Deslop Verify

deslop-verify

Purpose

Deterministically answer "did this edit break anything?" for a text rewrite. Given the original and the edited version, verify_edit.py asserts hard invariants (fenced/inline code byte-exact, quotations, numbers in both directions, URLs, link targets, identifiers) and measures over-correction (token edit ratio, length band, sentence-rhythm collapse, edit localization against a slop-lint report). It fixes the observed failure of AI-cleanup passes silently paraphrasing figures, softening quotes, flipping negations, and sanding human prose flat — failures no prompt-only humanizer pack checks.

When to use

• "verify / check the edited draft against the original"
• "did the rewrite change any facts, numbers, quotes, code?"
• "was this over-edited?" — any before/after pair, whatever tool or person made the edit (deslop, another humanizer, a human editor)
• As the mandatory post-step of a deslop rewrite, and in CI over golden before/after corpora

When NOT to use

• No before/after pair exists: scoring a single text → slop-lint.
• Producing or fixing a rewrite → deslop (this skill never writes prose).
• Generic file diffing, PR review, plagiarism, or build verification.

Workflow

1. Get both versions on disk (write them to temp files if pasted inline).

2. Run the script:

   python3 "${CLAUDE_SKILL_DIR}/scripts/verify_edit.py" original.md edited.md
   python3 "${CLAUDE_SKILL_DIR}/scripts/verify_edit.py" original.md edited.md --json
   

   Add --lint-report lint.json (output of slop-lint --json on the original) to compute edit localization — the share of changed lines inside flagged spans. Add --strict in CI to make warn-level issues fail too.

3. Read the exit code: 0 hard invariants pass, 1 usage/input error, 2 a hard invariant failed (or any warn under --strict).

4. Report to the user: hard results first (exactly what went missing or was added, with values), then warn-level findings (negation/modality flips with the sentence pair, edit ratio, rhythm collapse), then the honest framing — a pass is surface integrity, never proof that meaning survived.

5. On failure, recommend restoring the exact lost content (or reverting to the original when edit_ratio is high on text that linted clean) — do not silently accept a paraphrase of a figure or quote.

6. Sanity check after any script edit: --self-test must print all PASS.

Output spec

A report with three sections: hard (7 invariants, each pass/FAIL with up to 10 missing/added items), warn (entities proxy, negation parity, length band, edit_ratio, rhythm, localization), and metrics (edit_ratio, length_ratio, word counts). JSON mode emits the same as schema: 1. Success criteria for a well-behaved de-slop edit: all hard invariants pass, edit_ratio ≤ 0.30, length ratio 0.75-1.25, no negation flags, localization ≥ 0.80, and near-zero edit_ratio when the original was clean human text.

Gotchas

• Never claim "meaning preserved". Deterministic checks prove surface integrity only; two texts can pass everything and still differ in meaning. Say so in every report (the script prints this note — keep it).
• The entity check is a capitalized-run heuristic, not NER — warn-level by design; do not promote it to a hard failure.
• Quotes under 5 words are not tracked; scare-quotes would flood the check.
• Intentional edits can legitimately fail invariants (e.g. the user asked to delete a section). Ask whether the loss was intended before declaring the edit broken — the tool reports facts, the user owns intent. Exception: when the edit came from a deslop pass, its fail-closed rule wins — restore the content; never rationalize your own pipeline's failure as intent.
• Heavy table/frontmatter edits inflate edit_ratio without meaning harm.
• High edit ratio on prose that scored clean on slop-lint is the signature of over-correction — the top failure mode of AI-cleanup tools. See references/verification-contract.md for the release-gate numbers.

Pointers

• scripts/verify_edit.py — the checker (--help, --self-test).
• references/verification-contract.md — what each invariant catches, honest limits, and over-correction release gates with evidence.