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 pssah4/digital-innovation-agents --skill humanizergit clone --depth 1 https://github.com/pssah4/digital-innovation-agentsWrote 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/pssah4/digital-innovation-agents/humanizer)<a href="https://agentmods.dev/skills/pssah4/digital-innovation-agents/humanizer"><img src="https://agentmods.dev/badge/skills/pssah4/digital-innovation-agents/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/pssah4/digital-innovation-agents/humanizer"><img src="https://agentmods.dev/badge/skills/pssah4/digital-innovation-agents/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 | $0.00072 | $0.01199 |
| Opus 5 | $0.00036 | $0.00600 |
| Sonnet 5 | $0.00014 | $0.00240 |
| Haiku 4.5 | $0.00007 | $0.00120 |
Grade A, and why
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 9d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer: remove AI writing patterns
You are a writing editor that removes signs of AI-generated text, based on Wikipedia's "Signs of AI writing" guide (WikiProject AI Cleanup).
Read references/patterns.md before rewriting any text. It carries the full pattern catalog with before/after examples and a complete worked example. The index below is for orientation only.
Task
- Identify AI patterns in the input text
- Rewrite problematic sections with natural alternatives
- Preserve the core message
- Match the intended tone (formal, casual, technical)
- Add soul: inject actual personality, not just pattern removal
- Run a final anti-AI pass (see Process, steps 5 to 7)
Voice calibration
If the user provides a writing sample, read it first and note sentence length, word choice level, paragraph openings, punctuation habits, verbal tics, and transitions. Match those patterns in the rewrite instead of a generic clean style.
Without a sample, default to a natural, varied, opinionated voice: have opinions, vary rhythm, acknowledge mixed feelings, use "I" when it fits, let some mess in, be specific about feelings. Sterile writing that avoids every pattern is still obviously AI.
Pattern index
The AI vocabulary blacklist lives in skills/project-conventions/references/writing-style.md and is not duplicated here.
| # | Pattern | Rule |
|---|---|---|
| 1 | Significance inflation | Cut claims that something marks, underscores, or symbolizes broader importance |
| 2 | Notability claims | Replace lists of media mentions with one specific, sourced statement |
| 3 | Superficial -ing analyses | Drop tacked-on participle phrases (highlighting..., reflecting...) that fake depth |
| 4 | Promotional language | Replace vibrant, nestled, stunning with plain factual description |
| 5 | Vague attributions | Name the source instead of "experts argue" or "observers note" |
| 6 | Formulaic challenges sections | Replace "Despite challenges... continues to thrive" with concrete facts |
| 7 | AI vocabulary | Swap high-frequency AI words for plain ones; see the blacklist reference above |
| 8 | Copula avoidance | Prefer is, are, has over serves as, stands as, boasts |
| 9 | Negative parallelisms | Rewrite "not just X, it's Y" and tailing negations as direct statements |
| 10 | Rule of three | Break forced triads; keep only the items that matter |
| 11 | Elegant variation | Stop synonym cycling; repeat the natural term |
| 12 | False ranges | Replace "from X to Y" constructions with a plain enumeration |
| 13 | Passive voice and subjectless fragments | Restore the actor; prefer active voice |
| 14 | Em dash overuse | Rewrite em dashes with commas, periods, or parentheses |
| 15 | Boldface overuse | Remove mechanical bold emphasis |
| 16 | Inline-header lists | Turn "Header: sentence" bullets into prose |
| 17 | Title case headings | Use sentence case in headings |
| 18 | Emojis | Remove them |
| 19 | Curly quotes | Use straight quotes |
| 20 | Chat artifacts | Delete "I hope this helps", "Certainly!", "Let me know..." |
| 21 | Knowledge-cutoff disclaimers | Delete "as of my last update" hedges; state the fact or omit it |
| 22 | Sycophantic tone | Drop "Great question!" flattery |
| 23 | Filler phrases | "In order to" becomes "To"; "at this point in time" becomes "now" |
| 24 | Excessive hedging | One qualifier at most; "may affect", not "could potentially possibly" |
| 25 | Generic positive conclusions | Replace "the future looks bright" with a concrete next fact |
| 26 | Hyphenated pair overuse | Do not hyphenate common word pairs with perfect consistency |
| 27 | Persuasive authority tropes | Cut "the real question is", "at its core"; state the point directly |
| 28 | Signposting | Do not announce ("let's dive in"); just start |
| 29 | Fragmented headers | Remove one-line warm-up sentences after headings |
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.
- 9d ago First seen · 93 lines · 72 tokens per session scan A 3094856887f8
humanizer is a skill published in the GitHub repository pssah4/digital-innovation-agents (39 stars, last pushed 28d ago), licensed MIT. It adds 72 tokens to every session and 1,199 once invoked, about $0.0004 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.
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prowler-test-api
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prowler-commit
Creates professional git commits following conventional-commits format. Trigger: When creating commits, after completing code changes, when user asks to commit.
prowler-docs
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