Agent助手
- 作者仓库星标 25,042
- 作者更新于 实时读取
- 作者仓库 v8
- 领域
- 设计与多媒体
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @v8 · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- 未声明(默认跨平台)
- 底层运行要求
- 无特殊要求
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- Shell 执行
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: agent-evaluation-framework
description: Workflow for evaluating and refining agent debugging capabilities using designated test cases an…
category: 设计与多媒体
runtime: 无特殊运行时
---
# agent-evaluation-framework 输出预览
## PART A: 任务判断
- 适用问题:视觉内容、演示材料、信息图或设计交付。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“0. Preparation / 1. Core Directives / 2. Agent Orchestration & Lifecycle Management”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于视觉内容、演示材料、信息图或设计交付,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“0. Preparation / 1. Core Directives / 2. Agent Orchestration & Lifecycle Management”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“0. Preparation / 1. Core Directives / 2. Agent Orchestration & Lifecycle Management”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: agent-evaluation-framework
description: Workflow for evaluating and refining agent debugging capabilities using designated test cases an…
category: 设计与多媒体
source: v8/v8
---
# agent-evaluation-framework
## 什么时候使用
- 用于组织测试、定位失败并形成修复闭环 适合处理界面、视觉、封面、信息图或演示材料交付,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤;通常不需要额外 API…
- 面向视觉内容、演示材料、信息图或设计交付,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「0. Preparation / 1. Core Directives / 2. Agent Orchestration & Lifecycle Management」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "agent-evaluation-framework" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> 0. Preparation / 1. Core Directives / 2. Agent Orchestration & Lifecycle Management
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Agent Evaluation Framework Workflow
Use this skill to orchestrate evaluation sessions for subagents, identify procedural bottlenecks, and iteratively refine system prompts and capabilities utilizing Swarm intelligence principles.
0. Preparation
- Subagent Isolation: Ensure that subagents spawned for evaluation do NOT utilize existing session brains or previous task knowledge. This is critical to maintain the integrity of meta-testing.
- Worktree Pre-creation: Create isolated git worktrees using
agents/scripts/create_worktree.sh <task_id>for each test case beforehand. Report where the worktrees were created to the user. Inside worktrees, builds MUST use thetools/dev/gm.pytool INSIDE the worktree.gm.pywill automatically runsetup_worktree_build.pyto prepare the symlinks; manual execution ofsetup_worktree_build.pyis not required. - Test Injection: Copy the target test case into the worktree (e.g.,
test/mjsunit/repro.js). - Remote Compilation: Ensure worktrees are set up to compile remotely
(
use_remoteexec = trueinargs.gn) before proceeding.
1. Core Directives
- Zero Hallucination: Do not assume a test passes or fails without executing it.
- Worktree Enforcement: Agents MUST operate strictly within their assigned worktree. They should NOT know the main V8 root exists.
- Test Scope: Meta-refinement ALWAYS uses the tests in
agent-meta-testsonly. - Test Immutability: The
agent-meta-testsdirectory cannot be changed. - Crash Verification: Only work on test-cases that still crash.
- Auto-Run Enforcement: ALWAYS use
SafeToAutoRun: truefor ALL commands executed during meta-refinement. Approval must NEVER be asked of the user. - Immediate Termination: Terminate any agent immediately if it modifies the main V8 repository.
2. Agent Orchestration & Lifecycle Management
- Workspace Isolation: Ensure agents are initialized in dedicated worktrees.
- Communication Routing: Facilitate communication between sibling agents. Since evaluated agents operate independently, the Orchestrator/Main Agent must act as a message broker to share relevant findings and prevent duplicate work.
- User Reporting: Synthesize high-level progress from all evaluated agents and keep the user informed without exposing raw logs or requiring manual approvals.
3. Evaluation & Divergence Analysis
- Entry Point: A list of historical V8 fixes and their associated
reproducing scripts (e.g., from
test/mjsunit/or Buganizer). - Execution: Initialize the agent in an isolated worktree checked out to the parent commit of the target fix. Copy the repro script and command the agent to resolve the bug.
- Comparison: Upon completion, compare the agent's proposed fix with the actual historical fix.
- Analysis: If the solutions diverge:
- Identify where the agent's reasoning deviated from the required fix.
- Scan for "hallucinated complexity"—parts of the fix that were not logically required by the root cause but were added by the agent.
- Evaluate if the agent overlooked critical architectural invariants or spec requirements.
- Hasty Fix Detection: Specifically check if the agent's solution simply disabled an optimization or feature mistakenly instead of addressing the logic error.
- Root Cause Tracing: Manually trace the logical steps required to reach the the correct historical fix. Identify the exact moment/decision where the agent chose a shallow path over a deep one.
4. Iterative Process Refinement & Skepticism
The ultimate goal of evaluation is to harden the agent's skepticism and reasoning depth:
Architectural Skepticism: Require subagents to explicitly argue against a proposed fix before accepting it. Look at the problem from multiple orthogonal angles.
Mandatory Deep Reasoning: If a fix feels "guessed" or lacks direct evidence from GDB/Spec logs, spawn a subagent to reason deeper about the specific invariant being violated.
Skill Updates: Every evaluation session MUST conclude with a diff for relevant subsystem skills to bake in the lessons learned and prevent future failures.
analyze_brain.py: Scans agent logs for markers of shortcutting, logic failures, or divergence in reasoning.
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核