ag-depurar-erro

ag-depurar-erro is a skill for Claude Code from andregusman-raiz/a-gusman-claude. It costs 60 tokens per session (2,286 once invoked), scanned B, original, MIT.

A Portuguese-language debugging skill that investigates and fixes software errors using competing hypotheses, verification and root-cause analysis.

In plain words
What is it for?
Use it when an application breaks, hangs, reports errors, fails to build or behaves differently than expected.
Why use it?
It prevents repeated failed attempts by checking the project's error log before diagnosing an unexpected behaviour.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: model in frontmatter; mentions subagents; mentions Codex.

Good fit Use it when an application breaks, hangs, reports errors, fails to build or behaves differently than expected.

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Install with agentmods
npx agentmods add skills/andregusman-raiz/a-gusman-claude/ag-depurar-erro
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.

Any agent
npx skills add andregusman-raiz/a-gusman-claude --skill ag-depurar-erro
Clone the repo
git clone --depth 1 https://github.com/andregusman-raiz/a-gusman-claude

Made for: Claude Code.

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 ag-depurar-erro

README.md
[![agentmods](https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-depurar-erro/github.svg)](https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-depurar-erro)
Your own site
<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-depurar-erro"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-depurar-erro/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.

agentmods 80×15 button for ag-depurar-erro

Your own site · 80×15
<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-depurar-erro"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-depurar-erro.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,286 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 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 $0.00060 $0.02286
Opus 5 $0.00030 $0.01143
Sonnet 5 $0.00012 $0.00457
Haiku 4.5 $0.00006 $0.00229

Measured 12d ago against content hash ce2ecf3fb146, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade B, and why

ag-depurar-erro scanned grade B with 2 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 12d 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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

> **Reasoning protocol (tier topo — Fable, equivalente a `reasoning_effort=xhigh` do Codex)**: ANTES de propor diagnostico, Exhaust 3+ hipoteses concorrentes → Verify cada uma com Read/Bash/grep → Falsify (o que refutari

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

[ ] curl -I <url> → headers mostram versao/timestamp atualizado?
skills/ag-depurar-erro/SKILL.md · 215 lines

How it starts

The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ag-depurar-erro — Depurar Erro

Reasoning protocol (tier topo — Fable, equivalente a reasoning_effort=xhigh do Codex): ANTES de propor diagnostico, Exhaust 3+ hipoteses concorrentes → Verify cada uma com Read/Bash/grep → Falsify (o que refutaria?) → Connect cadeia causal sintoma→causa→fix → Report com confianca explicita (alta/media/baixa). Detalhes: .claude/rules/deep-reasoning-directive.md. NUNCA aceitar primeira hipotese plausivel sem listar concorrentes.

Spawn the ag-depurar-erro agent to diagnose and fix bugs using root cause analysis.

Invocation

Use the Agent tool with:

  • subagent_type: ag-depurar-erro
  • mode: bypassPermissions
  • run_in_background: true
  • prompt: Compose from template below + $ARGUMENTS

Dynamic Context

  • Errors known: !head -30 docs/ai-state/errors-log.md 2>/dev/null || echo "none"

Prompt Template

Projeto: [CWD or user-provided path]
Erro: [error message or description from $ARGUMENTS]
Contexto: [when it happens, what changed, environment]

Fluxo: Reproduzir → Isolar → Diagnosticar → Corrigir → Verificar.
- Ler errors-log.md ANTES de comecar (nao repetir tentativas falhadas)
- Encontrar causa raiz, NAO apenas sintoma
- Verificar SEMPRE se existem multiplas causas independentes
- Max 2 tentativas de fix por causa — escalar ao usuario se nao resolver
- Registrar diagnostico e resolucao em errors-log.md

Important

  • ALWAYS spawn as Agent subagent — do NOT execute inline
  • After spawning, confirm to the user that the debug agent is running
  • Reads errors-log.md to avoid repeating failed attempts
  • Follows root-cause-debugging.md protocol

Decision Tree: Classifying the Bug

BUG REPORTADO
├── Fix aplicado mas bug persiste? → DEPLOY GAP
│   ├── Verificar: build atual reflete o codigo?
│   ├── Verificar: cache (CDN, browser, server) invalidado?
│   ├── Verificar: processo reiniciado apos deploy?
│   └── Verificar: deploy foi para o ambiente correto (prod vs staging)?
│
├── Bug aparece em camadas diferentes? → MULTI-LAYER BUG
│   ├── Frontend: componente renderiza dado errado?
│   ├── Backend: API retorna valor incorreto?
│   ├── Config: variavel de ambiente ausente ou errada?
│   └── → Rastrear da UI ate banco — corrigir TODAS as camadas
│
├── Funciona para alguns users mas nao outros? → PERMISSION/ACL BUG
│   ├── RLS policy inconsistente entre tabelas relacionadas?
│   ├── Role/claim ausente no JWT?
│   ├── Middleware valida permissao diferente da query?
│   └── → Auditar toda a cadeia: JWT → middleware → RLS → query
│
└── Bug unico e isolado? → SINGLE-CAUSE BUG
    └── Fluxo padrao: Reproduzir → Root Cause → Fix → Verificar

Read the full file on GitHub · 215 lines

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. 12d ago First seen · 215 lines · 60 tokens per session scan B ce2ecf3fb146

Subscribe to this mod's changes

ag-depurar-erro is a skill published in the GitHub repository andregusman-raiz/a-gusman-claude (19 stars, last pushed 3d ago), licensed MIT. It adds 60 tokens to every session and 2,286 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (reads agent configuration directories, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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