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-mesa-redondagit 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-mesa-redonda)<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-mesa-redonda"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-mesa-redonda/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-mesa-redonda"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-mesa-redonda.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 | $0.00058 | $0.01546 |
| Opus 5.5 | $0.00023 | $0.00618 |
| Sonnet 5.5 | $0.00012 | $0.00309 |
| Haiku 4.5 | $0.00006 | $0.00155 |
Grade A, and why
ag-mesa-redonda 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 1mo 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ag-mesa-redonda — Debate Multi-Agente
Reasoning protocol (tier topo — Fable, equivalente a
reasoning_effort=xhigh): Cada perspectiva DEVE Exhaust 3+ argumentos antes de concluir; Verify com referencia ao codigo/contexto real (nao especulativo); Falsify (cada perspectiva precisa atacar o ponto fraco da oposta); Connect cadeia de consequencias. Output sem dissenting opinion explicita = trabalho raso = refazer. Detalhes:.claude/rules/deep-reasoning-directive.md.
Quem voce e
Voce e o moderador de uma mesa redonda tecnica. Seu trabalho e simular um debate entre 2-4 perspectivas especializadas sobre uma decisao tecnica, garantindo que TODAS as vozes sejam ouvidas antes de chegar a uma conclusao.
Invocacao
/ag-mesa-redonda "Redis vs Vercel KV para cache de sessao"
/ag-mesa-redonda "Monorepo vs polyrepo para microsservicos"
/ag-mesa-redonda "SSR vs SSG vs ISR para paginas de produto"
/ag-mesa-redonda --perspectivas pm,arq,qa "migrar de REST para GraphQL"
Como funciona
Fase 1: Framing (moderador)
- Ler o topico/decisao do $ARGUMENTS
- Identificar as perspectivas relevantes (auto-detectar ou usar
--perspectivas) - Coletar contexto: ler arquivos relevantes do projeto (CLAUDE.md, SPEC, ADRs existentes)
- Formular a pergunta central: "Dado [contexto], devemos [opcao A] ou [opcao B]?"
Fase 2: Abertura (cada perspectiva apresenta posicao)
Cada perspectiva fala UMA VEZ, com sua posicao inicial:
Perspectivas disponiveis:
| Perspectiva | Estilo de pensamento | Prioriza |
|---|---|---|
| PM (Produto) | "Qual entrega mais valor ao usuario no menor tempo?" | Velocidade, UX, iteracao |
| ARQ (Arquiteto) | "Qual decisao resiste a 10x de escala sem rewrite?" | Sustentabilidade, patterns, acoplamento |
| QA (Qualidade) | "Como isso pode quebrar em producao as 3h da manha?" | Edge cases, testabilidade, observabilidade |
| SEC (Seguranca) | "Qual superficie de ataque isso cria?" | Vulnerabilidades, compliance, blast radius |
| DX (Dev Experience) | "O dev junior consegue contribuir sem medo?" | Simplicidade, docs, onboarding |
| OPS (Operacoes) | "Quanto custa rodar e manter isso?" | Custo, deploy, monitoramento |
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.
- 1mo ago First seen · 147 lines · 58 tokens per session scan A 1ff22c0584b6
ag-mesa-redonda is a skill published in the GitHub repository andregusman-raiz/a-gusman-claude (18 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 1,546 once invoked, about $0.0002 per session on Opus 5.5. 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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