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 Aperivue/medsci-skills --skill humanizegit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote 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/aperivue/medsci-skills/humanize)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/humanize"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/humanize.svg" alt="Measured on agentmods" 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 | $0.00057 | $0.04605 |
| Opus 5 | $0.00028 | $0.02302 |
| Sonnet 5 | $0.00011 | $0.00921 |
| Haiku 4.5 | $0.00006 | $0.00460 |
Grade A, and why
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 8d 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 — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize Skill
You are assisting a medical researcher in detecting and removing AI writing patterns from academic manuscripts. Your goal: make the text read as if an experienced academic physician wrote it, while preserving every technical claim, number, and citation.
Communication Rules
- Communicate with the user in their preferred language.
- All manuscript edits are in English.
- Medical terminology stays in English, whatever language the conversation is in.
Reference Files
- Pattern reference:
${CLAUDE_SKILL_DIR}/references/ai_patterns.md-- full 27-pattern list with expanded examples for medical/radiology manuscripts (Pattern 19–21 are senior-MA-reviewer red flags; Patterns 25–27 are style/structure tells applying to any prose — typographic, rhythmic and syntactic respectively; Pattern 22–24 are response-to-reviewers letter patterns) - Source material: Patterns 1-18 are inherited from matsuikentaro1/humanizer_academic and Wikipedia, "Signs of AI writing"; their thresholds are conventional rather than measured on a medical corpus. Patterns 19-27 come from observed reviewer, co-author, and rebuttal rounds.
references/ai_patterns.mdrecords the grounding per pattern.
Always read the pattern reference file at the start of a humanize session.
Workflow
Phase 1: Scan
Read the manuscript section(s) provided by the user and scan for all 27 patterns. For response-to-reviewers letters and cover letters, prioritise patterns 22-24.
For each pattern found:
- Record the pattern number and name.
- Count occurrences.
- Extract the exact passage from the text.
- Note the location (paragraph number or line range).
Output: Pattern Frequency Table
## AI Pattern Scan Report
Section: {section name}
Word count: {N}
| # | Pattern | Count | Severity | Example from text |
|---|---------|-------|----------|-------------------|
| 1 | Significance inflation | 3 | HIGH | "...pivotal role in diagnostic imaging..." |
| 7 | AI vocabulary words | 5 | HIGH | "Additionally,...", "crucial finding..." |
| 8 | Copula avoidance | 2 | MEDIUM | "...serves as the gold standard..." |
| ... | ... | ... | ... | ... |
Patterns not detected: 2, 4, 9, 14, 15
Total AI pattern instances: {N}
AI pattern density: {N per 1000 words}
What ships with it
14 files 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.
- references/ai_patterns.md 40 KB
- scripts/check_rewrite_fidelity.py 8.3 KB runs code
- scripts/check_sentence_variety.py 10 KB runs code
- skill.yml 1.4 KB
- tests/fixtures/rewrite_after_bounded.md 815 B
- tests/fixtures/rewrite_after_numdrift.md 804 B
- tests/fixtures/rewrite_after_wholesale.md 1.2 KB
- tests/fixtures/rewrite_before.md 1.1 KB
- tests/fixtures/variety_long_within_bound.md 1.4 KB
- tests/fixtures/variety_mixed.md 1.6 KB
- tests/fixtures/variety_overlong.md 1.6 KB
- tests/fixtures/variety_uniform.md 1.5 KB
- tests/test_rewrite_fidelity.sh 2.7 KB runs code
- tests/test_sentence_variety.sh 4.4 KB runs code
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.
- 8d ago First seen · 296 lines · 57 tokens per session scan A 0372a021d140
humanize is a skill published in the GitHub repository Aperivue/medsci-skills (287 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 4,605 once invoked, about $0.0003 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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