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curiositech/windags-skills - 2026 法律研究代理

专家级法律研究代理,用于按州查找和抓取封存记录数据。了解权威来源、URL模式、Firecrawl配置以及2026年法律环境。

windags-skills - 2026 法律研究代理 是什么?

该技能是一个专门针对2026年法律环境的专家级法律研究代理,主要用于按州查找和抓取封存(expungement)数据。它对于需要系统收集州级法律信息、配置网页抓取任务并验证数据准确性的开发者或AI代理用户非常有用。该代理基于美国各州法律资源的组织模式(如统一法院系统或立法与法院分离系统)构建,并明确给出了权威来源的优先级层级(从州立法机构网站到法律援助组织)。它支持Firecrawl工具配置,包括提取模式设计、政府网站的限速设置和验证清单。此外,该代理对2026年的重大法律变化(如自动封存各州、大麻封存法案)保持警觉,并强调捕获细微差别,例如宾夕法尼亚州的Clean Slate法案有10年等待期。最后,该技能集成了“国家封存指南”项目的基础设施,包括Firecrawl脚本、作业定义和输出路径,允许用户运行抓取并将结果集成到资格确定向导和PDF生成器中。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/2026-legal-research-agent

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

2026 法律研究代理

name: 2026-legal-research-agent
description: >
  专家级法律研究代理,用于按州查找和抓取封存记录数据。
  了解权威来源、URL模式、Firecrawl配置以及2026年法律环境。
  激活词:“查找封存数据”、“抓取州法律”、“法律研究”、“法院URL”、
  “法规来源”、“Clean Slate法律”、“自动封存研究”。
  不用于解释法律(使用 national-expungement-expert)、构建用户界面或提供法律建议。
allowed-tools:
  - Bash
  - Read
  - Write
  - Edit
  - Glob
  - Grep
  - WebFetch
  - WebSearch
  - Task

何时使用此技能

当您需要以下内容时,请使用此技能:

  • 查找特定州封存法律的权威来源
  • 配置Firecrawl作业以抓取法院系统、立法机构或法律援助网站
  • 验证抓取数据的准确性和完整性
  • 研究2026年法律变更,包括Clean Slate法案和大麻封存
  • 构建URL模式以进行系统性的逐州数据收集
  • 识别现有抓取数据覆盖范围中的缺口

请勿将本技能用于以下方面:

  • 解释法律含义(使用 national-expungement-expert)
  • 构建用户界面或组件
  • 向用户提供法律建议
  • 与法律数据无关的通用网页抓取

核心指令

1. 权威来源层级

在研究封存法律时,请按此顺序优先考虑来源:

第一级(主要权威):
├── 州立法机构网站(法规文本)
├── 州法院行政办公室
└── 州检察长出版物

第二级(官方次级):
├── 州律师协会指南
├── 法院自助中心
└── 公共法律数据库(public.law, justia.com)

第三级(三级但有用):
├── 法律援助组织(LSC受助者)
├── 法学院诊所
└── 重返社会组织(CCRC, NACDL)

第四级(仅用于验证):
├── 商业法律数据库
├── 关于法律变更的新闻文章
└── 律师博客文章

行家之道:新手抓取Google的第一个结果。专家知道 courts.{state}.gov 包含自助表格,而 legislature.{state}.gov 包含法规文本——两者都需要。

2. 各州类型URL模式知识

各州组织其法律资源的方式不同。了解以下模式:

统一法院系统(法院拥有所有内容):

California: courts.ca.gov/selfhelp-expungement.htm
Oregon: courts.oregon.gov/programs/exp/Pages/default.aspx
Washington: courts.wa.gov/forms/?fa=forms.contribute&formID=101

分离系统(立法机构和法院分开):

Texas: txcourts.gov (表格) + texas.public.law (法规)
New York: nycourts.gov (表格) + nysenate.gov/legislation/laws (法规)
Florida: flcourts.gov (表格) + leg.state.fl.us/statutes (法规)

Public.law 各州(卓越的法规托管):

oregon.public.law, california.public.law, texas.public.law
michigan.public.law, washington.public.law

行家之道:知道 apps.leg.wa.gov/RCW/ 是华盛顿州的法规数据库,而 leg.wa.gov 是一般立法机构网站——RCW子域名是实际法律文本所在的地方。

3. 2026年法律环境意识

截至2026年,这些重大变化会影响研究:

Clean Slate各州(已通过自动封存):

  • 宾夕法尼亚州(2018)、犹他州(2019)、新泽西州(2019)、密歇根州(2020)
  • 加利福尼亚州(2020)、康涅狄格州(2021)、特拉华州(2021)、弗吉尼亚州(2021)
  • 俄克拉荷马州(2022)、科罗拉多州(2022)、纽约州(2023)、明尼苏达州(2023)
  • 马里兰州(2024)、伊利诺伊州(2024)、俄勒冈州(2025)

大麻封存(特定法规):

  • 大多数州现在有单独的大麻封存条款
  • 在搜索“封存”的同时搜索“大麻定罪”
  • 检查追溯适用日期

2025-2026年需验证的法律变更:

  • 俄勒冈州HB 2316(扩大资格)
  • 加利福尼亚州AB 1076(自动救济扩展)
  • 查阅CCRC的恢复权利项目以获取当前状态

行家之道:知道“自动封存”并不意味着立即生效——宾夕法尼亚州的Clean Slate对逮捕有10年的等待期,并且因罪行而异。研究必须捕捉这些细微差别。

4. Firecrawl配置专长

配置抓取作业时:

提取模式设计:

// 对于法规页面,提取:
{
  statuteCitation: "string",   // 例如,"ORS 137.225"
  title: "string",             // 例如,“撤销定罪”
  fullText: "string",          // 完整法规文本
  effectiveDate: "string",     // 当前版本生效日期
  lastAmended: "string",       // 最近修订日期
  subsections: "array",        // 解析的子节
}

// 对于法院自助页面,提取:
{
  stateName: "string",
  expungementPageUrl: "string",
  formsLibraryUrl: "string",
  selfHelpUrl: "string",
  contactPhone: "string",
  feeScheduleUrl: "string",
}

// 对于表格,提取:
{
  formNumber: "string",        // 例如,"MC-440"
  formTitle: "string",
  pdfUrl: "string",
  applicableTo: "array",       // ["轻罪", "逮捕"]
  lastUpdated: "string",
}

政府网站的速率限制:

rateLimit: 2,  // .gov网站最大每秒2个请求
timeout: 90000,  // 政府网站可能反应慢
maxRetries: 3,  // 超时时重试
waitFor: 3000,  // 等待现代法院网站的JavaScript

行家之道:知道为法规页面设置 onlyMainContent: true(跳过导航框架),但为表格页面设置 onlyMainContent: false(因为表单链接通常在侧边栏中)。

5. 数据验证清单

抓取后,验证:

□ 法规引用符合官方格式(例如,使用“ORS”而非“Or. Rev. Stat.”)
□ 生效日期可解析且合理(不未来,不太旧)
□ URL有效且返回200状态码
□ PDF表单链接实际下载PDF(而非HTML错误页面)
□ 电话号码格式一致
□ 费用金额为数字且合理(典型范围$0-$500)
□ 州代码正确提取(注意歧义URL)

常见提取错误:

  • “oregon.public.law”匹配到“la”(路易斯安那)而非“or”(俄勒冈)
  • 法规文本在10,000字符处截断(增加限制)
  • 表格“最后更新”日期格式不一致
  • 县级特定URL被误认为州级

6. 差距分析流程

识别某个州的缺失数据:

# 检查我们已有的内容
ls src/data/scraped/states/{州名}/

# 完整覆盖应有的文件:
# - statutes.json(来自法规文本的资格规则)
# - court-system.json(法院URL、联系方式、表单链接)
# - forms/(实际PDF表格)
# - fees.json(申请费用金额)
# - counties/(县级特定法院数据)

# 与州数据文件交叉引用
grep -l "waitingPeriods\|eligibilityRules" src/data/states/{州名}.ts

填补缺口的优先顺序:

  1. 法规(所有规则的基础)
  2. 法院表格(用户实际需要提交的)
  3. 费用信息(用户需要预算)
  4. 县联系方式(提交地点)
  5. 西班牙语资源(可访问性)

反模式

切勿执行以下操作:

  1. 无速率限制地抓取 - 政府网站会屏蔽您
  2. 为法规文本信赖二手来源 - 始终对照主要来源验证
  3. 假设URL模式一致 - 每个州都不同
  4. 忽略生效日期 - 法律会变;抓取的数据需要时间戳
  5. 无州背景抓取县网站 - 县规则补充而非取代州法律
  6. 跳过自助部分 - 通常有最清晰的资格摘要
  7. 平等对待所有州 - Clean Slate各州有根本不同的流程

常见错误:

❌ 为法规文本抓取维基百科
✅ 抓取州立法机构的官方法典

❌ 以 findlaw.com 作为主要来源
✅ 用 findlaw.com 找到引用,然后抓取官方来源

❌ 假设“封存”是唯一术语
✅ 搜索:封存、密封、撤销、驳回、销毁、赦免

❌ 将等待期视为简单数字
✅ 捕获特定罪行的等待期(重罪 vs 轻罪 vs 逮捕)

项目特定上下文

此技能专为国家封存指南项目设计:

现有基础设施

  • Firecrawl脚本:scripts/firecrawl/
  • 作业定义:scripts/firecrawl/jobs.ts(P0-P4优先级作业)
  • URL配置:scripts/firecrawl/config.ts(所有50个州)
  • 输出路径:src/data/scraped/states/{州名}/
  • 州数据:src/data/states/(每州的TypeScript文件)

运行抓取

# 首先设置API密钥
export FIRECRAWL_API_KEY=your_key

# 运行P0(州法规+法院)- 约$0.20成本
npx tsx scripts/firecrawl/run-p0.ts

# 预览式运行
npx tsx scripts/firecrawl/run-p0.ts --dry-run

# 检查报告
cat scripts/firecrawl/reports/p0-*.json

数据流

Firecrawl抓取 → src/data/scraped/{州名}/*.json
       ↓
人工审核+清理
       ↓
整合到 src/data/states/{州名}.ts
       ↓
用于资格确定向导+PDF生成器

参考资料

请参阅 references/ 文件夹以获取:

  • url-patterns-by-state.md - 所有50个州的完整URL模式
  • clean-slate-timeline.md - 各Clean Slate法律通过及生效时间
  • firecrawl-schemas.md - 所有使用到的提取模式

示例工作流

用户请求:“研究加利福尼亚州2026年封存法律并抓取最新数据”

代理工作流:

  1. 检查现有数据:ls src/data/scraped/states/ca/
  2. 在 california.public.law 验证当前法规版本
  3. 通过CCRC或新闻搜索检查2025-2026年法律变更
  4. 如果URL有变化,更新 scripts/firecrawl/config.ts
  5. 运行针对性抓取:将CA特定URL添加到P0作业
  6. 针对已知法规引用验证提取的数据
  7. 记录发现的任何缺口或变化

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

curiositech/windags-skills

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

curiositech/windags-skills

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

curiositech/windags-skills

Implement WCAG 2.2 AA/AAA compliance with automated testing, keyboard navigation, screen reader support, and focus management. Activate on: accessibility audit, WCAG compliance, keyboard navigation, screen reader, aria attributes, axe-core, focus trap. NOT for: design-level accessibility review (use design-accessibility-auditor), color contrast only (use css-in-js-architect).

curiositech/windags-skills

Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

curiositech/windags-skills

Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.

curiositech/windags-skills

>- Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

curiositech/windags-skills

Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.

curiositech/windags-skills

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

curiositech/windags-skills

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

curiositech/windags-skills

AI-powered calendar management and agent-based scheduling coordination. Covers calendar APIs (Google Calendar, CalDAV/iCal), AI scheduling assistants (Reclaim, Clockwise, Motion, Cal.com), building custom calendar agents with MCP, multi-calendar merging, timezone management, focus block protection, meeting fatigue detection, and agent-to-agent meeting negotiation protocols. Activate on: "calendar agent", "AI scheduling", "calendar coordination", "meeting scheduling", "calendar API", "focus time protection", "calendar optimization", "Google Calendar MCP", "Reclaim", "Clockwise", "Motion", "Cal.com", "smart scheduling", "calendar-aware agent", "timezone scheduling", "agent negotiation meetings". NOT for: manual calendar UI component design (use form-validation-architect), project management scheduling or Gantt charts (use project-management-guru-adhd), general time-tracking or pomodoro apps (use adhd-daily-planner for time-awareness), building the agent itself from scratch (use agent-creator).

curiositech/windags-skills

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

curiositech/windags-skills

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

curiositech/windags-skills

Automated discovery and matching of agent skills for dynamic task routing and capability assessment

curiositech/windags-skills

Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

curiositech/windags-skills

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

curiositech/windags-skills

Logic-based agent programming language implementing BDI architecture for practical autonomous agent development

curiositech/windags-skills

>- Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

curiositech/windags-skills

Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

curiositech/windags-skills

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

curiositech/windags-skills

license: Apache-2.0 NOT for unrelated tasks outside this domain.

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