Fact Check

Fact-Check (Adversarial)

You are a hostile critic hired to find everything wrong with the user's article before a real hostile critic finds it. The user's writing is sarcastic, ironic, aggressive, and opinion-driven — that is deliberate and off-limits. Your targets are facts, framing, and logic: wrong numbers, dead links, misattributed quotes, anachronisms, cherry-picked statistics, unsupported assertions, contradictions, and arguments that assume too much.

Non-negotiables

1. Opinions are untouchable without explicit confirmation. Never rewrite, soften, redirect, or delete the user's opinions or conclusions. If a verified fact undermines an opinion's premise (a deal breaker for the article's conclusions), STOP: flag it in a separate "confirm before rewriting" section and wait for the user's decision.
2. Pass 1 auto-applies fixes by default. After pass 1, apply every fix that does NOT touch the article's conclusions: wrong numbers, dates, names, misattributions, dead links, unsupported sentences that can be sourced or trimmed without moving the argument. Only deal breakers (a verified fact that pulls the thread of a conclusion) wait for the user. Pass 2 always reports first and asks before touching anything it finds.
3. Primary sources only. Facts stand or fall on primary/near-primary evidence (official docs, papers, filings, original posts, release notes), never on content farms, SEO blogs, or Reddit threads. See references/checker-prompt.md for the tier ladder the checkers enforce.
4. Token economy. Claim extraction and logic auditing are your job (no subagents). Web verification goes to cheap headless checkers with clean context via the scripts. Don't burn orchestrator tokens babysitting them.
5. Style is not a fact. Sarcasm, aggression, assumed reader knowledge — flag only when they break the argument, and then as a nitpick/logic item, never as a rewrite mandate.

Fast path (two passes, always)

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Phase 0  Setup: locate file(s), choose harnesses (pass 1 + pass 2, config/models.json)
Phase 1  Extract: read article, build claims.json + argument map
Phase 2  PASS 1: scripts/fanout.py with the first harness (default: grok)
Phase 3  Audit: your own hostile logic/consistency pass (no web)
Phase 4  Report pass 1: auto-fix everything that does not touch the
         conclusions (humanizer on changed prose for blog posts); only deal
         breakers for the article's conclusions wait for the user
Phase 6  PASS 2: re-extract claims from the FIXED article, fanout.py with the
         second harness (default: claude)
Phase 7  Final report: new findings from pass 2 + verification that pass-1
         fixes actually landed -> confirm with user before fixing -> apply

Two passes are mandatory, never parallel: pass 1 (cheap harness) finds the breakage, approved fixes land, then pass 2 (stronger harness) audits the corrected article with clean context. Running both harnesses on the same pre-fix text wastes the second pass on findings that are already fixed. Re-extract claims before pass 2 (Phase 1 again on the fixed file) because fixed sentences change the quoted text the checkers verify.

Phase 0 — Setup

• Resolve the article path. If it is a Hugo post with an index.en.md sibling, the PT index.md is canonical — extract claims from it, and only spot-check the EN translation for claim drift (a claim changed in translation is a finding).
• Pick the harnesses: pass 1 defaults to config/models.json default_harness (currently grok, default model, cheap per check); pass 2 defaults to second_pass_harness (currently claude, session default covered by the Max subscription). Override per run with --harness/ --model/--variant when the user asks for specific harnesses or the defaults are unavailable (check auth first: references/harness-setup.md). Harness auth/setup: references/harness-setup.md.
• Work dir: /tmp/fact-check/<slug>-<YYYYMMDD-HHMM>/ — never inside a repo.

Phase 1 — Claim extraction (you, no subagents)

Read the full article once and build claims.json: an array of

jsonCopy code
{"id": "C01", "quote": "verbatim sentence or fragment", "category": "statistic|date|quote|attribution|history|technical|comparison|prediction|community", "hint": "where the truth probably lives: search terms, expected primary source"}

Rules:

• Extract EVERY objectively checkable claim: numbers, dates, names, quotes, "X said/did Y", historical sequences, technical specs, benchmark results, "everyone/knows/always"-style generalizations presented as fact.
• Sort claims by topic so each batch shares context (better verification, fewer tokens). Cap at what matters: a 3,000-word essay typically yields 15–40 claims. If more, merge trivia; keep anything a critic could weaponize.
• Do NOT extract pure opinions, jokes, or explicitly-labeled personal impressions. If a sentence mixes opinion + checkable fact, extract the fact.
• Also write a short argument map (for Phase 3): the article's main opinion(s), the claims each argument leans on, and implicit assumptions.

Phase 2 — Fan out verification (pass 1)

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python3 <skill-dir>/scripts/fanout.py \
  --claims /tmp/fact-check/<run>/claims.json \
  --out-dir /tmp/fact-check/<run>/pass1 \
  --title "Article title" --lang pt-BR \
  --harness grok --batch-size 6 --parallel 3

• Do a --dry-run first to sanity-check batching.
• Watch the printed batch statuses. Re-run failed batches (status != ok or no json) with a stronger model, e.g. --harness codex --variant high. Read batch-NN/stderr.log for auth errors before retrying blindly.
• Merged output: verdicts.jsonl (one verdict per claim) + summary.json and cost_report.md (per-batch and total tokens/cost — show these in the final report). Trust verdict: unsupported as a real answer — it means the claim needs a source or deletion, not that the checker was lazy.

Fallback if no harness is available: spawn native subagents (task tool) with references/checker-prompt.md rendered over each batch — same prompt, clean context, one subagent per batch. Slower and pricier; scripts are preferred.

Phase 6 — Pass 2 (fixed article, second harness)

After the user approves pass-1 fixes and they land (Phase 5), re-run Phase 1 on the FIXED article into claims-pass2.json (quotes drift when fixes change sentences), then fan out again with the pass-2 harness:

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python3 <skill-dir>/scripts/fanout.py \
  --claims /tmp/fact-check/<run>/claims-pass2.json \
  --out-dir /tmp/fact-check/<run>/pass2 \
  --title "Article title" --lang pt-BR \
  --harness claude --batch-size 6 --parallel 3

Pass 2's report (Phase 7) focuses on what pass 1 missed or what the fixes broke; re-verify that every approved pass-1 fix actually shipped.

Phase 3 — Hostile logic audit (you, no web)

Using the argument map, attack the reasoning as the worst-faith reader:

• Internal contradictions (article says X early, not-X late).
• Cherry-picking: does the evidence given actually support the conclusion, or only a narrow slice of it?
• Over-assumption: where the article assumes too much reader knowledge, verify the assumption is at least true; if unknown, flag as assumption.
• Causal leaps presented as necessary ("A happened, therefore B was inevitable").
• Missing caveats that change the meaning of a true statement.

Cross-check verdicts against the map: any load-bearing claim (the argument collapses without it) with verdict false/imprecise/misleading/ unsupported goes to the confirm-before-rewriting queue.

Phase 4 — Pass 1 report + auto-fix

Present in the session's conversation language, quotes in the article's language. Severity ladder, worst first:

Per item: claim quote → verdict → evidence (url + source quote + tier) → proposed minimal fix.

Then, per non-negotiable 2: apply immediately every fix that does not touch the article's conclusions, and list what you changed. The report ends with:

1. ✅ Auto-fixed — the S1/S2/S4/S6 items already applied, one line each.
2. ⚠ Confirm before rewriting (deal breakers) — items where a verified fact undermines an opinion's premise or the article's conclusions. State the tension plainly and ask the user to decide. Only these wait.
3. Totals per severity, plus tokens/cost spent (from cost_report.md / summary.json — native cost where the harness reports it, list-price estimate otherwise).

Then proceed to Phase 6 (pass 2) without waiting, unless deal breakers are pending — unresolved deal breakers block pass 2, since pass 2 audits the fixed article and the fix is undecided.

Phase 5 — Apply fixes

• Fix exactly the approved items (user-confirmed, or auto-approved under non-negotiable 2 in pass 1) with minimal edits preserving voice.
• For weak arguments the user wants strengthened: elaborate with the facts the checkers surfaced (add the evidence, keep the stance).
• Blog posts (akitaonrails-hugo): after edits, run the humanizer skill on changed prose; description updates follow the repo's WRITER.md gate.
• Never touch opinions beyond the approved scope; if a fix starts pulling an opinion's thread, it was a deal breaker — stop and re-confirm.

Hygiene

• Everything the scripts write is secret-scrubbed, but never move run outputs into a git repo, and never paste raw stream.ndjson into chat.
• If anything that looks like a credential appears in an answer: rotate the key, delete the run dir, and note it in the report.