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/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-extractgit clone --depth 1 https://github.com/yunbow/ai-dev-os-plugin-cursorWrote 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/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-extract)<a href="https://agentmods.dev/rules/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-extract"><img src="https://agentmods.dev/badge/rules/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-extract.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 | $0.00037 | $0.00362 |
| Opus 5 | $0.00018 | $0.00181 |
| Sonnet 5 | $0.00007 | $0.00072 |
| Haiku 4.5 | $0.00004 | $0.00036 |
Grade A, and why
ai-dev-os-extract 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
AI Dev OS Rule Harvesting
Execution Flow
1. Get Target File Diff
Retrieve the diff between AI-generated code (before) and corrected code (after) using git diff.
2. Analyze the Diff
Infer "why it was changed" rather than "what changed". Classify patterns:
- Naming convention violation → common/naming.md
- Security gap → common/security.md
- Architecture deviation → frameworks/*/overview.md
3. Generate Rule Candidates
- One-line rule format: "MUST: Always call auth() in server actions"
- Check for duplicates against existing guidelines
4. User Confirmation
- "Would you like to add the following rules?"
- Suggest the target file (L3 guideline) for each rule
- Suggest links to L2 principles
5. Append to Guideline Files
- Include traceability comments
- Record extraction date and rationale
Output Example
## Rule Extraction Results
### Detected Patterns (N rules extracted)
#### Rule 1: [Rule Name]
- **From**: [File name] (line number)
- **Pattern**: [Detected pattern]
- **Proposed Rule**: `MUST: [Rule content]`
- **Target**: [Target guideline file]
- **Traces to**: [L2 principle reference]
### Add these rules? [Y/n/edit]
Language
Respond in the same language as the project's .cursorrules file.
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 · 54 lines · 37 tokens per session scan A f8a9283dcf60
ai-dev-os-extract is a cursor rule published in the GitHub repository yunbow/ai-dev-os-plugin-cursor (2 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 362 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 2026-08-31.
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