Prompts · Python · v0.1.0
小跃虚拟伴侣 - 使用智谱 AI 提供温暖的对话陪伴和静态图片分享
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Use Ontoly's deterministic Software Graph and MCP capabilities for architecture review, request tracing, dependency analysis, configuration lookup, and impact analysis before falling back to source-file search.
by anbeime · updated 7h ago · imported from GitHub
Use this skill when a coding agent needs evidence-backed software understanding from an Ontoly graph before searching repository files directly.
Check whether an Ontoly graph already exists by looking for .ontoly/, SoftwareGraph.json, diagnostics.json, validation reports, or an Ontoly MCP configuration.
If no graph exists and the user permits local analysis, run:
ontoly build .
Inspect graph health before answering: diagnostics, graph hash, semantic coverage, trust or quality score, framework detection, and generation timestamp.
Prefer Ontoly CLI or MCP capabilities for graph questions instead of scanning source files first.
Use repository search only when Ontoly cannot answer, the graph is stale, the graph is incomplete, or the user explicitly asks for source-level verification.
Always cite graph evidence in the answer: node IDs, edge types, file paths, source locations, diagnostics, or framework analyzer output.
State confidence from graph evidence. Do not guess confidence.
ExplainArchitecture for repository and package topologyFindDependencies for dependency trees and direct consumersImpactAnalysis for refactor blast radiusTraceExecution for request, route, and call-flow tracingFindConfigurationUsage for configuration and environment variable usageFrameworkReport for detected framework concepts such as modules, controllers, providers, and routesFindDeadCode for unreachable or unused graph regionsWhen answering, include:
Example:
AuthController handles authentication.
Evidence:
- node: class:src/auth/auth.controller.ts:AuthController
- route edges: HANDLES POST /login, POST /logout
- dependency edges: USES AuthService, JwtService
Confidence: high, because the graph has controller, route, and dependency edges with source locations.
NOT_FOUND with the closest graph evidence instead of inventing an answer.No comments yet — start the thread.
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Prompts · Python · v0.1.0
小跃虚拟伴侣 - 使用智谱 AI 提供温暖的对话陪伴和静态图片分享
#agent-skills#claude-skills#codex
Prompts · Python · v0.1.0
专为软件开发工程师与综合办公文员打造。当你在等待任务执行、工作疲惫或需要鼓励时,使用此技能通过智谱AI自动生成温暖对话回应与场景配图,一键获取专属情绪价值与虚拟陪伴,让AI助手更懂你。
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Prompts · Python · v0.1.0
软件开发工程师与综合办公文员在长时间独自执行后台任务或处理繁琐工作时,当需要情绪陪伴与互动反馈,请使用此技能。小跃虚拟伴侣会在任务期间主动陪聊,智能生成专属生活照片并通过飞书发送温暖消息,为你提供全天候的情绪价值与贴心陪伴。
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Prompts · Python · v0.1.0
技能创建模板。复制此目录并修改 SKILL.md 即可创建新技能。请将所有 <占位符> 替换为实际内容。
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