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Agent Bootstrap

ASecurity

任意智能体自举驱动本academic-toolkit:读协议、探测能力、选择路由或启动工作流。触发词:启动项目、如何驱动、bootstrap、自举、宿主无关接入、agent 开始工作。

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Added 9/24/2026
ai-agentspythonbash

Works with

claude codecursorcli

Security Analysis

A100/100

Scanned 9/28/2026

$npx -y skills add FOURTEEN1416/academic-agent-toolkit --skill agent-bootstrap --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.md
---
name: agent-bootstrap
description: 任意智能体自举驱动本academic-toolkit:读协议、探测能力、选择路由或启动工作流。触发词:启动项目、如何驱动、bootstrap、自举、宿主无关接入、agent 开始工作。
status: active
---

# agent-bootstrap — 宿主无关 Agent 自举协议

## 定位

本技能告诉**当前驱动本项目的任意 Agent**(Claude Code / Cursor / Gemini CLI /
MiMo Desktop / OpenCode / ZCode / 自写脚本)如何在 5 分钟内合法接入并开始工作。

**不依赖任何宿主专用配置。** OpenCode / ZCode 配置仅为可选适配器(本地可选自建)。

## 输入契约

- 仓库已 clone(本检出目录名以本地为准,勿假设绝对路径)
- 用户需求文本(竞赛题 / 论文 / 文献 / 图表 / 其他)

## 执行步骤

1. **读协议**
   - 仓库根 `AGENTS.md`(宿主中立入口)
   - `academic-toolkit/AGENTS.md`(路由与门禁细节)
   - 机器可读契约:在 `academic-toolkit/` 下执行 `python -m engine.workflow_cli boot`

2. **探测能力**
   ```bash
   cd academic-toolkit
   python -m engine.workflow_cli probe
   ```
   阅读 `host_adapters` 与 `gaps`。适配器缺失不阻断;环境缺口按 hint 补齐或改用替代路径。

3. **确定驱动标签**
   - 在 evidence 中自报 `agent`:如 `claude-code` / `cursor` / `mimo-desktop`
   - 可用环境变量 `ACAT_AGENT_LABEL` 固定默认标签

3.5 **扫资产台账(先知道自己有什么,再决定怎么做)**
   - 读 `academic-toolkit/data/asset_catalog.json`(probe 输出的 `asset_catalog` 字段是同一份摘要)
   - 按 `when_to_use` / `owner_skills` 匹配当前任务:命中资产 → 先用资产,再造轮子
     (例:写竞赛摘要前读 62 篇摘要统计;画图前查统一配色注册表;建模前查题型案例库)
   - `local_only: true` 条目在私有资料区(`assets-local/` 等):本机有则用;公开 clone 缺席
     属语义缺位,不算断链,改走降级路径
   - 台账没覆盖到的需求 → TOOL_GAP 流程(forge 或如实上报)

4. **选择路径**
   - **竞赛/多步管线**:`python -m engine.workflow_cli start --template comp_cumcm --workspace <ws>` → `next`(自动提供执行会话/本步技能正文)→ `session run/write` → `session finish`(自动组装证据、一次核验和推进)
   - **单技能任务**:按 `academic-toolkit/AGENTS.md` §三 路由表读 `skills/<name>/SKILL.md` 直接执行
   - **能力不足**:转入 `skills/tool-forge`

5. **执行与回报**
   - 产出写入 StepAction.workspace(工作流)或用户指定目录(单技能)
   - 默认用 `session run --session <next返回路径> --plan <任务计划> --finish`;纯内容用 `session write --path <产物> --stdin`,完成后 `session finish`。程序采集实际返回码、输入输出和指纹,不手填 evidence JSON。
   - 计划只声明业务命令/依赖/产物;明确纯计算且完整依赖的节点可复用,变化时自动重跑受影响节点。不要缓存网络、独立评审或人工批准。
   - `needs_work` 保持本步可编辑,一次修复诊断后再次 finish;不为修字段创建 retry 循环。中断恢复需确认进程已停,不能伪造成功。
   - 旧外部接入仍可 `complete_step` + 真实 `execution_evidence`。无宿主 L1 时审计如实降级;执行会话的机器采集记录独立核验,不冒充宿主L1。

## 输出契约

- 已选定的驱动路径(工作流 ID 或技能名)
- probe 摘要(可用适配器 / 关键 gaps)
- 后续动作清单

## 质量铁律

- 引擎只编排不执行;执行者是你(当前 Agent)
- 同一时刻只有一个主控 Agent
- 无证据=未执行
- TOOL_GAP:不够就 forge 或如实上报,禁止伪造通过

## 关联

- 协议实现: `academic-toolkit/engine/agent_protocol.py`
- 能力探测: `academic-toolkit/engine/capability_probe.py`
- 工具铸造: `skills/tool-forge`
- 可选适配器: 协议不依赖(`forge --adapter` 可本地生成)

Attribution

FOURTEEN1416FOURTEEN1416
View sourceSee grades on GitHubMore from FOURTEEN1416 →
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