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 causify-ai/helpers --skill text.humanizegit clone --depth 1 https://github.com/causify-ai/helpersWrote 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/causify-ai/helpers/text.humanize)<a href="https://agentmods.dev/skills/causify-ai/helpers/text.humanize"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/text.humanize/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/causify-ai/helpers/text.humanize"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/text.humanize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00013 | $0.02380 |
| Opus 5.5 | $0.00005 | $0.00952 |
| Sonnet 5.5 | $0.00003 | $0.00476 |
| Haiku 4.5 | $0.00001 | $0.00238 |
Grade A, and why
text.humanize 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal
- Change text with recognizable AI mannerisms (hedging, dramatic pivots, cliched transitions) and clean up AI-written content that's factually correct but tonally flat
What This Skill Preserves
- Author's opinions, arguments, and claims
- Document structure and organization
- Paragraph and section order
- Core meaning and facts
- Intentional stylistic choices (fragments, unconventional punctuation, etc.)
Workflow
Core Principles
- Edit, don't rewrite: clean up pattern slop, preserve voice, opinions, structure, and meaning
- Apply systematically: check all 19 rules below; skip only if the rule doesn't apply to the text
- Use judgment on conflicts: if a rule break serves the writing (e.g., a well-placed em dash for effect), keep it
- Change nothing else: don't reorder paragraphs, add ideas, rephrase core arguments, or alter facts
Pattern Checklist
Scan for these AI markers before applying detailed rules:
- Overused Words and Jargon
- Grandiose: "vital", "pivotal", "groundbreaking", "robust", "comprehensive", "nuanced", "multifaceted", "transformative"
- AI vocabulary: "delve", "leverage" (verb), "reimagine", "empower", "unpack", "synergy"
- Filler and Hedges
- "It's worth noting that", "It's important to remember", "It should be noted", "Interestingly enough"
- "Something we've observed", "This is where X really shines"
- Theatrical Transitions and Pivots
- Throat-clearing: "Let's dive in", "Let's unpack", "In this article, we'll"
- Dramatic pivots: "But here's the thing", "Here's the catch", "Here's what most people miss"
- Overused connectors: "Furthermore", "Moreover", "Additionally", "In addition to the above"
- Rhetorical openers: "So what does this mean for you?", "Why does this matter?"
- Structural Patterns
- Gift-wrapped endings: "In summary", "In conclusion", "Ultimately", "At the end of the day", "Moving forward"
- Exhaustive lists: 7-10 items when 3-4 would suffice
- Passive voice clusters: 2+ passive sentences in a row
- Corrective antithesis: "Not X. But Y." setup-payoff constructions
- Meta-Language and Vagueness
- Meta-verbs: "highlights", "underscores", "emphasizes", "showcases", "illustrates"
- Vague attribution: "some experts say", "widely covered", "significant attention"
- Copy-paste metaphors: same metaphor repeated word-for-word 3+ times
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 Changed · +4 lines · -2 tokens per session 12a60ad465b9
- 1mo ago First seen · 224 lines · 15 tokens per session scan A aa9b879d573d
text.humanize is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 13 tokens to every session and 2,380 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 2026-09-03.
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