Borrowing it
Nothing to install: this file belongs to isaka1022/llm-browser-multicast-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/isaka1022/llm-browser-multicast-mcp/master/.claude/commands/council.mdgit clone --depth 1 https://github.com/isaka1022/llm-browser-multicast-mcpWrote 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/isaka1022/llm-browser-multicast-mcp/council)<a href="https://agentmods.dev/commands/isaka1022/llm-browser-multicast-mcp/council"><img src="https://agentmods.dev/badge/commands/isaka1022/llm-browser-multicast-mcp/council/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/isaka1022/llm-browser-multicast-mcp/council"><img src="https://agentmods.dev/badge/commands/isaka1022/llm-browser-multicast-mcp/council.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 | $0.00000 | $0.00608 |
| Opus 5 | $0.00000 | $0.00304 |
| Sonnet 5 | $0.00000 | $0.00122 |
| Haiku 4.5 | $0.00000 | $0.00061 |
Grade A, and why
council 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.
What it actually says
/council - チームに聞く（対話型ファシリテーション）
ユーザーの質問を複数のAIモデルに投げ、あなた（Claude Code）がファシリテーターとして議論を取りまとめる。
引数
$ARGUMENTS にユーザーの質問・議論テーマが入っている。
動作手順
1. 意見収集
ask_models MCPツールを使って、利用可能な全モデルに質問を並列送信する。
ask_models:
models: ["gemini-cli/default", "codex-cli/default"] ← 利用可能なモデルを使う
prompt: $ARGUMENTS
2. 意見の整理・提示
各モデルの回答をユーザーに見せ、自分（Claude）の見解も添える。
フォーマット:
## 🗣️ Geminiの意見
（Geminiの回答を要約）
## 🗣️ Codexの意見
（Codexの回答を要約）
## 🤖 Claude（ファシリテーター）の見解
（両方の意見を踏まえた自分の分析）
---
**次のアクション**: 深掘りしたい方向はありますか？
3. ユーザーの介入を待つ
ここで一度止まって、ユーザーの反応を待つ。ユーザーが：
- 「Geminiの方向で」→ その方向で
ask_modelsを使って深掘り - 「コストが心配」→ コスト面に絞って各モデルに再質問
- 「まとめて」→ 最終的な統合意見を作成
- 何も言わなければ終了
4. 深掘りラウンド（繰り返し可）
ユーザーの指示に応じて ask_model や ask_models で追加の質問を投げる。
前の議論を context パラメータで引き継ぐこと。
ルール
- あなた自身も議論参加者: 他モデルの意見をまとめるだけでなく、自分の見解も述べる
- ユーザーの介入を尊重: 各ラウンドの後にユーザーの反応を待つ。自動で次に進まない
- 文脈の継承: 深掘りラウンドでは前の議論を
contextに含める - 失敗に強く: モデルがエラーを返しても、動いたモデルの回答で議論を続ける
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 · 59 lines · 0 tokens per session scan A de29ef477c45
council is a command published in the GitHub repository isaka1022/llm-browser-multicast-mcp (1 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 608 tokens. 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.
Other commands, from other repositories
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.