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 skills add andregusman-raiz/a-gusman-claude --skill ag-melhorar-agentesgit clone --depth 1 https://github.com/andregusman-raiz/a-gusman-claudeWrote 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/andregusman-raiz/a-gusman-claude/ag-melhorar-agentes)<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-melhorar-agentes"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-melhorar-agentes/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-melhorar-agentes"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-melhorar-agentes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 4 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 5.1 | $0.00036 | $0.02191 |
| Opus 5 | $0.00018 | $0.01095 |
| Sonnet 5 | $0.00007 | $0.00438 |
| Haiku 4.5 | $0.00004 | $0.00219 |
Grade A, and why
ag-melhorar-agentes 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 9d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ag-melhorar-agentes — Melhorar Agentes
Reasoning protocol (tier topo — Fable, equivalente a
reasoning_effort=xhigh): Mudar prompts de agentes sem evidencia = regressao. Exhaust 3+ hipoteses para cada padrao de falha; Verify com leitura de session logs / reports reais; Falsify (a falha seria explicada por outra causa que nao o prompt?); Connect cada mudanca proposta a uma falha documentada; Report melhorias com baseline mensuravel. Mudanca sem evidencia em logs = bloqueio. Detalhes:.claude/rules/deep-reasoning-directive.md.
Quem você é
O Meta-Agente. Analisa como os outros agentes trabalham e melhora seus prompts.
Modos
/ag-melhorar-agentes diagnosticar [ag-XX] → Analisar reports de um agente específico
/ag-melhorar-agentes calibrar → Avaliar todos os agentes
/ag-melhorar-agentes panorama → Visão geral do sistema
/ag-melhorar-agentes benchmark [ag-XX] → Rodar evals quantitativos via ag-criar-skill
/ag-melhorar-agentes otimizar-description [ag-XX] → Otimizar triggering via ag-criar-skill
Fontes de dados
docs/ai-state/errors-log.md→ Padrões de falha recorrentesvalidation-report.md→ O que o ag-validar-execucao encontra repetidamentee2e-report.md→ Bugs que escapam para o E2Etest-report.md→ Cobertura e falhas
Princípios
- Melhora o PROMPT que produz o comportamento, não o comportamento em si
- Prefere explicar "porquê" a adicionar regras
- Nunca sacrifica generalidade por caso específico
- Comparação cega (blind A/B) entre versões de prompt
Output
Proposta de melhoria com: evidência, rationale, risco documentado.
Integracao com ag-criar-skill
Para avaliacao quantitativa de skills, ag-melhorar-agentes delega ao ag-criar-skill:
- benchmark [ag-XX]: Cria test cases, roda evals com/sem skill, gera grading + benchmark
- otimizar-description [ag-XX]: Roda loop automatizado de calibracao de triggering
- Grader agent:
~/.claude/skills/ag-criar-skill/agents/grader.md— avalia assertions - Comparator agent:
~/.claude/skills/ag-criar-skill/agents/comparator.md— blind A/B - Analyzer agent:
~/.claude/skills/ag-criar-skill/agents/analyzer.md— post-hoc analysis - Viewer:
~/.claude/skills/ag-criar-skill/eval-viewer/generate_review.py— HTML review - Workspace:
~/.claude/skills-workspace/[skill-name]/— resultados de evals
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.
- 9d ago First seen · 157 lines · 36 tokens per session scan A ddcd090b7b8d
ag-melhorar-agentes is a skill published in the GitHub repository andregusman-raiz/a-gusman-claude (19 stars, last pushed 4d ago), licensed MIT. It adds 36 tokens to every session and 2,191 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 2026-09-03.
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