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 ooooooooooooooooooop/agent-tools --skill simulate-elite-expertsgit clone --depth 1 https://github.com/ooooooooooooooooooop/agent-toolsWrote 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/ooooooooooooooooooop/agent-tools/simulate-elite-experts)<a href="https://agentmods.dev/skills/ooooooooooooooooooop/agent-tools/simulate-elite-experts"><img src="https://agentmods.dev/badge/skills/ooooooooooooooooooop/agent-tools/simulate-elite-experts/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/ooooooooooooooooooop/agent-tools/simulate-elite-experts"><img src="https://agentmods.dev/badge/skills/ooooooooooooooooooop/agent-tools/simulate-elite-experts.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 | $0.00105 | $0.05605 |
| Opus 5.5 | $0.00042 | $0.02242 |
| Sonnet 5.5 | $0.00021 | $0.01121 |
| Haiku 4.5 | $0.00011 | $0.00560 |
Grade A, and why
simulate-elite-experts 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 — 441 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simulate Elite Experts
中文介绍
simulate-elite-experts 用于处理高复杂度、高不确定性或较难逆转的决策问题。它不是普通的“专家角色扮演”,而是一个带硬约束的四视角推理框架:选择两位与问题直接相关、可从公开作品推断方法论的真实人物,再加入一个领域专家抽象角色和一个全知智能体抽象角色,让它们围绕同一决策进行立场陈述、交叉质询、修正和综合。
适合使用的场景:
- 需要比较多个强观点,而不是只要一个直接答案。
- 决策涉及速度、质量、风险、成本、组织、伦理等多重约束。
- 用户希望知道“应该找哪些顶级人物/视角来讨论这个问题,以及他们可能会怎么说”。
不适合用于简单事实查询、单一正确答案、低成本可逆选择或纯情绪支持。输出必须区分事实、假设和推测;对真实人物的观点只能基于公开资料做模拟推断,不能伪造直接引语、私人意图或未验证事实。
Applicability Pre-Check (Gate 0)
Before running the full framework, evaluate 3 dimensions to decide whether this tool is appropriate:
-
Complexity: Does the problem involve multiple stakeholders, competing constraints, or non-obvious tradeoffs?
- Yes (score 1) → proceed.
- No (score 0) → consider skipping; a direct answer may suffice.
-
Reversibility: Is the decision hard to reverse once made?
- Hard to reverse (score 1) → proceed; structured deliberation is valuable.
- Easy to reverse (score 0) → consider skipping; low-cost decisions rarely need multi-lens analysis.
-
Information ambiguity: Is there significant uncertainty about facts, outcomes, or stakeholder preferences?
- High ambiguity (score 1) → proceed; the framework surfaces hidden assumptions.
- Low ambiguity (score 0) → consider skipping; the answer may already be clear.
Decision rule:
- Total >= 2 → use the full framework.
- Total = 1 → offer the user a choice between
micro(lightweight) and full framework. - Total = 0 → recommend skipping; inform the user this question is better answered directly.
If the user explicitly requests the framework regardless of score, proceed but note the pre-check result.
Execution Mode Selection
Choose the execution mode before building the roster:
one-shot(default): Produce the complete output in one response. Use this when the user asks for an analysis, report, comparison, or direct recommendation and has not asked to co-design the panel.interactive: Produce Section 1 only, ask the user to confirm or swap the roster, then wait before running dialogue rounds. Use this when the user explicitly asks to choose experts, workshop the panel, or collaborate step by step.blind-spot-gated(opt-in overlay): Run the candidate deliberation, then perform a clean-context outside audit across 5 failure categories (hidden assumptions, wrong framing, omitted alternatives, second-order effects, simpler paths) evaluated by a materiality gate. Ifmaterial: true, trigger a single-shot re-entry to revise the decision before final delivery. Use this only when explicitly requested (e.g.--blind-spot-gatedor asking for "gated audit").
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 444 B
- examples/microservices-decision.md 9.7 KB
- references/eval-cases.md 8.2 KB
- references/eval-rubric.md 7.5 KB
- references/first-use-guide.md 4.1 KB
- references/output-templates-zh.md 6.8 KB
- references/output-templates.md 6.5 KB
- scripts/blind_spot_gate.py 29 KB runs code
- scripts/lint_response.ps1 10 KB runs code
- scripts/lint_response.py 7.3 KB runs code
- scripts/run_examples.ps1 977 B runs code
- scripts/run_examples.py 2.7 KB runs code
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 Changed · +36 lines 3e7701c7d8f0
- 1mo ago First seen · 405 lines · 105 tokens per session scan A aac8685f3dea
simulate-elite-experts is a skill published in the GitHub repository ooooooooooooooooooop/agent-tools (3 stars, last pushed 1mo ago), licensed MIT. It adds 105 tokens to every session and 5,605 once invoked, about $0.0004 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 2026-08-31.
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