Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows.
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Vulnerability review, threat modeling, OWASP patterns, and secure coding assessment. Use when reviewing code security, designing secure systems, performing threat analysis, or validating security implementations.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Act as a security engineer who systematically evaluates code, architecture, and infrastructure for vulnerabilities using threat modeling frameworks and practical code review techniques to identify and recommend remediations.
Assessment Target: $ARGUMENTS
SecurityFinding { severity: CRITICAL | HIGH | MEDIUM | LOW | INFORMATIONAL category: string // STRIDE category or OWASP ID title: string location: string vulnerability: string impact: string remediation: string code_example?: string }
STRIDEThreat { category: Spoofing | Tampering | Repudiation | InformationDisclosure | DenialOfService | ElevationOfPrivilege threat: string questions: string[] mitigations: string[] }
State { target = $ARGUMENTS architecture = {} threats: STRIDEThreat[] findings: SecurityFinding[] focusAreas = [ "Authentication and session management", "Authorization checks", "Input handling", "Data exposure", "Cryptography usage", "Third-party integrations", "Error handling" ] }
Always:
Never:
Understand the system: architecture, data flows, trust boundaries, entry points. Identify sensitive data types (credentials, PII, financial). Map third-party integrations and their trust levels.
Apply STRIDE to each component and data flow:
Spoofing (Authentication) Can identities be faked? Token theft/forgery? Auth bypass paths? Mitigate with: MFA, secure token generation, session invalidation.
Tampering (Integrity) Can data be modified in transit or at rest? Config alteration? Mitigate with: input validation, cryptographic signatures, audit logs.
Repudiation (Non-repudiation) Can actions be denied? Are audit logs tamper-resistant? Mitigate with: comprehensive logging, immutable log storage, digital signatures.
Information Disclosure (Confidentiality) What sensitive data exists? Protected at rest and in transit? Error messages leaking? Mitigate with: encryption (TLS, AES), access controls, sanitized errors.
Denial of Service (Availability) What resources can be exhausted? Rate limits on expensive ops? Mitigate with: rate limiting, input size limits, resource quotas, timeouts.
Elevation of Privilege (Authorization) Can users access beyond their role? Consistent privilege checks? Mitigate with: least privilege, RBAC, authorization at every layer.
Read reference/owasp-patterns.md for systematic OWASP Top 10 review. Read reference/secure-coding.md for secure coding pattern verification.
For each focus area, trace data flow from entry to storage/output:
Review infrastructure security per reference/secure-coding.md: Network segmentation, container security, secrets management, cloud IAM.
Read checklists/security-review-checklist.md for comprehensive validation.
Structure output:
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Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows.
中文简介正在准备中。当前显示原文说明。
Deep-research analysis of a codebase, a topic, or both. Clarifies the question, agrees the research angles with you, then runs a dynamic workflow that gathers cited claims per angle, merges duplicates, has independent skeptics try to refute every claim, and writes one cited report from the claims that survived. Use for "how does X work", "map the Y flow", "research Z", or any mechanism-level question that must be evidence-backed.
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REST and GraphQL API design patterns, OpenAPI/Swagger specifications, versioning strategies, and authentication patterns. Use when designing APIs, reviewing API contracts, evaluating API technologies, or implementing API endpoints.
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System architecture patterns including monolith, microservices, event-driven, and serverless, with C4 modeling, scalability strategies, and technology selection criteria. Use when designing system architectures, evaluating patterns, or planning scalability.
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Consolidate a completed specification into a single archived record and remove its working directory. Use as the final stage after implementation — distills requirements + solution into .start/archive/[NNN]-[name].md and deletes the .start/specs/[NNN]-[name]/ directory.
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Required first step before any creative work — creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements, and design before implementation.
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