Agent skill · Data & Analytics

multi-ai-research

Parallel multi-AI cross-validation research workflow (大版本). Dispatch N internal sub-agents + grok + gemini in parallel, automatically cross-validate findings, tier by confidence (strong consensus / partial / conflict / insufficient), generate tiered action items with arbitration. Use when user says "多 AI 调研", "交叉验证", "独立共识", "三脑调研", "multi-ai research", "parallel research", "cross-validate", or needs deep research that benefits from internal data + external 2026 consensus. NOT for quick factual Q&A, pure code reasoning, or tasks needing deep project context.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill multi-ai-research --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 8 KB
Bundled scripts: none
Path: skills/ai-llm/multi-ai-research/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

<!-- v1 | 2026-04-09 | 从 2026-04-08 X reply deboost 调研实战沉淀；集成自动置信度分级 + 仲裁 + 改动清单 --> # Multi-AI Research（并行多 AI 交叉验证） **核心价值**：能力乘法，不是加法。Claude（主脑）+ grok（X 社区/实时）+ gemini（Google 生态/结构化）+ N 个内部 sub-agent = **N+3 个 agent 并行**处理同一个研究问题。 **关键洞察**（2026-04-08 实战验证）：两个独立外部 AI 的共识信号 **强于** 任何单个 AI 的深度。"更深度" < "更少错"。 **和 ask-opencli 的关系**： - `ask-opencli` = 单次 grok 或 gemini 调用（日常 second opinion） - `multi-ai-research` = **完整调研工作流**，并行多 AI + 内部数据 + 交叉验证 + 自动仲裁 如果用户只是想"问 grok 一个问题"，用 `ask-opencli`。如果用户要做"深度调研"或"交叉验证多个维度"，用这个 skill。 --- ## 何时触发 ### ✅ 适合 - 深度研究任务（手动做 >30 分钟） - 行业机制问题（算法规则、产品决策、社区共识） - 需要外部共识加权的内部数据推断 - 时效性问题（grok 有实时 X 数据，gemini 有最新 web 索引） - 反转既有假设（数据 vs 理论冲突时仲裁） - 新工具/新做法的可行性调研 ### ❌ 不适合 - 纯代码推理（单个 Claude 足够） - 需要深度项目 context 的任务（外部 AI 不了解你的代码库） - 快速事实问答（<30 秒能解决，并行开销不值） - 创意生成（单家强模型即可） --- ## 工作流（7 Phase） ### Phase 1：问题分解（Claude 自动 + 用户可覆盖） 从用户的一个研究问题，自动拆分成： 1. **内部数据查询**（1-5 个 sub-agent 并行） - 数据分布/量化分析 - 内容对比/质性分析 - 多维度切分 - 时序/趋势分析 - （按需增加） 2. **外部理论查询**（2 个 Bash 并行） - grok：侧重实时/社区/X 信号 - gemini：侧重结构化/框架/长推理 **默认分解策略**： - 3 个内部 agent + 2 个外部 AI = 5 个并行任务 - 如果用户问题偏理论 → 减少内部 agent 到 1-2 个，加大外部 AI 权重 - 如果用户问题偏数据 → 加到 4-5 个内部 agent，只跑 2 个外部 AI 做交叉 **用户可覆盖**：用户明确说"只问 grok 和 gemin

What's inside
Steps it walks through
  1. 何时触发
  2. ✅ 适合
  3. ❌ 不适合
  4. 工作流（7 Phase）
  5. Phase 1：问题分解（Claude 自动 + 用户可覆盖）
  6. Phase 2：Prompt 自动生成
  7. Phase 3：并行派发（一条消息多个工具调用）
  8. Phase 4：等待（不要 poll）
  9. Phase 5：交叉验证 + 自动置信度分级（大版本核心）
  10. Phase 5.5：外部 AI 案例二次验证（大版本新增）
  11. Phase 6：自动生成改动清单（tiered action items）
  12. Phase 7：Artifact 保存
  13. ⚠️ 命令速查（防呆，最先看这个）
  14. 首次使用：安装 opencli（5 分钟一次性）
Ships with 1 file
  • metadata.json
Commands it runs
opencli gemini ask "问题" --format plain         # 最常用（单次问答）
opencli gemini new                               # 开新对话
opencli gemini deep-research "问题"              # Deep Research
opencli gemini deep-research-result              # 取 Deep Research 结果
opencli gemini image "画一个..."                 # 生图
npm install -g @jackwener/opencli
opencli --version
opencli doctor
export OPENCLI_BROWSER_COMMAND_TIMEOUT=300
npx skills add jackwener/opencli
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About this skill
What does the multi-ai-research skill do?

Parallel multi-AI cross-validation research workflow (大版本). Dispatch N internal sub-agents + grok + gemini in parallel, automatically cross-validate findings, tier by confidence (strong consensus / partial / conflict / insufficient), generate tiered action items with arbitration. Use when user says "多 AI 调研", "交叉验证", "独立共识", "三脑调研", "multi-ai research", "parallel research", "cross-validate", or needs deep research that benefits from internal data + external 2026 consensus. NOT for quick factual Q&A, pure code reasoning, or tasks needing deep project context.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill multi-ai-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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