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/ne11nn/cantos-plugin/ai-detectnpx skills add ne11nn/cantos-plugin --skill ai-detectgit clone --depth 1 https://github.com/ne11nn/cantos-pluginWrote 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/ne11nn/cantos-plugin/ai-detect)<a href="https://agentmods.dev/skills/ne11nn/cantos-plugin/ai-detect"><img src="https://agentmods.dev/badge/skills/ne11nn/cantos-plugin/ai-detect.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 5.1 | $0.00152 | $0.01504 |
| Opus 5 | $0.00076 | $0.00752 |
| Sonnet 5 | $0.00030 | $0.00301 |
| Haiku 4.5 | $0.00015 | $0.00150 |
Grade A, and why
ai-detect 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 5d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: ai-detect
Invocation: /ai-detect <file-path>
Three-phase pipeline: browser scan (Haiku) → deep pattern analysis (Opus) → file updates (Sonnet).
Privacy and Consent (Non-Negotiable)
This skill uploads the full draft text to a third-party website (gptzero.me) and requires the user's own GPTZero login in the browser window. The draft leaves the local machine. Before any upload:
- State plainly that running this skill sends the draft's text to GPTZero (gptzero.me), an external service, and needs the user to be logged in there.
- Ask the user to confirm they want to scan this specific draft externally. This is opt-in — never run it by default.
- Never scan sensitive, confidential, or private text (anything the user hasn't agreed to share externally — personal records, unpublished/embargoed work, client or third-party material) without an explicit go-ahead for that exact text. When in doubt, ask.
- If the user declines, do not open the browser or upload anything. Use the manual fallback below instead.
Manual fallback (when the user declines external scan)
Stay entirely local — no upload:
- Read the draft and apply
references/signs-of-ai-writing.mdby hand, flagging each passage that matches a catalogued pattern. - Rewrite the flagged passages using the
write-like-meskill (if installed) so the prose matches the user's own voice; otherwise rewrite to remove the catalogued tells while preserving citations, statistics, and word limits. - Report which patterns were found and what was changed. Skip Phases 1–3 entirely — they exist only for the external GPTZero pipeline.
Phase 1 — Browser Scan (Haiku)
Only reach this phase after the Privacy and Consent gate above has passed (user explicitly agreed to upload this draft to GPTZero). If consent was not given, stop and run the manual fallback instead.
Spawn a Haiku agent for all browser work. Pass it the file path and content.
The Haiku agent should:
- Read the file at the given path — extract plain text (strip markdown formatting and headers for paste)
- Open a headed (non-headless) Playwright browser via the playwright-cli skill
- Navigate to
gptzero.me. Take a snapshot to check login state - If not logged in: output "Please log in to GPTZero in the browser window, then confirm here." Wait for user confirmation before continuing
- Navigate to the document scanner (Dashboard → Detect). Take a snapshot
- Find and click "Deep Analysis" or "Advanced Analysis" mode if visible. Take a snapshot to confirm
- Locate the text input area. Clear it. Paste the plain text content
- Click the Scan or Analyze button. Poll for results every 5 seconds (max 60 seconds total)
- Once results appear, extract from the DOM:
- Overall AI probability score (%)
- Each AI-flagged sentence + all reason tags + probability score
- Human-rated sentences where AI probability is ≥ 10% only (skip any sentence rated ≥ 90% human — those are clean)
- Save raw results to
/tmp/gptzero-raw-<timestamp>.md:
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.
- 5d ago First seen · 135 lines · 152 tokens per session scan A ca328f1394da
ai-detect is a skill published in the GitHub repository ne11nn/cantos-plugin (1 stars, last pushed yesterday), licensed MIT. It adds 152 tokens to every session and 1,504 once invoked, about $0.0008 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 2026-08-31.
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