d3300c7db9
Two new OpenSpec skills for porting features to sandboxed codebases: - /opsx:extract-feature generates minimal, printable code recipes - /opsx:export-spec generates compact specs for AI-assisted reimplementation Both support cumulative dependency analysis across archived changes. Includes first export of migrate-to-semantic-kernel in all three formats: code recipe (~120 lines), portable spec (~40 lines), OpenSpec variant (~25 lines). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
77 lines
3.1 KiB
Markdown
77 lines
3.1 KiB
Markdown
# OpenSpec Portable Variant
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## For hand-typing into target's OpenSpec on the sandbox
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Type these two files into the target's `openspec/changes/add-sk-chat/` directory.
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Then run `/opsx:apply` and let the AI implement from the tasks.
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**Lines to type**: ~25 | **vs code recipe**: ~120 lines | **Compression**: 4.8x
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---
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## File 1: proposal.md
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```markdown
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# Add SK Chat Endpoint with Tool Calling
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## Why
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Need an AI chat endpoint that streams responses and supports
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autonomous tool calling for structured data extraction/validation.
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## What Changes
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- Add Semantic Kernel 1.74.0 with OpenAI connector
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- POST /api/chat endpoint streaming SSE via SK
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- ExtractionPlugin with [KernelFunction] for field validation
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- Shared models: ChatRequest, ChatMessage, ExtractedFields, ValidationResult
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## Impact
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- API project: new controller, plugin, DI wiring
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- Shared project: new models
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- Config: ResponsesApi section in appsettings.json
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```
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## File 2: tasks.md
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```markdown
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## 1. Shared Models
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- [ ] 1.1 Create ChatMessage (Role string, Content string, Timestamp DateTime)
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- [ ] 1.2 Create ChatRequest (Messages List<ChatMessage>)
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- [ ] 1.3 Create ExtractedFields with required fields: Client, Project, Hours(decimal?), Rate(decimal?), Currency, Date; optional: Description, PoNumber
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- [ ] 1.4 Create ValidationResult (IsValid bool, Errors List<string>)
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## 2. Extraction Plugin
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- [ ] 2.1 Create ExtractionPlugin class with [KernelFunction("validate_extracted_fields")]
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- [ ] 2.2 Accepts fieldsJson string, deserializes to ExtractedFields with PropertyNameCaseInsensitive
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- [ ] 2.3 Validates required fields non-null/non-empty, decimals > 0
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- [ ] 2.4 Returns JSON serialized ValidationResult
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## 3. Chat Controller
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- [ ] 3.1 Create ChatController [ApiController] Route("api/[controller]") injecting Kernel
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- [ ] 3.2 POST endpoint: set response to text/event-stream, no-cache
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- [ ] 3.3 Convert request messages to SK ChatHistory (user/assistant roles)
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- [ ] 3.4 Import ExtractionPlugin per-request via _kernel.ImportPluginFromObject
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- [ ] 3.5 Use OpenAIPromptExecutionSettings with FunctionChoiceBehavior.Auto()
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- [ ] 3.6 Stream via GetStreamingChatMessageContentsAsync, emit SSE: data: {"text":"..."}\n\n
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- [ ] 3.7 Emit data: [DONE]\n\n on completion
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- [ ] 3.8 Handle HttpRequestException (emit error SSE), TaskCanceledException (silent)
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## 4. DI Wiring (Program.cs)
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- [ ] 4.1 Add using Microsoft.SemanticKernel at top of Program.cs
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- [ ] 4.2 Read BaseUrl and Model from config "ResponsesApi" section
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- [ ] 4.3 AddOpenAIChatCompletion(modelId, endpoint with /v1 suffix, apiKey)
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- [ ] 4.4 AddKernel()
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- [ ] 4.5 AddSingleton<ExtractionPlugin>()
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## 5. Configuration
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- [ ] 5.1 Add ResponsesApi section to appsettings.json: BaseUrl "http://localhost:8317/v1", Model "claude-sonnet-4-6"
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- [ ] 5.2 Add NuGet: Microsoft.SemanticKernel 1.74.0, Microsoft.SemanticKernel.Connectors.OpenAI 1.74.0
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```
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---
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## Usage on sandbox
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1. Create the change: `openspec new change "add-sk-chat"`
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2. Type `proposal.md` into `openspec/changes/add-sk-chat/proposal.md`
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3. Type `tasks.md` into `openspec/changes/add-sk-chat/tasks.md`
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4. Run `/opsx:apply add-sk-chat` — the AI implements all tasks
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