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/codex-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/codex-review-prose-fix)<a href="https://agentmods.dev/skills/tbhb/vale-ai-tells/codex-review-prose-fix"><img src="https://agentmods.dev/badge/skills/tbhb/vale-ai-tells/codex-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/codex-review-prose-fix"><img src="https://agentmods.dev/badge/skills/tbhb/vale-ai-tells/codex-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 | $0.00062 | $0.00328 |
| Opus 5 | $0.00031 | $0.00164 |
| Sonnet 5 | $0.00012 | $0.00066 |
| Haiku 4.5 | $0.00006 | $0.00033 |
Grade A, and why
codex-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.
What it actually says
Review a prose fix
The caller supplies the repository root, target, and workflow state. Work read-only. Run:
bash .agents/skills/codex-fix-prose/scripts/check-suppressions.sh --diff <state> <target>
bash .agents/skills/codex-fix-prose/scripts/check-suppressions.sh --verify <state> <target>
Read the resulting target in full. Don't repeat the prose lint command. Its clean result is the premise of this review.
Flag any changed claim, qualifier, number, path, identifier, proper noun, or omitted content. Flag precision traded for generic wording, new metaphor or idiom, inflated register, unnecessary length, and any new count, hedge, praise, or provenance. A mechanical linter-config-changed, suppression-comment, or stray-edit result always requires changes. Leave a linter finding uncleared only when preserving correct meaning requires it.
Sweep the whole diff for every class of issue you find, then return exactly:
REVIEW: CLEAN
or:
REVIEW: CHANGES REQUIRED
1. `<exact text>` — <problem>. Fix: <specific correction>.
Don't edit any file.
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 · 35 lines · 62 tokens per session scan A 318b98bc5e74
codex-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 62 tokens to every session and 328 once invoked, about $0.0003 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.
Other skills, from other repositories
anti-slop
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…
kill-slop
Audit a file or draft for AI slop and off-voice lines against the author's bound voice, report findings with in-voice swaps, then apply approved fixes. Use when the user says "kill slop", "/kill-slop [file]", "find the slop in this", "audit this for slop", or wants a deck, article, page, or draft cleaned to sound like…
humanizer
Humanize and de-slop AI-sounding prose while preserving meaning, specific detail, and genuine human quirks. When a voice sample or style profile is available, rewrite in that writer's actual voice. Remove recurring AI tells such as inflated significance, stock vocabulary, uniform rhythm, excessive hedging, formulaic…
voice-critic
Skill "voice-critic" from petar-djukic/writing-skills, covering voice critic (read-only gatekeeper), the five dimensions, snark scale and hard rules (constitution §4), judged vs computed, and the judge's leash and the unhedged-prediction work list (for inject-vernacular).
authenticity-check
Score how authentically text reads as a real human author's work, flag spans that read as AI-generated, AI-templated, or generically derivative, and run a separate read-only scan for suspicious Unicode provenance carriers. Return an authenticity band, a 0-100 score, provenance signals, and span-level reasons. Use when…
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…