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.
git clone --depth 1 https://github.com/linofcp007/advogado-ptWrote 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/commands/linofcp007/advogado-pt/despedir)<a href="https://agentmods.dev/commands/linofcp007/advogado-pt/despedir"><img src="https://agentmods.dev/badge/commands/linofcp007/advogado-pt/despedir/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/commands/linofcp007/advogado-pt/despedir"><img src="https://agentmods.dev/badge/commands/linofcp007/advogado-pt/despedir.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00026 | $0.00361 |
| Opus 5 | $0.00013 | $0.00180 |
| Sonnet 5 | $0.00005 | $0.00072 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
despedir 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.
What it actually says
Ativa a skill advogado-pt para a cessação de contrato em $ARGUMENTS.
Segue o playbook quero-despedir (via tool MCP obter_playbook com nome: quero-despedir, ou lê playbooks/quero-despedir.md): identifica a modalidade correta e os requisitos legais. Calcula a compensação com calc_compensacao_despedimento (retribuicao_base, diuturnidades, anos, modalidade). Para documentos, usa templates como notificacao-resolucao-contrato, nota-de-culpa ou acordo-revogacao.
EN: Activate the advogado-pt skill for the contract termination in $ARGUMENTS. Follow the quero-despedir playbook (via the obter_playbook MCP tool with nome: quero-despedir, or read playbooks/quero-despedir.md): identify the correct form of termination and the legal requirements. Compute the severance with calc_compensacao_despedimento (retribuicao_base, diuturnidades, anos, modalidade). For documents, use templates such as notificacao-resolucao-contrato, nota-de-culpa or acordo-revogacao.
Exemplos · Examples: "quanto custa despedir um trabalhador com 4 anos", "severance for a 4-year employee".
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.
- 8d ago First seen · 13 lines · 26 tokens per session scan A 5d087dfd1199
despedir is a command published in the GitHub repository linofcp007/advogado-pt (0 stars, last pushed 29d ago), licensed MIT. It adds 26 tokens to every session and 361 once invoked, about $0.0001 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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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.