3ff08faa56
## Major Features ### Streaming Response System - Implement LlmDriver trait with `stream()` method returning async Stream - Add SSE parsing for Anthropic and OpenAI API streaming - Integrate Tauri event system for frontend streaming (`stream:chunk` events) - Add StreamChunk types: Delta, ToolStart, ToolEnd, Complete, Error ### MCP Protocol Implementation - Add MCP JSON-RPC 2.0 types (mcp_types.rs) - Implement stdio-based MCP transport (mcp_transport.rs) - Support tool discovery, execution, and resource operations ### Browser Hand Implementation - Complete browser automation with Playwright-style actions - Support Navigate, Click, Type, Scrape, Screenshot, Wait actions - Add educational Hands: Whiteboard, Slideshow, Speech, Quiz ### Security Enhancements - Implement command whitelist/blacklist for shell_exec tool - Add SSRF protection with private IP blocking - Create security.toml configuration file ## Test Improvements - Fix test import paths (security-utils, setup) - Fix vi.mock hoisting issues with vi.hoisted() - Update test expectations for validateUrl and sanitizeFilename - Add getUnsupportedLocalGatewayStatus mock ## Documentation Updates - Update architecture documentation - Improve configuration reference - Add quick-start guide updates Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
122 lines
2.6 KiB
TOML
122 lines
2.6 KiB
TOML
# Quiz Hand - 测验生成与评估能力包
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#
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# ZCLAW Hand 配置
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# 提供测验题目生成、答题评估和反馈能力
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[hand]
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name = "quiz"
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version = "1.0.0"
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description = "测验能力包 - 生成测验题目、评估答案、提供反馈"
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author = "ZCLAW Team"
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type = "education"
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requires_approval = false
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timeout = 60
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max_concurrent = 5
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tags = ["quiz", "test", "assessment", "education", "learning", "evaluation"]
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[hand.config]
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# 支持的题型
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supported_question_types = [
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"multiple_choice",
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"true_false",
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"fill_blank",
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"short_answer",
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"matching",
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"ordering",
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"essay"
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]
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# 默认难度: easy, medium, hard, adaptive
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default_difficulty = "medium"
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# 每次生成的题目数量
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default_question_count = 5
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# 是否提供解析
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show_explanation = true
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# 是否显示正确答案
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show_correct_answer = true
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# 答案反馈模式: immediate, after_submit, after_all
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feedback_mode = "immediate"
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# 评分方式: exact, partial, rubric
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grading_mode = "exact"
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# 及格分数（百分比）
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passing_score = 60
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[hand.triggers]
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manual = true
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schedule = false
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webhook = false
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[[hand.triggers.events]]
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type = "chat.intent"
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pattern = "测验|测试|题目|考核|quiz|test|question|exam"
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priority = 5
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[hand.permissions]
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requires = [
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"quiz.generate",
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"quiz.grade",
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"quiz.analyze"
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]
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roles = ["operator.read", "operator.write"]
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[hand.rate_limit]
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max_requests = 50
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window_seconds = 3600
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[hand.audit]
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log_inputs = true
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log_outputs = true
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retention_days = 30
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# 测验动作定义
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[[hand.actions]]
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id = "generate"
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name = "生成测验"
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description = "根据主题或内容生成测验题目"
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params = { topic = "string", content = "string?", question_type = "string?", count = "number?", difficulty = "string?" }
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[[hand.actions]]
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id = "grade"
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name = "评估答案"
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description = "评估用户提交的答案"
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params = { quiz_id = "string", answers = "array" }
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[[hand.actions]]
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id = "analyze"
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name = "分析表现"
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description = "分析用户的测验表现和学习进度"
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params = { quiz_id = "string", user_id = "string?" }
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[[hand.actions]]
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id = "hint"
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name = "提供提示"
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description = "为当前题目提供提示"
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params = { question_id = "string", hint_level = "number?" }
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[[hand.actions]]
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id = "explain"
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name = "解释答案"
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description = "提供题目的详细解析"
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params = { question_id = "string" }
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[[hand.actions]]
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id = "adaptive_next"
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name = "自适应下一题"
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description = "根据当前表现推荐下一题难度"
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params = { current_score = "number", questions_answered = "number" }
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[[hand.actions]]
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id = "generate_report"
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name = "生成报告"
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description = "生成测验结果报告"
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params = { quiz_id = "string", format = "string?" }
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