self_improve

self_improve is a cursor rule for Cursor from dutradotdev/mcp-portal-transparencia. It costs 530 tokens per session, scanned A, original, MIT.

Rules for improving Cursor rules, which are instructions that guide how the Cursor coding assistant works in a project.

In plain words
What is it for?
Use it to decide when to add or revise rules based on repeated implementations, common bugs, new libraries, security or performance practices, and test patterns.
Why use it?
It helps identify repeated code patterns, recurring mistakes, and review feedback that could be turned into clearer project rules.

Cursor rule for Cursor

Written for Cursor: installed under .cursor/. Also seen: mentions Cursor.

Good fit Use it to decide when to add or revise rules based on repeated implementations, common bugs, new libraries, security or performance practices, and test patterns.

Compare 6 cursor rules from other repositories ↓
Install with agentmods
npx agentmods add rules/dutradotdev/mcp-portal-transparencia/self_improve
Install

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.

Clone the repo
git clone --depth 1 https://github.com/dutradotdev/mcp-portal-transparencia

Made for: Cursor.

Wrote 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.

agentmods badge for self_improve

README.md
[![agentmods](https://agentmods.dev/badge/rules/dutradotdev/mcp-portal-transparencia/self_improve.svg)](https://agentmods.dev/rules/dutradotdev/mcp-portal-transparencia/self_improve)
Your own site
<a href="https://agentmods.dev/rules/dutradotdev/mcp-portal-transparencia/self_improve"><img src="https://agentmods.dev/badge/rules/dutradotdev/mcp-portal-transparencia/self_improve.svg" alt="Measured on agentmods" height="20"></a>
Per session 530 This file is loaded in full into every session.
When invoked 530 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00530 $0.00530
Opus 5 $0.00265 $0.00265
Sonnet 5 $0.00106 $0.00106
Haiku 4.5 $0.00053 $0.00053

Measured 8d ago against content hash b1ad380d15cc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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 8d 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.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

.cursor/rules/self_improve.mdc · 73 lines

What it actually says

  • Rule Improvement Triggers:

    • New code patterns not covered by existing rules
    • Repeated similar implementations across files
    • Common error patterns that could be prevented
    • New libraries or tools being used consistently
    • Emerging best practices in the codebase
  • Analysis Process:

    • Compare new code with existing rules
    • Identify patterns that should be standardized
    • Look for references to external documentation
    • Check for consistent error handling patterns
    • Monitor test patterns and coverage
  • Rule Updates:

    • 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
  • Example Pattern Recognition:

    // If you see repeated patterns like:
    const data = await prisma.user.findMany({
      select: { id: true, email: true },
      where: { status: 'ACTIVE' }
    });
    
    // Consider adding to [prisma.mdc](mdc:.cursor/rules/prisma.mdc):
    // - Standard select fields
    // - Common where conditions
    // - Performance optimization patterns
    
  • Rule Quality Checks:

    • Rules should be actionable and specific
    • Examples should come from actual code
    • References should be up to date
    • Patterns should be consistently enforced
  • Continuous Improvement:

    • Monitor code review comments
    • Track common development questions
    • Update rules after major refactors
    • Add links to relevant documentation
    • Cross-reference 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 Updates:

    • Keep examples synchronized with code
    • Update references to external docs
    • Maintain links between related rules
    • Document breaking changes Follow cursor_rules.mdc for proper rule formatting and structure.
Changes

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.

  1. 8d ago First seen · 73 lines · 530 tokens per session scan A b1ad380d15cc

Subscribe to this mod's changes

self_improve is a cursor rule published in the GitHub repository dutradotdev/mcp-portal-transparencia (223 stars, last pushed 1y ago), licensed MIT. It adds 530 tokens to every session, about $0.0027 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.