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 rules/csoai-org/bias-detection-mcp/cursorrulesgit clone --depth 1 https://github.com/CSOAI-ORG/bias-detection-mcpWrote 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/rules/csoai-org/bias-detection-mcp/cursorrules)<a href="https://agentmods.dev/rules/csoai-org/bias-detection-mcp/cursorrules"><img src="https://agentmods.dev/badge/rules/csoai-org/bias-detection-mcp/cursorrules.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 | $0.00172 | $0.00172 |
| Opus 5 | $0.00086 | $0.00086 |
| Sonnet 5 | $0.00034 | $0.00034 |
| Haiku 4.5 | $0.00017 | $0.00017 |
Grade A, and why
cursorrules 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 3d 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
Bias Detection MCP - Auto-trigger Rules
When the user asks about AI bias, fairness assessment, demographic parity, disparate impact, protected attributes, or model fairness metrics, use bias-detection-mcp tools:
- quick_scan: Instant bias risk assessment (one sentence)
- detect_bias: Full bias analysis across protected classes
- fairness_metrics: Compute demographic parity, equal opportunity, predictive parity
- mitigation_recommendations: Action plan to reduce identified bias
- regulatory_check: Map findings to EU AI Act Article 10, GDPR Article 22, NYC Local Law 144
Install: pip install bias-detection-mcp
Bias detection is mandatory for high-risk AI systems under EU AI Act Article 10 (data governance) and Article 15 (accuracy/robustness).
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.
- 3d ago First seen · 14 lines · 172 tokens per session scan A 4948c01dd5cb
cursorrules is a cursor rule published in the GitHub repository CSOAI-ORG/bias-detection-mcp (0 stars, last pushed 2mo ago), licensed MIT. It adds 172 tokens to every session, about $0.0009 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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When the user asks about api docs generator, use api-docs-generator-ai-mcp tools: generateendpoint, generateschema, generatefullspec, addauthtospec, validatespec.