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 artttj/rapid-stack --skill the-humanizergit clone --depth 1 https://github.com/artttj/rapid-stackWrote 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/artttj/rapid-stack/the-humanizer)<a href="https://agentmods.dev/skills/artttj/rapid-stack/the-humanizer"><img src="https://agentmods.dev/badge/skills/artttj/rapid-stack/the-humanizer/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/artttj/rapid-stack/the-humanizer"><img src="https://agentmods.dev/badge/skills/artttj/rapid-stack/the-humanizer.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 | $0.00106 | $0.07842 |
| Opus 5.5 | $0.00042 | $0.03137 |
| Sonnet 5.5 | $0.00021 | $0.01568 |
| Haiku 4.5 | $0.00011 | $0.00784 |
Grade A, and why
the-humanizer 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 — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.
.-----------.
| ~~ o ~~ |
| ~ (_) ~ | The Humanizer
| ~~ \_/ ~~ | v2.2
| scanning | Crazy Marketer
'-----------'
Changelog
Every time this skill is updated, log the changes below with the date and a brief description. This section must be maintained on every edit.
| Version | Date | Changes |
|---|---|---|
| v2.2 | 2026-05-06 | Pattern refresh. +2 hollow intensifiers (pivotal, enduring) and +8 AI vocabulary words (intricate, underscore/underscores, vibrant, groundbreaking, showcasing, garner, meticulous, surpass). +2 phrase categories: promotional verb upgrades ("stands as", "serves as", "boasts", "features" replacing plain "is/has") and vague unsourced attribution ("industry reports", "observers have cited", "experts argue", "due to its unique characteristics"). +4 structural markers: avoidance of plain "is/are", present-participle attachments for shallow analysis, challenges-then-future content-mill framework, Title-Cased Headers. Sources: Wikipedia "Signs of AI writing" community register, Sean Goedecke's em-dash analysis, Hacker News discussion on AI vocabulary leaking into everyday speech. |
| v2.1 | 2026-03-30 | Weekly pattern refresh. +4 AI vocabulary words (elevate, realm, essentially, certainly). +5 AI phrases & metaphors (not only...but also, here's a breakdown, in the ever-evolving landscape, a testament to, there is a specific kind of [noun] that happens when). +3 LinkedIn-specific structural markers (common-belief-then-counter opener, period-separated word emphasis, self-intro paragraph at post bottom). Sources: LinkedIn feed analysis (15+ posts from user's live feed) + web research (SEJ, Hastewire, WalterWrites, onesecmedia). |
| v2.0 | 2026-03-25 | Major release: The Humanizer is now a universal content reviewer. Auto-detects content type (blog post, LinkedIn post, email, Slack message) and applies channel-specific AI pattern detection, scoring, and rewrite rules automatically. New patterns since v1.0: +16 AI vocabulary words (tapestry, multifaceted, nuanced, foster, cultivate, facilitate, utilize, comprehensive, albeit, whilst, theater, plainly, superpower, empower, journey, reality). +12 AI phrases & metaphors (brutal clarity, lost the plot, painfully clear, blunt honesty, that way you can, with precision, lived experience, launching a new chapter, the energy in the room, laying the groundwork, Here's to [noun]!, will never be the same, that promise becomes reality, ends the era of, the same tension, keeping my hands dirty). +1 new phrase-level category (stacked abstract noun lists). +18 structural markers (three-part parallel structure, colon-list pattern, contrast-based negation constructions, exclamation-point inflation, adverb-stacking pivot formula, declarative simplicity setup, triple rhetorical question hook, self-posed question as transition, declarative reveal pattern, label-colon framework, stat bomb opener, honesty disclaimer, credential stacking opener, definition reframe, punchy orphan closer, tension-colon opener, parenthetical aside for fake candor, standalone hype fragment). New channel-specific detection: LinkedIn (engagement bait closers, vulnerability performance, fake humility, one-line paragraph formatting, tag-and-thank, arrow chains, vulnerability bait hooks, hyperbole openers, negation upgrades). Email (AI greetings, AI closings, corporate filler, fake personalization, hedge language, over-politeness stacking, subject line patterns, buried asks, template structures). Slack (over-formal language, corporate filler, unnecessary hedging, emoji overload, length violations). New channel-specific scoring: Email uses Clarity + Appropriate Tone instead of Reader Value + Domain Credibility. Slack uses Naturalness + Clarity + Brevity. New files: the-humanizer-linkedin.md (standalone), the-humanizer-email.md (standalone). Source: LinkedIn feed analysis of 15+ organic posts. |
| v1.0 | 2026-03-10 | Initial release. Expanded AI vocabulary with 10 new high-signal words. Added "AI phrasing & metaphors" category with 7 entries. Added stacked abstract noun lists detection. Added 10 structural markers: three-part parallel structure, colon-list pattern, contrast-based negation constructions, exclamation-point inflation, adverb-stacking pivot formula, declarative simplicity setup, triple rhetorical question hook, self-posed question as transition, declarative reveal pattern, label-colon framework. Source: LinkedIn feed analysis. |
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 · 485 lines · 106 tokens per session scan A 56a00cd484c5
the-humanizer is a skill published in the GitHub repository artttj/rapid-stack (2 stars, last pushed 3mo ago), licensed MIT. It adds 106 tokens to every session and 7,842 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 2026-08-31.
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