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geutebruck/geutebruck-api/.claude/commands/speckit.plan.md
Administrator 14893e62a5 feat: Geutebruck GeViScope/GeViSoft Action Mapping System - MVP 
This MVP release provides a complete full-stack solution for managing action mappings
in Geutebruck's GeViScope and GeViSoft video surveillance systems.

## Features

### Flutter Web Application (Port 8081)
- Modern, responsive UI for managing action mappings
- Action picker dialog with full parameter configuration
- Support for both GSC (GeViScope) and G-Core server actions
- Consistent UI for input and output actions with edit/delete capabilities
- Real-time action mapping creation, editing, and deletion
- Server categorization (GSC: prefix for GeViScope, G-Core: prefix for G-Core servers)

### FastAPI REST Backend (Port 8000)
- RESTful API for action mapping CRUD operations
- Action template service with comprehensive action catalog (247 actions)
- Server management (G-Core and GeViScope servers)
- Configuration tree reading and writing
- JWT authentication with role-based access control
- PostgreSQL database integration

### C# SDK Bridge (gRPC, Port 50051)
- Native integration with GeViSoft SDK (GeViProcAPINET_4_0.dll)
- Action mapping creation with correct binary format
- Support for GSC and G-Core action types
- Proper Camera parameter inclusion in action strings (fixes CrossSwitch bug)
- Action ID lookup table with server-specific action IDs
- Configuration reading/writing via SetupClient

## Bug Fixes
- **CrossSwitch Bug**: GSC and G-Core actions now correctly display camera/PTZ head parameters in GeViSet
- Action strings now include Camera parameter: `@ PanLeft (Comment: "", Camera: 101028)`
- Proper filter flags and VideoInput=0 for action mappings
- Correct action ID assignment (4198 for GSC, 9294 for G-Core PanLeft)

## Technical Stack
- **Frontend**: Flutter Web, Dart, Dio HTTP client
- **Backend**: Python FastAPI, PostgreSQL, Redis
- **SDK Bridge**: C# .NET 8.0, gRPC, GeViSoft SDK
- **Authentication**: JWT tokens
- **Configuration**: GeViSoft .set files (binary format)

## Credentials
- GeViSoft/GeViScope: username=sysadmin, password=masterkey
- Default admin: username=admin, password=admin123

## Deployment
All services run on localhost:
- Flutter Web: http://localhost:8081
- FastAPI: http://localhost:8000
- SDK Bridge gRPC: localhost:50051
- GeViServer: localhost (default port)

Generated with Claude Code (https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2025-12-31 18:10:54 +01:00

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description
description
Execute the implementation planning workflow using the plan template to generate design artifacts.

User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Outline

  1. Setup: Run .specify/scripts/powershell/setup-plan.ps1 -Json from repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot").

  2. Load context: Read FEATURE_SPEC and .specify/memory/constitution.md. Load IMPL_PLAN template (already copied).

  3. Execute plan workflow: Follow the structure in IMPL_PLAN template to:

    • Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
    • Fill Constitution Check section from constitution
    • Evaluate gates (ERROR if violations unjustified)
    • Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
    • Phase 1: Generate data-model.md, contracts/, quickstart.md
    • Phase 1: Update agent context by running the agent script
    • Re-evaluate Constitution Check post-design

  4. Stop and report: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.

Phases

Phase 0: Outline & Research

  1. Extract unknowns from Technical Context above:

    • For each NEEDS CLARIFICATION → research task
    • For each dependency → best practices task
    • For each integration → patterns task

  2. Generate and dispatch research agents:

    For each unknown in Technical Context:
  Task: "Research {unknown} for {feature context}"
For each technology choice:
  Task: "Find best practices for {tech} in {domain}"

  3. Consolidate findings in research.md using format:

    • Decision: [what was chosen]
    • Rationale: [why chosen]
    • Alternatives considered: [what else evaluated]

Output: research.md with all NEEDS CLARIFICATION resolved

Phase 1: Design & Contracts

Prerequisites: research.md complete

  1. Extract entities from feature spec → data-model.md:

    • Entity name, fields, relationships
    • Validation rules from requirements
    • State transitions if applicable

  2. Generate API contracts from functional requirements:

    • For each user action → endpoint
    • Use standard REST/GraphQL patterns
    • Output OpenAPI/GraphQL schema to /contracts/

  3. Agent context update:

    • Run .specify/scripts/powershell/update-agent-context.ps1 -AgentType claude
    • These scripts detect which AI agent is in use
    • Update the appropriate agent-specific context file
    • Add only new technology from current plan
    • Preserve manual additions between markers

Output: data-model.md, /contracts/*, quickstart.md, agent-specific file

Key rules

  • Use absolute paths
  • ERROR on gate failures or unresolved clarifications