Borrowing it
Nothing to install: this file belongs to ThomasHoussin/Claude-Book. 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/ThomasHoussin/Claude-Book/main/.claude/skills/perplexity-improver/SKILL.mdgit clone --depth 1 https://github.com/ThomasHoussin/Claude-BookWrote 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/thomashoussin/claude-book/perplexity-improver)<a href="https://agentmods.dev/skills/thomashoussin/claude-book/perplexity-improver"><img src="https://agentmods.dev/badge/skills/thomashoussin/claude-book/perplexity-improver/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/thomashoussin/claude-book/perplexity-improver"><img src="https://agentmods.dev/badge/skills/thomashoussin/claude-book/perplexity-improver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00031 | $0.00893 |
| Opus 5.5 | $0.00012 | $0.00357 |
| Sonnet 5.5 | $0.00006 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
perplexity-improver 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 1mo 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perplexity Improver Skill
Reduce AI-detectable patterns in chapters by rewriting low-perplexity sentences while preserving narrative integrity.
Quick Start
/perplexity-improver story/chapters/chapitre-05.md
Multiple chapters can be analyzed in one run:
/perplexity-improver story/chapters/chapitre-01.md story/chapters/chapitre-02.md
Performance Warning
⚠️ The analysis script is SLOW (several minutes for model loading and analysis).
- Accumulate several corrections before re-running
- Use at the end of a writing session, not after each edit
When to Use
- After writing a chapter, before final validation
- When perplexity analysis shows a warning (⚠️)
Supported Languages
| Code | Language | Techniques File |
|---|---|---|
fr |
Français | references/rewriting-techniques-fr.md |
en |
English | references/rewriting-techniques-en.md |
Workflow
Phase 1: Analyze Chapter
1. Detect language from chapter content (first 500 characters):
- Identify language code (
fr,en, etc.) - Load corresponding techniques file:
references/rewriting-techniques-{lang}.md - If language not supported → report error and exit
2. Run perplexity analysis from the script directory (required for uv to find dependencies):
cd scripts/detection && uv run python detection.py ../../<path/to/chapter.md>
Important: The uv run command must be executed from scripts/detection/ where the pyproject.toml is located.
3. Extract from output:
- Median perplexity score
- Warning status (median below threshold)
- Suspect rate (percentage of suspect sentences)
- List of suspect sentences sorted by ascending perplexity
Phase 2: Evaluate Need
The script flags sentences using multiple criteria:
- low_perplexity: individually predictable sentence
- low_std: passage with uniform perplexity (no surprises)
- adjacent_low: extended stretch without friction
- low_ppl_density: cumulative boredom signal
- forbidden_word: AI-signal vocabulary
What ships with it
2 files 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.
- 1mo ago First seen · 120 lines · 31 tokens per session scan A 3e967d9b3d4f
perplexity-improver is a skill published in the GitHub repository ThomasHoussin/Claude-Book (129 stars, last pushed 9mo ago), licensed MIT. It adds 31 tokens to every session and 893 once invoked, about $0.0001 per session on Opus 5.5. 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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