Borrowing it
Nothing to install: this file belongs to tbhb/vale-ai-tells. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tbhb/vale-ai-tells/main/.claude/skills/review-prose-fix/SKILL.mdgit clone --depth 1 https://github.com/tbhb/vale-ai-tellsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tbhb/vale-ai-tells/review-prose-fix)<a href="https://agentmods.dev/skills/tbhb/vale-ai-tells/review-prose-fix"><img src="https://agentmods.dev/badge/skills/tbhb/vale-ai-tells/review-prose-fix/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tbhb/vale-ai-tells/review-prose-fix"><img src="https://agentmods.dev/badge/skills/tbhb/vale-ai-tells/review-prose-fix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.01048 |
| Opus 5 | $0.00013 | $0.00524 |
| Sonnet 5 | $0.00005 | $0.00210 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
review-prose-fix scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review a prose fix
Judge what a copy edit did to a document. You didn't make these edits and you didn't see the findings that prompted them, so read what the text now says rather than what somebody meant to preserve.
The gates already ran. Repeating them wastes the round, and a clean vale run is the premise of this review rather than its subject. Your subject is everything vale can't score.
The repository
$ARGUMENTS
Treat the first word as the repository. A second word, when present, is the target file. Bind them once:
REPO="${ARGUMENTS%% *}"
Gather the inputs
From $REPO, run these before judging anything:
bash .claude/skills/fix-prose/scripts/check-suppressions.sh --difffor what changedbash .claude/skills/fix-prose/scripts/check-suppressions.sh --verifyfor edits outside the targetcatthe target for the result in one piece
The diff is the review. Where it comes back empty, the fixer changed nothing, and that's a finding unless its report said so.
Where the caller passed the fixer's report, read it too. A claim there that the diff doesn't support is a finding in itself.
What to check
Every finding names the exact text and the correction.
Meaning
The document says what it said before.
- Flag any claim that changed. A dropped qualifier, an added hedge, or an absolute turned conditional edits the argument while looking like a reword.
- Flag any number, path, identifier, filename, flag, or proper noun that differs. These are never a style matter.
- Flag precision traded for a clean run. Replacing a dependency name with
the fuzzing dependencyclears a spelling rule and costs the reader the fact. - Flag a clause or sentence that went missing. Deleting text clears a distributional rule and takes content with it.
Language the linter can't score
The rule set names a few figurative verbs and misses the category around them.
- Flag metaphor, idiom, and personification that the edit introduced. Rules don't fight, checks don't bite, and a document doesn't sail anywhere.
- Flag a sentence that got longer without getting clearer. A reword that doubles a sentence to avoid a rule trades a measured problem for one nothing measures.
- Flag register that drifted upward: a plain word swapped for a formal one to clear a rule about a different word entirely.
- Flag anything the document didn't say before. Counts, hedges, praise, and mentions of tools or sessions all belong to nobody.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 101 lines · 26 tokens per session scan A 5061db9a99d1
review-prose-fix is a skill published in the GitHub repository tbhb/vale-ai-tells (93 stars, last pushed 2d ago), licensed MIT. It adds 26 tokens to every session and 1,048 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Remove AI slop from any voice-bearing prose — original posts, threads, articles, long-form, emails, docs, READMEs, marketing copy, bios, scripts. Use when drafting text meant to sound like a specific person or brand, when rewriting text that reads generic, corporate, or AI-generated, or when asked to humanize…
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authenticity-check
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ai-slop-detector
Universal prose audit. Scores writing on TWO axes — AI-Slop (does this read like AI wrote it?) and Comprehension (can a fresh reader follow this?). Use PROACTIVELY as a mandatory final QA pass on ANY prose generated for humans to read — every email (internal or external), proposal, report, status update, blog post…
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