions have different signatures — check the Bundled Scripts table.

Verdict semantics & claim formalization

• Don't conflate source count with evidence strength: 5 news articles citing the same study count as 1 independent source, not 5. For qualitative consensus proofs, check whether sources trace to independent primary research. Document the independence rationale in the cross-checks section.
• Absence claims need search documentation: For "no evidence exists" claims, use the Absence-of-Evidence template. Document what was searched (databases, query terms, date ranges), not just what was found. The search_registry structure makes this machine-checkable.
• Don't weaken causal claims to prove them: If the claim says X "causes" Y, you cannot redefine it to X "is associated with" Y in operator_note and then PROVE the weaker version. Decompose into SC-association + SC-causation sub-claims using the compound claim template. If only observational evidence exists without causal inference methods (Bradford Hill, Mendelian randomization, natural experiments), the result is PARTIALLY VERIFIED (association confirmed, causation not established), not PROVED.
• Don't rank from point estimates when the source says they overlap: If Our World in Data says "nuclear: 0.07/TWh, solar: 0.05/TWh" but also says "the uncertainties mean these values are likely to overlap," you cannot conclude solar is safer than nuclear. Set uncertainty_override = True and return UNDETERMINED.
• Don't hardcode decisive variables: Any variable assigned True or False that is later passed to compare() circumvents evidence-based verdict computation. The validator catches *_holds names, but other names (e.g., rh_is_solved = False) must also be computed from evidence. Use compare() or derive from verify_*() / extract_*() results.
• Verdict qualifier suffix: PARTIALLY VERIFIED and UNDETERMINED never get (with unverified citations). Always use apply_verdict_qualifier(base_verdict, any_unverified) — never verdict += or manual if/elif chains for the suffix. The function validates the base verdict string and only applies the suffix to PROVED, DISPROVED, and SUPPORTED.

Methodology pitfalls

• Don't use int() truncation as a cross-check: int(days / 365.2425) == calendar_years is not independent — both are functions of the same input.
• Don't restate the proof as an adversarial check: "70 years after 1948 is 2018, and 2026 > 2018" catches nothing. Search for counter-evidence.
• Don't call verify_extraction() on data_values: It's circular. Instead, call verify_data_values(url, data_values, fact_id) to confirm each value string appears on the source page, then cross-check across sources (Rule 6).
• Adversarial evidence is prose-only, not citation-verified: Sources in adversarial_checks are documented as prose in verification_performed — they are not machine-verified by verify_all_citations(). For contested qualifier proofs, this means the strongest counter-evidence (e.g., independent reviews rejecting a qualifier) is only as trustworthy as the proof author's characterization. Mitigate by: (1) quoting specific findings verbatim in verification_performed, (2) citing the source URL so reviewers can check, and (3) using multiple adversarial sources that independently reach the same conclusion.

Paths & imports

• Don't inline verification logic: Import the bundled scripts. Rewriting normalize_text() inline risks garbling the HTML-stripping logic.
• Index base mismatches: Economic data from different aggregators may use different base periods. If cross_check() flags a large disagreement, check whether sources use different scaling. Document the base period in source_name.
• PROOF_ENGINE_ROOT uses env-var override with a walk-up fallback (see scripts-api.md for the exact snippet). Resolution order: (1) env var if set — Binder and site-publishing tools set it explicitly; (2) walk up from proof.py's directory until proof-engine/skills/proof-engine/scripts/ is found — makes the published proof portable across any repo clone; (3) raise RuntimeError with a clear message. Do NOT hardcode an absolute path as the fallback — it leaks the generating agent's filesystem and breaks for every other re-runner. Copy the block from the relevant template verbatim; do not collapse, shorten, or replace with __file__ dirname traversal.

Reference Files

Read these on demand, not all upfront.

File	Read when
hardening-rules.md	Step 3 — the 9 rules with bad/good examples
proof-templates.md	Step 3 — read this index to choose a template, then read the specific template file it directs you to
scripts-api.md	Step 3 — exact function signatures and import pattern
research-workflow.md	Step 2 — recency check, academic-paper deep dives, snapshot pre-fetching, quote harvesting, verify-as-you-go
output-specs.md	Step 5 — proof.md, proof_audit.md, and proof_narrative.md structure
self-critique-checklist.md	Step 7 — before presenting results
advanced-patterns.md	When encountering complex quotes or table-sourced data
environment-and-sources.md	When facing fetch failures, paywalls, or .gov 403s

Bundled Scripts

Import these instead of re-implementing verification logic.

Script	Purpose	Key functions
scripts/extract_values.py	Parse values FROM quote strings (Rule 1)	parse_date_from_quote(), parse_number_from_quote(), parse_percentage_from_quote()
scripts/smart_extract.py	Unicode normalization + extraction utilities	normalize_unicode(), verify_extraction(), diagnose_mismatch()
scripts/verify_citations.py	Fetch URLs, verify quotes (Rule 2)	verify_citation(), verify_all_citations(), build_citation_detail(), verify_data_values()
scripts/computations.py	Verified constants, formulas, self-documenting output (Rule 7)	compute_age(), compare(), explain_calc(), cross_check(), compute_percentage_change()
scripts/source_credibility.py	Domain credibility from URL (offline). Called automatically by verify_all_citations().	assess_credibility(url)
scripts/validate_proof.py	Static analysis for rule compliance	ProofValidator(filepath).validate()
scripts/proof_summary.py	Build v3 proof.json summary (primary path)	ProofSummaryBuilder

For exact function signatures, modes, and the standard import pattern, see scripts-api.md. Read at Step 3.

Environment

Full Type B verification requires outbound HTTP from Python. Fallback chain: live fetch -> snapshot -> Wayback Machine (opt-in). Type A proofs run entirely offline.

For environment-specific details (Claude Code, ChatGPT, sandboxed), paywalled sources, and .gov workarounds, see environment-and-sources.md. When a source is paywalled, use snapshot_file (not inline snapshot) to keep copyrighted content out of the committed proof.py — see the "Handling Paywalled Sources" section in that reference.

Core Concepts

Type A facts (Pure): Established entirely by code. The computation IS the verification.

Type B facts (Empirical): Established by citation. Each MUST have: source name, working URL, exact quote. Reputable sources only.

Type S facts (Search): For absence-of-evidence proofs. Each database search is documented with a clickable search_url. The tool confirms the URL is accessible but cannot verify the result count — that's author-reported and reproducible by a human reviewer. This weaker trust boundary is reflected in the SUPPORTED verdict (never PROVED).

Every proof has three parts: (1) Fact Registry — numbered facts tagged Type A, B, or S, (2) Proof Logic — a self-contained Python script, (3) Verdict — one of the levels below.

The 9 Hardening Rules

Rule	Closes failure mode	Enforced by
1. Never hand-type values	LLM misreads dates/numbers from quotes	scripts/extract_values.py
2. Verify citations by fetching	Fabricated quotes/URLs	scripts/verify_citations.py
3. Anchor to system time	LLM wrong about today's date	date.today()
4. Explicit claim interpretation	Silent ambiguity	CLAIM_FORMAL dict with operator_note
5. Independent adversarial check	Confirmation bias	Counter-evidence web searches
6. Independent cross-checks	Shared-variable bugs	Multiple sources parsed separately
7. Never hard-code constants/formulas	LLM misremembers values	scripts/computations.py
8. Evidence relevance for rejection	Weak/off-subject rejection sources	adversarial_checks documentation
9. Prose references mechanically resolvable	Hand-typed attribution hallucinations; raw tokens archived	tools/lib/reference_resolver.py + prose_reference_scan.py + cite-expand

See hardening-rules.md for detailed examples of each.

Workflow

Step 1: Analyze the Claim

Classify: mathematical (Type A), empirical (Type B), or mixed. Identify ambiguous terms. Determine what constitutes proof AND disproof. For compound claims (X AND Y, X BECAUSE Y), decompose into sub-claims. Write a brief proof strategy and share with the user before proceeding.

If the claim is an opinion or has no verifiable answer, do NOT attempt a proof. Offer a related factual claim instead.

Normative claims ("X is valid," "X is ethical," "X is proper scientific practice") that can only be operationalized by counting authorities who agree must be declined or explicitly disclosed as proxy operationalizations in operator_note. If the proof's supporting citations state generic principles (e.g., Popper on falsifiability) without naming the specific subject of the claim, the entailment gap must be disclosed in operator_note and in proof.md's Claim Interpretation section.

Guiding questions:

• Crisp true/false threshold? Extractable facts? Canonical sources? Clear disproof condition?
• If fewer than 3 are true, consider a simpler factual summary instead.
• For consensus claims, see template-qualitative.md.

Step 2: Gather Facts (Both Directions)

Use your environment's web search tool — do not rely on memory for source selection. LLM training data has a cutoff; sources recalled from memory may be outdated. At minimum, run three core searches:

1. The claim itself — authoritative sources that address it directly
2. Recent data — latest benchmarks, studies, or statistics
3. Counter-evidence — sources that contradict, debunk, or criticize the claim ("[claim] debunked", "[claim] wrong", "[claim] criticism")

For the full workflow — recency check, academic-paper deep dives (follow-up authors, broader phenomenon, full-paper read, citation network), real-world demonstrations, snapshot pre-fetching, quote harvesting from rendered pages, and the verify_citation() pre-flight loop — read research-workflow.md.

If web search is unavailable in your environment, note this limitation in the proof audit under adversarial checks and flag that sources may not reflect the latest data.

Find at least two independent sources (Rule 6). For math claims, plan two independent computation approaches.

Adversarial work happens once, here. Use web search for counter-evidence — do not rely on memory. The adversarial_checks list in proof code records what you found — it's documentation of Step 2 research, not code that runs searches at proof execution time. Use past tense in verification_performed (e.g., "Searched for counter-evidence...") to make this clear.

Adversarial sources belong in adversarial_checks, not empirical_facts. Sources that argue against your proof's conclusion should be documented in the adversarial_checks list's verification_performed field. For disproof proofs (proof_direction: "disprove"), the adversarial direction is reversed: search for sources that support the claim — evidence that it might be true. The adversarial question becomes "Is there credible support for the claim I'm disproving?" Only sources that support the proof's conclusion belong in empirical_facts. Exception for disproof proofs: if adversarial investigation produces a finding that is itself a verifiable rejection source — for example, the claim's stated empirical origin is demonstrated to be fabricated or non-existent — include that finding in empirical_facts as a rejection source (subject to citation verification) and document the investigation in adversarial_checks. The finding's dual role does not disqualify it from empirical_facts; the test is whether it supports the proof's conclusion, and for a disproof proof, exposing a fabricated premise is supporting evidence. This prevents adversarial citation failures from degrading the verdict via any_unverified. For contested qualifier claims: sources that reject the qualifier (e.g., "claims not substantiated," "allegations not verified") are adversarial to SC2 — put them in adversarial_checks, not SC2's empirical_facts. It is normal and expected for SC2 to have zero empirical facts when no independent body has confirmed the qualifier.

Do not proceed to Step 3 with fixable not_found or partial citation results — these usually mean the quote was paraphrased and can be corrected. However, fetch_failed from known-unfetchable sources (403, JS-rendered, paywalled) is expected — document these and proceed. The "with unverified citations" verdict exists for exactly this case.

If a quote must be paraphrased (e.g., the verbatim text is unwieldy or the source is paywalled and a snapshot is unavailable), declare "verbatim": False on the empirical_facts entry. The validator will warn and the proof report will note the reduced evidentiary weight. Do not omit this field silently — an undeclared paraphrase is harder to audit than a declared one.

Step 3: Write the Proof Code

Read hardening-rules.md for the 9 rules. Then read proof-templates.md to identify which template matches your claim type. Then read the specific template file it directs you to (e.g., template-qualitative.md, template-compound.md). Do not skip the second read — the index contains only the decision table, not the template code. If the claim uses an epistemic qualifier ("verified," "confirmed," "proven," "established"), use the compound claim template (template-compound.md) with the contested qualifier pattern: SC1 (provenance — do the numbers come from a credible source?) + SC2 (epistemic warrant — has the qualifier been independently confirmed?). If the claim uses causal language ("causes," "leads to," "promotes," "damages," "prevents"), use the compound claim template (template-compound.md) with SC-association + SC-causation sub-claims — see "Causal vs. associational claims" in the Verdicts section. For claims where the primary answer is absence of supporting evidence (and no authoritative sources actively debunk the claim), use template-absence.md. If authoritative sources actively reject the claim — even if absence language also appears — use template-qualitative.md with proof_direction: "disprove"; this produces a DISPROVED verdict rather than the weaker SUPPORTED. The proof script must be self-contained: python proof.py produces the full output.

Required elements:

• CLAIM_FORMAL dict with operator_note (Rule 4)
• empirical_facts dict (empirical proofs) or pure-math template (math proofs)
• compare() for claim evaluation, explain_calc() for computation traces (Rule 7)
• Adversarial checks with verification_performed field (Rule 5)
• Cross-checks from independent sources/methods (Rule 6)
• FACT_REGISTRY mapping report IDs to proof-script keys
• JSON summary block in __main__ ending with === PROOF SUMMARY (JSON) ===

Pre-flight citation check (before Step 4): Run verify_all_citations(empirical_facts, wayback_fallback=True) interactively and inspect the results. Fix any not_found, partial, or fetch_failed entries. Use closest_passage as a diagnostic hint to locate the right region, then copy the visible rendered text from the page. Do not proceed to Step 4 with known citation failures that are fixable.

Formalization fidelity check (before Step 4): Re-read the natural-language claim and CLAIM_FORMAL side by side. Confirm each element of the natural claim is captured in the formal spec. If any aspect is narrowed, excluded, or operationalized by proxy, it must be documented in operator_note and the proof.md Claim Interpretation section's formalization scope note. Flag unresolvable divergences to the user before continuing.

Step 4: Validate

Run python ${CLAUDE_SKILL_DIR}/scripts/validate_proof.py proof_file.py and fix issues.

Step 5: Execute and Report

Run the proof script. Write four files: proof.py, proof.md, proof_audit.md, proof_narrative.md.

For detailed output specifications, see output-specs.md.

proof_narrative.md — Reader-Facing Narrative

The fourth and final output file. Written AFTER proof.py, proof.md, proof_audit.md, and proof.json are complete. This is a presentation of the proof you already built — do not re-derive or reinterpret findings.

Structure (all sections required, use these exact headings):

1. Title line: # Proof Narrative: <claim_natural>
2. Section Verdict — contains **Verdict: <exact verdict from proof.json>** followed by 1-2 sentence hook with verdict-adapted tone
3. Section What Was Claimed? — plain-language restatement, why someone might care, do NOT copy CLAIM_FORMAL
4. Section What Did We Find? — 3-6 paragraphs walking through evidence as a story, not a table. Verdict-adapted: PROVED/DISPROVED = linear strongest-first; SUPPORTED = evidence then gaps; PARTIALLY VERIFIED = what held then what didn't; UNDETERMINED = what was tried and why insufficient
5. Section What Should You Keep In Mind? — mandatory caveats, edge cases, what evidence doesn't address, what surprised, limitations
6. Section How Was This Verified? — 2-3 sentences naming the process, with these links: [the structured proof report](proof.md), [the full verification audit](proof_audit.md), [re-run the proof yourself](proof.py)

Constraints:

• 200-800 words total
• No fact IDs (A1, B1, S1, etc.)
• No jargon — accessible to a general audience
• No tables — prose only
• No CLAIM_FORMAL reproduction
• Purpose-based language with explicit markdown links for formal outputs
• Verdict declaration must use the EXACT full verdict string from proof.json (including qualifiers like "with unverified citations")

Step 6: Citation Recovery

After executing proof.py, check the citation verification output. If any citation has status not_found or partial:

1. Diagnose: Fetch the failing URL with requests.get() (not WebFetch — it returns summaries) and search the raw response text for your attempted quote. Common causes:

   • (a) WebSearch summary was paraphrased, not verbatim — the #1 failure cause
   • (b) Case mismatch (e.g., gsm8k vs GSM8K, PaLM-540b vs PaLMB)
   • (c) LaTeX/Unicode artifacts on academic pages ($\Lambda$CDM, MathML whitespace)
   • (d) Abstract page cited instead of full paper HTML (use ar5iv instead of arxiv.org/abs)
   • (e) Page content has drifted since you last checked — find the current wording

2. Fix the quote: Use closest_passage from the verification result as a diagnostic hint to locate the right region on the page — it shows approximately where your quote might be, but do NOT copy it directly (it uses simplified cleaning that may introduce artifacts). Instead, locate the passage on the rendered page and copy the visible text into empirical_facts. The verifier strips HTML tags and decodes entities before matching, so your quote should be plain text matching what a reader sees. If the page doesn't contain any relevant text (JS-rendered, paywall), try the Wayback archive (wayback_fallback=True) or find an alternative URL for the same source.

3. Re-run: Execute proof.py again and verify the citation now passes.

4. Iterate: Repeat until all citations are verified, or until you've exhausted recovery options. If a citation remains not_found after recovery attempts, document why in proof_audit.md's Citation Verification Details (Impact field).

Do not skip this step. The difference between PROVED and PROVED (with unverified citations) is often a fixable quote mismatch, not a genuinely missing source. A 5-minute recovery loop is almost always worth the verdict upgrade.

Step 7: Self-Critique

Before presenting results, run through the checklist in self-critique-checklist.md.

Publishing

After a proof passes validation, it can be published to the site and archived as a citable research object.

Publish to the site:

python tools/proof-site.py publish <proof-dir> --site-dir site

Mint a Zenodo DOI (archives the proof as a citable research object; requires ZENODO_TOKEN):

# Build the site first so machine-readable artifacts are generated
python tools/build-site.py --site-dir site --output-dir _site --base-url / --site-url https://proofengine.info --design-md docs/DESIGN.md --hardening-rules-md proof-engine/skills/proof-engine/references/hardening-rules.md

# Mint DOI — pass --output-dir to include provenance.json, proof.ipynb, ro-crate-metadata.json
source .env
python tools/proof-site.py mint-doi <slug> --site-dir site --output-dir _site

The deposit includes all five proof artifacts plus, when --output-dir is supplied: the W3C PROV-JSON provenance chain, the Jupyter Notebook (interactive re-verification), and the RO-Crate 1.1 manifest — making the deposit a standards-compliant research object discoverable by Linked Data clients and research registries.

When the notebook is included, doi.json gains a binder_url field (https://mybinder.org/v2/zenodo/{zenodo_id}/?filepath=proof.ipynb) that lets anyone re-run the proof in a browser with one click. The Binder URL is printed to the console after minting.

Verdicts

Verdict	Meaning
PROVED	All facts verified, logic valid, conclusion follows
PROVED (with unverified citations)	Logic valid but some citation URLs couldn't be fetched
SUPPORTED	Absence-of-evidence threshold met, no counter-evidence found
SUPPORTED (with unverified citations)	Absence threshold met but corroborating citations couldn't be fetched
DISPROVED	Verified counterexample or contradiction found
DISPROVED (with unverified citations)	Counterexample found but some citations couldn't be fetched
PARTIALLY VERIFIED	Some sub-claims met threshold, others did not — Conclusion states whether each failing SC lacked evidence or was contradicted
UNDETERMINED	Insufficient evidence either way

Threshold guidance for source-counting proofs: The default threshold: 3 means 3 independently verified sources must confirm the claim. Never set threshold: 1 — a single source is not consensus.

Reducing to threshold: 2 is permitted only when ALL of the following are met:

1. Domain scarcity: Fewer than 3 independent authoritative sources exist. Document the search that established this (databases queried, terms used, why results are insufficient).
2. Source quality adequate for domain: Each threshold source must meet the minimum quality standard for its domain:
   • Human/clinical studies: n >= 30 participants (or justify smaller n for rare conditions)
   • Physical/mathematical: peer-reviewed or established reference (textbook, standards body)
   • Economic/statistical: government or intergovernmental data source, or peer-reviewed analysis
   • Other domains: tier >= 3 credibility (no unclassified or flagged sources)
3. No majority COI: No more than half of the threshold sources may have a conflict of interest (per Rule 6's COI taxonomy: organizational, funding dependency, institutional co-benefit, competitive antagonism, revolving door, or advocacy/ideological) on the same side of the claim.
4. Documented in operator_note: State why 3 sources are unavailable, confirm source quality, and disclose any known COI.

If these conditions are not met, keep threshold: 3. If fewer than 3 qualifying sources exist and the quality gates are not met, the verdict should be UNDETERMINED (insufficient evidence), not PROVED at a lowered threshold.

Causal vs. associational claims: When the claim uses causal language — "causes," "leads to," "promotes," "triggers," "results in," "damages," "prevents" — the proof must decompose it into at least two sub-claims using the compound claim template:

• SC-association: "X is associated with Y" — satisfiable by observational cohorts, cross-sectional studies, epidemiological correlations.
• SC-causation: "The association is causal (not confounded)" — satisfiable by:
  • RCTs or controlled experiments (gold standard)
  • Established causal inference methods where RCTs are impractical: Bradford Hill criteria assessment, Mendelian randomization, natural experiments, dose-response with adequate confounding control
  • Converging mechanistic evidence (biological pathway) + longitudinal outcome data from independent studies

Verdict outcomes:

• SC-association holds + SC-causation holds -> PROVED
• SC-association holds + SC-causation fails -> PARTIALLY VERIFIED (association confirmed, causation not established)
• SC-association fails -> use proof_direction: "disprove" in CLAIM_FORMAL, which maps to DISPROVED when disproof sub-claims hold
• Insufficient evidence for SC-association -> UNDETERMINED

The operator_note must NOT redefine a causal claim as associational to avoid this decomposition. This rule does not apply to claims already phrased associationally ("is correlated with," "is associated with").

Limitations

Strong for crisp, auditable, bounded claims; weak for open-ended, normative, or predictive claims. The key limit is formalizable vs fuzzy — a claim works if it decomposes into extractable facts and a clear rule for proof/disproof.

Disproof is often easier (single counterexample suffices). The engine struggles with: deep original mathematics beyond sympy, broad causal inference, competing definitions, and large literature synthesis. Citation verification confirms quote presence, not semantic entailment — the adversarial check (Rule 5) mitigates this.

Edge Cases

Fictitious source attributions: If a claim attributes data to a specific source that doesn't contain that data (e.g., "according to the 1947 British census" when no such census exists), treat it as a compound claim: (SC1) the numeric value is correct, (SC2) the stated source contains it. Prove SC1 from the actual source, and note the attribution error in operator_note and adversarial checks. The verdict reflects both sub-claims.

Partial-period data: If a claim covers a time range but the best sources only cover part of it (e.g., claim says 1994-2023, sources cover 1994-2020), document the gap in operator_note. For cumulative nonnegative totals (e.g., total aid disbursed, cumulative emissions), if the partial-period sum already exceeds the claim's threshold, prove it with a logical extension: "If S₂₀ > T and the quantity is monotonically nondecreasing (cumulative total cannot shrink), then S₂₃ ≥ S₂₀ > T." State the monotonicity assumption explicitly using explain_calc(). This shortcut does NOT apply to averages, rates, percentages, or rolling metrics — for those, the missing-period values could decrease the aggregate, and you must either find full-period sources or return UNDETERMINED with an explanation of what data is missing.

Source doesn't contain claimed constant: If a claim says "per [Source]" but that source doesn't publish the specific constant (e.g., "CODATA values for solar mass" when CODATA doesn't list solar mass), document the substitution in operator_note: which source you actually used, why it's authoritative, and how it relates to the claimed source.

Comparative claims with source-acknowledged uncertainty: When a claim uses a superlative or comparative — "the safest," "the lowest," "the most," "better than" — and the source used to evaluate the comparison explicitly states that the compared values have overlapping uncertainty ranges, confidence intervals, or error bars: set uncertainty_override = True in the verdict section (date/age or numeric template). The verdict will be UNDETERMINED, not PROVED or DISPROVED. Point estimates alone cannot resolve a ranking when the source itself flags that the estimates are not statistically distinguishable. Document in operator_note: "Source [name] states [exact quote about uncertainty overlap]." This applies when the source providing the data also flags the uncertainty, or when the caveat comes from a source analyzing the same underlying dataset.