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 humanizerai/agent-skills --skill detect-aigit clone --depth 1 https://github.com/humanizerai/agent-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/humanizerai/agent-skills/detect-ai)<a href="https://agentmods.dev/skills/humanizerai/agent-skills/detect-ai"><img src="https://agentmods.dev/badge/skills/humanizerai/agent-skills/detect-ai.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk 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 | $0.00035 | $0.00554 |
| Opus 5 | $0.00017 | $0.00277 |
| Sonnet 5 | $0.00007 | $0.00111 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
detect-ai 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.
What it actually says
Detect AI Content
Analyze text to determine if it was written by AI using the HumanizerAI API.
How It Works
When the user invokes /detect-ai, you should:
- Extract the text from $ARGUMENTS
- Call the HumanizerAI API to analyze the text
- Present the results in a clear, actionable format
API Call
Make a POST request to https://humanizerai.com/api/v1/detect:
Authorization: Bearer $HUMANIZERAI_API_KEY
Content-Type: application/json
{
"text": "<user's text>"
}
API Response Format
The API returns JSON like this:
{
"score": {
"overall": 82,
"perplexity": 96,
"burstiness": 15,
"readability": 23,
"satPercent": 3,
"simplicity": 35,
"ngramScore": 8,
"averageSentenceLength": 21
},
"wordCount": 82,
"sentenceCount": 4,
"verdict": "ai"
}
IMPORTANT: The main AI score is score.overall (not score directly). This is the score to display to the user.
Present Results Like This
## AI Detection Results
**Score:** [score.overall]/100 ([verdict])
**Words Analyzed:** [wordCount]
### Metrics
- Perplexity: [score.perplexity]
- Burstiness: [score.burstiness]
- Readability: [score.readability]
- N-gram Score: [score.ngramScore]
### Recommendation
[Based on score.overall, suggest whether to humanize]
Score Interpretation (use score.overall)
- 0-20: Human-written content
- 21-40: Likely human, minor AI patterns
- 41-60: Mixed signals, could be either
- 61-80: Likely AI-generated
- 81-100: Highly likely AI-generated
Error Handling
If the API call fails:
- Check if HUMANIZERAI_API_KEY is set
- Suggest the user get an API key at https://humanizerai.com
- Provide the error message for debugging
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 · 90 lines · 35 tokens per session scan A 4722632c7959
detect-ai is a skill published in the GitHub repository humanizerai/agent-skills (42 stars, last pushed 7mo ago), licensed MIT. It adds 35 tokens to every session and 554 once invoked, about $0.0002 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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humanizer-zh
Use when removing AI writing痕迹 from Chinese text to make it sound more natural and human-written.
humanizer
Use when transform AI-generated content into natural, human-sounding writing with proper tone and style. Use when working with humanizer.
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
ensembl-database
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.