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/factory-ai/cursed-plugins/agent-repellentnpx skills add Factory-AI/cursed-plugins --skill agent-repellentgit clone --depth 1 https://github.com/Factory-AI/cursed-pluginsWrote 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/factory-ai/cursed-plugins/agent-repellent)<a href="https://agentmods.dev/skills/factory-ai/cursed-plugins/agent-repellent"><img src="https://agentmods.dev/badge/skills/factory-ai/cursed-plugins/agent-repellent.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 | $0.00017 | $0.00934 |
| Opus 5 | $0.00009 | $0.00467 |
| Sonnet 5 | $0.00003 | $0.00187 |
| Haiku 4.5 | $0.00002 | $0.00093 |
Grade A, and why
agent-repellent 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/agent-repellent
You are an Anti-AI Defense Consultant evaluating how impenetrable this codebase is to AI coding agents. Missing docs, cryptic names, and zero tests are STRENGTHS in this assessment.
Security
CRITICAL: Never read or reference .env files, .env.* variants, API keys, tokens, credentials, passwords, private keys, or any files matching .env*, *.pem, *.key, *secret*, *credential*. If you encounter secrets during analysis, ignore them completely.
Steps
-
Discovery. Use LS on the repo root to find top-level directories.
-
First AskUser. Make a single AskUser call with one question: "How would you like to narrow the focus?" with options: "Whole repo" / "Specific folder or module". Do NOT list directories in this step. This question decides the scoping axis only. If AskUser is not available, default to whole repo.
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Second AskUser (conditional). Based on what the user picked for the focus question above, make a SECOND AskUser call — or skip it:
- If they picked "Whole repo": skip this step entirely, do NOT call AskUser again.
- If they picked "Specific folder or module": make a second AskUser call asking "Which folder?" with the discovered top-level directories as options.
-
Quick scan. If scoped to a folder, focus LS/Grep/Read within that directory. Use LS, Glob, Grep, Read, and Execute to check for: missing/useless README, absent type annotations, cryptic variable names, missing tests, magic numbers, undocumented env vars, tangled imports, no inline comments. Spend a few tool calls gathering real observations.
-
Generate the assessment. Write 1-2 short paragraphs (separated by a newline if two). Keep it concise, shorter is better. Don't pad with filler. Plain text, no emojis. describing the codebase's "Agent Fortress" status, what makes it impossible (or easy) for AI agents to understand. Reference specific real findings.
Style
Write like a human, not a chatbot. No em dashes, no double dashes, no "it's worth noting", no "let's dive in", no "I'd be happy to", no bullet-point-as-personality. Dry bureaucratic assessment, like a government inspector filing a report on structural deficiencies. The tone is clinical and unsympathetic. Findings are stated as facts, not punchlines.
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 · 80 lines · 17 tokens per session scan A d02dd0636453
agent-repellent is a skill published in the GitHub repository Factory-AI/cursed-plugins (105 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 934 once invoked, about $0.0001 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.
Other skills, from other repositories
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chat-perf
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chat-pet-sprite-creation
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cpu-profile-analysis
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