detect-ai — Claude Code skill: what it does, how to install
Analyzes text using the HumanizerAI API to spot machine-generated content and returns a score from 0 to 100 with detailed metrics.
npx skills add humanizerai/agent-skills --skill detect-ai -a claude-codeWhat detect-ai does
The skill captures text from slash command arguments and evaluates it through HumanizerAI via the WebFetch tool. The main metric reported back is score.overall. A score from 0 to 20 points to human writing, while 81 to 100 indicates machine generation.
Alongside the final verdict, the output breaks down word count, sentence length, perplexity, burstiness, readability, and n-gram patterns. It also suggests whether the inspected piece needs rewriting before publication.
The skill will fail immediately without a valid HUMANIZERAI_API_KEY environment variable. You must configure this key before testing texts.
What it helps with
- check drafted articles for AI patterns before publishing
- inspect perplexity and burstiness metrics for given paragraphs
- get an overall detection score from 0 to 100
- decide whether text needs manual editing based on the verdict
Example prompts for the agent
- /detect-ai Paste the draft documentation section here to check it
- /detect-ai Analyze this blog intro for AI generated phrasing
How to install detect-ai
1Skills CLI (Vercel)
npx skills add humanizerai/agent-skills --skill detect-ai -a claude-code2Manually: put the files in place
~/.claude/skills/detect-ai/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/detect-ai/
1Skills CLI (Vercel)
npx skills add humanizerai/agent-skills --skill detect-ai -a codex2Manually: put the files in place
.agents/skills/detect-ai/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add humanizerai/agent-skills --skill detect-ai -a cursor2Manually: put the files in place
.cursor/skills/detect-ai/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install humanizerai/agent-skills detect-ai2Manually: put the files in place
.github/skills/detect-ai/SKILL.md1Skills CLI (Vercel)
npx skills add humanizerai/agent-skills --skill detect-ai -a gemini-cli2Platform CLI
gemini skills install https://github.com/humanizerai/agent-skills --path skills/detect-aiManually: put the files in place
.devin/skills/detect-ai/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add humanizerai/agent-skills --skill detect-ai -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about detect-ai
How to install detect-ai in Claude Code?
Run npx skills add humanizerai/agent-skills --skill detect-ai -a claude-code in your terminal. This sets up the skill under ~/.claude/skills/detect-ai/SKILL.md.
What does detect-ai do?
It inspects submitted text via HumanizerAI, providing a 0 to 100 detection rating and metrics like sentence counts and perplexity.
What are the requirements for detect-ai?
It requires an active HUMANIZERAI_API_KEY set in your environment and access to the WebFetch tool.
Source text
SKILL.md · MIT
---
name: detect-ai
description: Analyze text to detect if it was written by AI. Returns a score from 0-100 with detailed metrics. Use when checking content before publishing or submitting.
user-invocable: true
argument-hint: [text to analyze]
allowed-tools: WebFetch
---
# 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:
1. Extract the text from $ARGUMENTS
2. Call the HumanizerAI API to analyze the text
3. 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:
```json
{
"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:
1. Check if HUMANIZERAI_API_KEY is set
2. Suggest the user get an API key at https://humanizerai.com
3. Provide the error message for debugging
© humanizerai · MIT · humanizerai/agent-skills