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 agentmods add skills/borda/ai-rig/humanizernpx skills add Borda/AI-Rig --skill humanizergit clone --depth 1 https://github.com/Borda/AI-RigWrote 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/borda/ai-rig/humanizer)<a href="https://agentmods.dev/skills/borda/ai-rig/humanizer"><img src="https://agentmods.dev/badge/skills/borda/ai-rig/humanizer.svg" alt="Measured on agentmods" 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 | $0.00247 | $0.01487 |
| Opus 5 | $0.00123 | $0.00744 |
| Sonnet 5 | $0.00049 | $0.00297 |
| Haiku 4.5 | $0.00025 | $0.00149 |
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 4d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detect and remove statistical AI-writing fingerprints from human-facing prose before it ships. Grounded in Wikipedia's crowd-sourced AI-detection corpus (Wikipedia:Signs of AI writing) — a maintained list of vocabulary, syntax, and formatting patterns that over-represent in LLM output vs human baseline. Apply as a final pass, not a rewrite-from-scratch: preserve meaning, facts, and structure; only excise the tells.
- text or file path to humanize: optional. Inline text, or a file path (Markdown/plain text) to edit in place.
- check
<file>: read-only mode — report findings without editing. - No argument: humanize the draft already composed earlier in this turn (self-review pass) — only reachable when the model chooses to invoke this skill mid-task; there is no platform hook that guarantees a pre-send interception, so treat this path as best-effort, not a hard gate.
1. Load the target text
- Inline text → work on it directly, no file I/O.
- File path →
Readthe file. - No argument → treat the draft already composed earlier in this turn as the target.
2. Scan against the checklist
Walk the text once per category below; flag every hit before editing anything (report-first, matches check mode output).
Vocabulary — cut or replace with plain equivalent:
| Banned | Plain replacement |
|---|---|
| delve, boasts, testament, underscore(s), showcase, tapestry, intricate/intricacies, meticulous, robust, vibrant, pivotal, crucial, garner, foster(ing), align with, landscape, interplay, enduring, enhance | say the specific thing instead — drop the word, don't swap in another vague one |
| "stands as", "serves as", "marks a", "represents" (as copula dodge) | "is" / "was" |
| "Additionally,", "Moreover,", "It is important to note that" | delete, or state the fact directly |
Syntax — flag and restructure:
- Negative parallelism: "not just X, but Y" / "not X, but Y" / "not only X but also Y" / "X rather than Y" used as a crutch
- Rule-of-three triads used for false comprehensiveness ("fast, reliable, and scalable")
- "-ing" superficial-analysis tails: "highlighting...", "underscoring...", "contributing to..." tacked onto a claim with no source
- Vague attribution / weasel words: "industry reports", "observers", "experts argue", "some critics" with no named source
- Formulaic "Despite its [positives], X faces challenges..." conclusion pattern
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.
- 4d ago First seen · 82 lines · 247 tokens per session scan A aaeff49873d5
humanizer is a skill published in the GitHub repository Borda/AI-Rig (25 stars, last pushed today), licensed Apache-2.0. It adds 247 tokens to every session and 1,487 once invoked, about $0.0012 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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