arckit

Document architectural decisions with options analysis and traceability

Assess UK Government AI Playbook compliance for responsible AI deployment

Perform comprehensive governance quality analysis across architecture artifacts (requirements, principles, designs, assessments)

Generate Algorithmic Transparency Recording Standard (ATRS) record for AI/algorithmic tools

Research AWS services and architecture patterns using AWS Knowledge MCP for authoritative guidance

Research Azure services and architecture patterns using Microsoft Learn MCP for authoritative guidance

Generate prioritised product backlog from ArcKit artifacts - convert requirements to user stories, organise into sprints

Assess architecture conformance — ADR decision implementation, cross-decision consistency, design-principles alignment, architecture drift, technical debt, and custom constraint rules

Copy plugin templates to project for customization

Create federated data product contracts for mesh architectures with SLAs, governance, and interoperability guarantees (project)

Create comprehensive data model with entity relationships, GDPR compliance, and data governance

Discover external data sources (APIs, datasets, open data portals) to fulfil project requirements

Create DevOps strategy with CI/CD pipelines, IaC, container orchestration, and developer experience

Generate Yourdon-DeMarco Data Flow Diagrams (DFDs) with structured analysis notation

Generate architecture diagrams using Mermaid or PlantUML C4 for visual documentation

Review Detailed Design (DLD) for implementation readiness

Generate Digital Outcomes and Specialists (DOS) procurement documentation for UK Digital Marketplace

Generate Data Protection Impact Assessment (DPIA) for UK GDPR Article 35 compliance

Create vendor evaluation framework and score vendor proposals

Create FinOps strategy with cloud cost management, optimization, governance, and forecasting

Analyze G-Cloud service gaps and generate supplier clarification questions

Find G-Cloud services on UK Digital Marketplace with live search and comparison

Research Google Cloud services and architecture patterns using Google Developer Knowledge MCP for authoritative guidance

Scan all projects for stale research, forgotten ADRs, unresolved review conditions, orphaned artifacts, missing traceability, and version drift

Review High-Level Design (HLD) against architecture principles and requirements

Initialize ArcKit project structure for enterprise architecture governance

Create MLOps strategy with model lifecycle, training pipelines, serving, monitoring, and governance

Generate a MOD Secure by Design assessment for UK Ministry of Defence projects using CAAT and continuous assurance

Create operational readiness pack with support model, runbooks, DR/BCP, on-call, and handover documentation

Generate documentation site with governance dashboard, document viewer, and Mermaid diagram support

Create project plan with timeline, phases, gates, and Mermaid diagrams

Create platform strategy using Platform Design Toolkit (8 canvases for multi-sided ecosystems)

Generate MARP presentation slides from existing project artifacts for governance boards and stakeholder briefings

Assess compliance with architecture principles and generate scorecard with evidence, gaps, and recommendations

Create or update enterprise architecture principles

Create comprehensive business and technical requirements

Research technology, services, and products to meet requirements with build vs buy analysis

Create comprehensive risk register following HM Treasury Orange Book principles

Create strategic architecture roadmap with multi-year timeline, capability evolution, and governance

Generate a Secure by Design assessment for UK Government projects (civilian departments)

Prepare for GDS Service Standard assessment - analyze evidence against 14 points, identify gaps, generate readiness report

Create comprehensive ServiceNow service design with CMDB, SLAs, incident management, and change control

Create Strategic Outline Business Case (SOBC) using UK Government Green Book 5-case model

Generate Statement of Work (SOW) / RFP document for vendor procurement

Analyze stakeholder drivers, goals, and measurable outcomes

Get oriented with ArcKit — check project status, explore available commands, and choose your next step

Generate comprehensive project story with timeline analysis, traceability, and governance achievements (project)

Synthesise strategic artifacts into executive-level Architecture Strategy document

Generate a Technology Code of Practice (TCoP) review document for a UK Government technology project

Create new document templates by interviewing the user about their organization's requirements

Generate requirements traceability matrix from requirements to design to tests

Export product backlog to Trello - create board, lists, cards with labels and checklists from backlog JSON

Create strategic Wardley Maps for architecture decisions and build vs buy analysis

Use this agent when the user needs AWS-specific technology research using the AWS Knowledge MCP server to match project requirements to AWS services, architecture patterns, Well-Architected guidance, and Security Hub controls. Examples: <example> Context: User has a project with requirements and wants AWS service recommendations user: "/arckit:aws-research Research AWS services for microservices platform" assistant: "I'll launch the AWS research agent to match your requirements to AWS services using official AWS documentation via the MCP server. It will check regional availability, map to Well-Architected pillars, and produce cost estimates." <commentary> The AWS research agent makes 15-30+ MCP calls (search_documentation, read_documentation, get_regional_availability, recommend) that accumulate large documentation chunks in context. Running as an agent keeps this isolated. </commentary> </example> <example> Context: User wants to know which AWS services to use for their UK Government project user: "What AWS services should we use for this project?" assistant: "I'll launch the AWS research agent to research AWS services for your project, including UK region availability, G-Cloud status, and NCSC compliance." <commentary> Any request for AWS-specific service recommendations should trigger this agent since it involves heavy MCP documentation retrieval. </commentary> </example> <example> Context: User wants AWS architecture patterns and cost estimates user: "/arckit:aws-research AWS options for UK Government data analytics platform" assistant: "I'll launch the AWS research agent to research data analytics services on AWS, check eu-west-2 availability, verify G-Cloud procurement, and produce cost estimates with Well-Architected assessment." <commentary> UK Government AWS research needs regional availability checks, G-Cloud verification, and NCSC compliance — all requiring multiple MCP calls. </commentary> </example>

Use this agent when the user needs Azure-specific technology research using the Microsoft Learn MCP server to match project requirements to Azure services, architecture patterns, Well-Architected guidance, and Security Benchmark controls. Examples: <example> Context: User has a project with requirements and wants Azure service recommendations user: "/arckit:azure-research Research Azure services for microservices platform" assistant: "I'll launch the Azure research agent to match your requirements to Azure services using official Microsoft documentation via the MCP server. It will check UK region availability, map to Well-Architected pillars, and produce cost estimates." <commentary> The Azure research agent makes 15-30+ MCP calls (microsoft_docs_search, microsoft_docs_fetch, microsoft_code_sample_search) that accumulate large documentation chunks in context. Running as an agent keeps this isolated. </commentary> </example> <example> Context: User wants to know which Azure services to use for their UK Government project user: "What Azure services should we use for this project?" assistant: "I'll launch the Azure research agent to research Azure services for your project, including UK region availability, G-Cloud status, and NCSC compliance." <commentary> Any request for Azure-specific service recommendations should trigger this agent since it involves heavy MCP documentation retrieval. </commentary> </example> <example> Context: User wants Azure architecture patterns and cost estimates user: "/arckit:azure-research Azure options for UK Government data analytics platform" assistant: "I'll launch the Azure research agent to research data analytics services on Azure, check UK South/West availability, verify G-Cloud procurement, and produce cost estimates with Well-Architected assessment." <commentary> UK Government Azure research needs regional availability checks, G-Cloud verification, and NCSC compliance — all requiring multiple MCP calls. </commentary> </example>

Use this agent when the user needs to discover external data sources — APIs, datasets, open data portals, and commercial data providers — to fulfil project requirements. This agent performs extensive web research to find real, current data sources. Examples: <example> Context: User has a project with requirements and wants to find external data sources user: "/arckit:datascout Discover data sources for the fuel price transparency project" assistant: "I'll launch the datascout agent to discover external data sources for the fuel price transparency project. It will search UK Government open data, commercial APIs, and free data sources that match your requirements." <commentary> The datascout agent is ideal here because it needs to perform many WebSearch and WebFetch calls to discover APIs, check documentation, verify rate limits, and assess data quality. Running as an agent keeps this research isolated. </commentary> </example> <example> Context: User wants to find APIs and datasets for their project user: "What external data sources and APIs are available for this project?" assistant: "I'll launch the datascout agent to systematically discover and evaluate external data sources, APIs, and datasets that can fulfil your project's data requirements." <commentary> Any request for external data source discovery should trigger this agent since it involves heavy web research across government portals, API catalogues, and commercial providers. </commentary> </example> <example> Context: User needs UK Government open data for their project user: "Find what government open data we can use for the smart meter app" assistant: "I'll launch the datascout agent to search UK Government open data portals, the API catalogue at api.gov.uk, and data.gov.uk for relevant datasets and APIs." <commentary> UK Government data discovery requires searching multiple portals (api.gov.uk, data.gov.uk, department developer hubs) which benefits from agent isolation. </commentary> </example>

Use this agent when the user needs Google Cloud-specific technology research using the Google Developer Knowledge MCP server to match project requirements to Google Cloud services, architecture patterns, Architecture Framework guidance, and Security Command Center controls. Examples: <example> Context: User has a project with requirements and wants Google Cloud service recommendations user: "/arckit:gcp-research Research Google Cloud services for microservices platform" assistant: "I'll launch the Google Cloud research agent to match your requirements to Google Cloud services using official Google documentation via the MCP server. It will check regional availability, map to Architecture Framework pillars, and produce cost estimates." <commentary> The Google Cloud research agent makes 15-30+ MCP calls (search_documents, get_document, batch_get_documents) that accumulate large documentation chunks in context. Running as an agent keeps this isolated. </commentary> </example> <example> Context: User wants to know which Google Cloud services to use for their UK Government project user: "What Google Cloud services should we use for this project?" assistant: "I'll launch the Google Cloud research agent to research Google Cloud services for your project, including europe-west2 region availability, G-Cloud status, and NCSC compliance." <commentary> Any request for Google Cloud-specific service recommendations should trigger this agent since it involves heavy MCP documentation retrieval. </commentary> </example> <example> Context: User wants Google Cloud architecture patterns and cost estimates user: "/arckit:gcp-research Google Cloud options for UK Government data analytics platform" assistant: "I'll launch the Google Cloud research agent to research data analytics services on Google Cloud, check europe-west2 availability, verify G-Cloud procurement, and produce cost estimates with Architecture Framework assessment." <commentary> UK Government Google Cloud research needs regional availability checks, G-Cloud verification, and NCSC compliance — all requiring multiple MCP calls. </commentary> </example>

Use this agent when the user needs technology and service market research for a project, including build vs buy analysis, vendor evaluation, TCO comparison, and UK Government Digital Marketplace search. This agent performs extensive web research autonomously. Examples: <example> Context: User has a project with requirements and wants to research available technology solutions user: "/arckit:research Research technology options for the NHS appointment booking project" assistant: "I'll launch the research agent to conduct market research for the NHS appointment booking project. It will search for vendors, open source options, UK Government platforms, and produce a build vs buy analysis with TCO comparison." <commentary> The research agent is ideal here because it needs to perform dozens of WebSearch and WebFetch calls to gather vendor pricing, reviews, and product details. Running as an agent keeps this context-heavy work isolated. </commentary> </example> <example> Context: User wants to explore technology options after creating requirements user: "Can you research what platforms and services we could use for this project?" assistant: "I'll launch the research agent to discover and evaluate technology options based on your project requirements." <commentary> Even without the explicit slash command, the request for technology/service research should trigger this agent since it involves heavy web research. </commentary> </example> <example> Context: User wants build vs buy analysis user: "Should we build or buy for authentication and payment processing?" assistant: "I'll launch the research agent to perform a detailed build vs buy analysis for authentication and payment processing, including vendor comparison and TCO estimates." <commentary> Build vs buy analysis requires extensive vendor research with pricing, which benefits from agent isolation. </commentary> </example>

This skill should be used when the user asks about Mermaid diagram syntax, how to write a specific Mermaid diagram type, flowchart node shapes, Gantt date formats, sequence diagram arrows, ER diagram cardinality, C4 diagram syntax, state diagram notation, pie chart syntax, timeline formatting, mindmap indentation, quadrant chart axes, or any Mermaid configuration and theming question. Triggers: mermaid syntax, how to write a flowchart, gantt chart date format, sequence diagram arrows, entity relationship diagram, mermaid class diagram, state diagram syntax, pie chart mermaid, mindmap mermaid, timeline diagram, quadrant chart, c4 diagram mermaid, mermaid theming, mermaid configuration, mermaid layout, block diagram, kanban board mermaid, architecture diagram mermaid, radar chart, sankey diagram, git graph mermaid, user journey diagram, what mermaid diagram types are available, mermaid node shapes, mermaid edge labels, mermaid styling.

This skill should be used when the user asks about PlantUML diagram syntax, C4-PlantUML library, PlantUML sequence diagrams, component diagrams, deployment diagrams, class diagrams, state diagrams, activity diagrams, ER diagrams, use case diagrams, PlantUML skinparams, themes, layout hints, Rel_Down, Rel_Right, Lay_Right, Lay_Down, or PlantUML directional hints. Triggers: plantuml syntax, C4-PlantUML, plantuml sequence diagram, plantuml class diagram, plantuml component diagram, plantuml deployment diagram, plantuml ER diagram, plantuml layout, plantuml skinparams, plantuml themes, plantuml icons, plantuml styling, Rel_Down vs Lay_Right, plantuml layout conflicts, plantuml activity diagram, plantuml state diagram, plantuml use case diagram, plantuml common errors.

This skill should be used when the user asks about Wardley Mapping, evolution stages, strategic positioning, situational awareness, technology evolution, or competitive landscape. Covers creating maps, gameplay patterns, doctrine, build vs. buy, inertia, and quantitative analysis. Triggers: create a wardley map, map our value chain, what evolution stage is this component, should we build or buy, assess doctrine maturity, analyze competitive landscape, where should we invest vs outsource, score evolution, calculate ubiquity, quantitative positioning, evolution formula, differentiation pressure, commodity leverage, weak signal detection, readiness score.

11 hooks across 5 events