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/jpke/cursor-vibe-coding-template/self_improvegit clone --depth 1 https://github.com/jpke/cursor-vibe-coding-templateWhat 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 | $0.00377 | $0.00377 |
| Opus 5 | $0.00188 | $0.00188 |
| Sonnet 5 | $0.00075 | $0.00075 |
| Haiku 4.5 | $0.00038 | $0.00038 |
Grade A, and why
self_improve 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 yesterday.
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.
This is a copy
86% identical to self_improve — 102 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Rule Improvement Triggers
- New code patterns not covered by existing rules
- Repeated implementations across 3+ files
- Common error patterns that could be prevented
- New libraries/tools being used consistently
- Emerging best practices in the codebase
When to Update Rules
Add New Rules When:
- A new technology/pattern is used in 3+ files
- Common bugs could be prevented by a rule
- Code reviews repeatedly mention the same feedback
- New security or performance patterns emerge
Modify Existing Rules When:
- Better examples exist in the codebase
- Additional edge cases are discovered
- Related rules have been updated
- Implementation details have changed
Rule Quality Assurance
- Rules should be actionable and specific
- Examples should come from actual code
- References should be up to date
- Patterns should be consistently enforced
- Cross-reference related rules
Continuous Improvement Process
- Monitor code review comments
- Track common development questions
- Update rules after major refactors
- Add links to relevant documentation
- Maintain links between related rules
Rule Deprecation
- Mark outdated patterns as deprecated
- Remove rules that no longer apply
- Update references to deprecated rules
- Document migration paths for old patterns
Documentation Maintenance
- Keep examples synchronized with code
- Update references to external docs
- Maintain links between related rules
- Document breaking changes
Follow rule_management.mdc for proper rule formatting and structure.
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.
- yesterday First seen · 55 lines · 377 tokens per session scan A 3db34c634295
self_improve is a cursor rule published in the GitHub repository jpke/cursor-vibe-coding-template (16 stars, last pushed 1y ago), licensed MIT. It adds 377 tokens to every session, about $0.0019 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to self_improve, differing in 102 lines, and is treated as a copy.
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