Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nicknisi/claude-plugins --skill tighten-prosegit clone --depth 1 https://github.com/nicknisi/claude-pluginsWrote 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/nicknisi/claude-plugins/tighten-prose)<a href="https://agentmods.dev/skills/nicknisi/claude-plugins/tighten-prose"><img src="https://agentmods.dev/badge/skills/nicknisi/claude-plugins/tighten-prose/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/nicknisi/claude-plugins/tighten-prose"><img src="https://agentmods.dev/badge/skills/nicknisi/claude-plugins/tighten-prose.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 | $0.00088 | $0.01427 |
| Opus 5.5 | $0.00035 | $0.00571 |
| Sonnet 5.5 | $0.00018 | $0.00285 |
| Haiku 4.5 | $0.00009 | $0.00143 |
Grade A, and why
tighten-prose 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 2mo 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tighten
A final editing pass that catches and removes AI-detectable writing patterns. Not a detector — an editor. The output reads like a human wrote it because, after this pass, a human shaped every sentence.
What this pass cannot see
This skill scans vocabulary and sentence-level habits. It is blind to structure, and structure is where modern AI prose gives itself away.
A ghost-written blog post once passed this gate with zero findings — no AI vocabulary, em-dash rate below the author's own baseline — while ending eight of eight sections on an aphorism, using six "Not X. That's Y." antitheses, and asserting nine invented claims about the author's inner life. Readers called it slop immediately. The gate had certified its own blind spot.
If you are tightening a draft written for someone else in their voice, run a
corpus-calibrated structural check first — see
plugins/content/skills/blog-post-writer/scripts/slopcheck.py. Two rules that pass
list can't encode:
- Slop is a rate, not a presence. Real writers use every device on the list below. The tell is one firing on every section instead of once a post. Thresholds have to be measured against the author's real corpus.
- Check both directions. A metric sitting far below an author's baseline means someone wrote to the metric. That's as suspicious as excess.
Run this skill last and weight it least.
When to Run
- After completing any prose-heavy output (blog posts, documentation, emails, talks)
- When another skill requests a tighten pass as a quality gate
- When the user asks to clean up or improve writing
- Before shipping anything where "sounds like AI" would undermine credibility
Process
1. Load the Tells Reference
Read references/ai-tells.md for the full catalog of patterns, organized by category with examples and fix strategies.
2. Fetch Live Patterns
AI writing tells evolve as models change. Fetch the current vocabulary list from Wikipedia:
https://en.wikipedia.org/w/api.php?action=parse&page=Wikipedia:Signs_of_AI_writing&prop=wikitext&format=json
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.
- 2mo ago First seen · 130 lines · 88 tokens per session scan A 91c02d8075eb
tighten-prose is a skill published in the GitHub repository nicknisi/claude-plugins (114 stars, last pushed 2mo ago), licensed MIT. It adds 88 tokens to every session and 1,427 once invoked, about $0.0004 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.
tighten-prose compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| tighten-prosenicknisi/claude-plugins | Skill | 114 | 2mo ago | MIT | A | 88 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |