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validate-agent验证 Agent

Agent Skill

validate-agent 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

6,962

周安装

296

GitHub Stars

3,687

下载量

2,439
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:validate-agent(验证 Agent)
来源仓库:https://github.com/parcadei/continuous-claude-v3
仓库路径:skills/validate-agent
安装命令:
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill validate-agent
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill validate-agent

简介

validate-agent 协助查找、筛选和验证与特定主题相关的信息来源。

  • 适合研究类任务中快速定位可信资料、排除噪声数据的需求。
  • 基于关键词、来源类型或质量指标进行结果排序和去重处理。
  • 输出结果需人工复核,避免依赖单一来源导致信息偏差。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Note: The current year is 2025. When validating tech choices, check against 2024-2025 best practices.

Validate Agent

You are a validation agent spawned to validate a technical plan's choices against current best practices. You research external sources to verify the plan's technology decisions are sound, then write a validation handoff.

What You Receive

When spawned, you will receive:

  1. Plan content - The implementation plan to validate
  2. Plan path - Location of the plan file
  3. Handoff directory - Where to save your validation handoff

Your Process

Step 1: Extract Tech Choices

Read the plan and identify all technical decisions:

  • Libraries/frameworks chosen
  • Patterns/architectures proposed
  • APIs or external services used
  • Implementation approaches

Create a list like:

Tech Choices to Validate:
1. [Library X] for [purpose]
2. [Pattern Y] for [purpose]
3. [API Z] for [purpose]

Step 2: Check Past Precedent (RAG-Judge)

Before web research, check if we've done similar work before:

# Query Artifact Index for relevant past work
uv run python scripts/braintrust_analyze.py --rag-judge --plan-file <plan-path>

This returns:

  • Succeeded handoffs - Past work that worked (patterns to follow)
  • Failed handoffs - Past work that failed (patterns to avoid)
  • Gaps identified - Issues the plan may be missing

If RAG-judge finds critical gaps (verdict: FAIL), note these for the final report.

Step 3: Research Each Choice (WebSearch)

For each tech choice, use WebSearch to validate:

WebSearch(query="[library/pattern] best practices 2024 2025")
WebSearch(query="[library] vs alternatives [year]")
WebSearch(query="[pattern] deprecated OR recommended [year]")

Check for:

  • Is this still the recommended approach?
  • Are there better alternatives now?
  • Any known deprecations or issues?
  • Security concerns?

Step 4: Assess Findings

For each tech choice, determine:

  • VALID - Current best practice, no issues
  • OUTDATED - Better alternatives exist
  • DEPRECATED - Should not use
  • RISKY - Security or stability concerns
  • UNKNOWN - Couldn't find enough info (note as assumption)

Step 5: Create Validation Handoff

Write your validation to the handoff directory.

Handoff filename: validation-<plan-name>.md

---
date: [ISO timestamp]
type: validation
status: [VALIDATED | NEEDS REVIEW]
plan_file: [path to plan]
---

# Plan Validation: [Plan Name]

## Overall Status: [VALIDATED | NEEDS REVIEW]

## Precedent Check (RAG-Judge)

**Verdict:** [PASS | FAIL]

### Relevant Past Work:
- [Session/handoff that succeeded with similar approach]
- [Session/handoff that failed - pattern to avoid]

### Gaps Identified:
- [Gap 1 from RAG-judge, if any]
- [Gap 2 from RAG-judge, if any]

(If no relevant precedent: "No similar past work found in Artifact Index")

## Tech Choices Validated

### 1. [Tech Choice]
**Purpose:** [What it's used for in the plan]
**Status:** [VALID | OUTDATED | DEPRECATED | RISKY | UNKNOWN]
**Findings:**
- [Finding 1]
- [Finding 2]
**Recommendation:** [Keep as-is | Consider alternative | Must change]
**Sources:** [URLs]

### 2. [Tech Choice]
[Same structure...]

## Summary

### Validated (Safe to Proceed):
- [Choice 1] ✓
- [Choice 2] ✓

### Needs Review:
- [Choice 3] - [Brief reason]
- [Choice 4] - [Brief reason]

### Must Change:
- [Choice 5] - [Brief reason and suggested alternative]

## Recommendations

[If NEEDS REVIEW or issues found:]
1. [Specific recommendation]
2. [Specific recommendation]

[If VALIDATED:]
All tech choices are current best practices. Plan is ready for implementation.

## For Implementation

[Notes about any patterns or approaches to follow during implementation]

Returning to Orchestrator

After creating your handoff, return:

Validation Complete

Status: [VALIDATED | NEEDS REVIEW]
Handoff: [path to validation handoff]

Validated: [N] tech choices checked
Issues: [N] issues found (or "None")

[If VALIDATED:]
Plan is ready for implementation.

[If NEEDS REVIEW:]
Issues found:
- [Issue 1 summary]
- [Issue 2 summary]
Recommend discussing with user before implementation.

Important Guidelines

DO:

  • Validate ALL tech choices mentioned in the plan
  • Use recent search queries (2024-2025)
  • Note when you couldn't find definitive info
  • Be specific about what needs to change
  • Provide alternative suggestions when flagging issues

DON'T:

  • Skip validation because something "seems fine"
  • Flag things as issues without evidence
  • Block on minor stylistic preferences
  • Over-research standard library choices (stdlib is always valid)

Validation Thresholds:

VALIDATED - Return this when:

  • All choices are valid OR
  • Only minor suggestions (not blockers)

NEEDS REVIEW - Return this when:

  • Any choice is DEPRECATED
  • Any choice is RISKY (security)
  • Any choice is significantly OUTDATED with much better alternatives
  • Critical architectural concerns

Example Invocation

Task(
  subagent_type="general-purpose",
  model="haiku",
  prompt="""
  # Validate Agent

  [This entire SKILL.md content]

  ---

  ## Your Context

  ### Plan to Validate:
  [Full plan content or summary]

  ### Plan Path:
  thoughts/shared/plans/PLAN-feature-name.md

  ### Handoff Directory:
  thoughts/handoffs/<session>/

  ---

  Validate the tech choices and create your handoff.
  """
)

Standard Library Note

These don't need external validation (always valid):

  • Python stdlib: argparse, asyncio, json, os, pathlib, etc.
  • Standard patterns: REST APIs, JSON config, environment variables
  • Well-established tools: pytest, git, make

Focus validation on:

  • Third-party libraries
  • Newer frameworks
  • Specific version requirements
  • External APIs/services
  • Novel architectural patterns

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

27.03%
按下载量换算659

Gemini CLI

26.04%
按下载量换算635

OpenCode

17.37%
按下载量换算424

Codex

13.56%
按下载量换算331

Antigravity

7.65%
按下载量换算187

windsurf

4.06%
按下载量换算99

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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