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llm-integration LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.
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npx skills add https://github.com/yonatangross/orchestkit --skill llm-integrationコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Zipをダウンロード ダウンロード中... このリポジトリの他の Skills Chain patterns for multi-phase pipelines: MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building or debugging a pipeline skill.
OrchestKit doctor for health diagnostics across manifest integrity, hook configuration, skill validation, agent frontmatter, MCP server connectivity, CC version compatibility, and permission rules. Reports issues with severity levels and auto-remediation suggestions. Validates component counts, detects orphaned entries, and checks CC version matrix compliance. Use when diagnosing plugin health, troubleshooting configuration issues, or running pre-release checks.
Inspects the OrchestKit telemetry pipeline for the current project — lists all known telemetry files with write counts, sizes, schema status, growth trend, and orphan detection. Use when verifying the observability pipeline is healthy, debugging a missing writer, or auditing which files have schema locks vs. which are drift-vulnerable. Read-only — never modifies telemetry files.
name llm-integration license MIT compatibility Claude Code 2.1.220+. description LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs. tags ["llm","function-calling","streaming","ollama","fine-tuning","lora","tool-use","local-inference"] context fork agent llm-integrator version 2.0.0 author OrchestKit user-invocable false disable-model-invocation true complexity medium persuasion-type reference metadata {"category":"mcp-enhancement"} allowed-tools ["Read","Glob","Grep","WebFetch","WebSearch"]
LLM Integration
Patterns for integrating LLMs into production applications: tool use, streaming, local inference, and fine-tuning. Each category has individual rule files in rules/ loaded on-demand.
Quick Reference
Category Rules Impact When to Use Function Calling 3 CRITICAL Tool definitions, parallel execution, input validation Streaming 3 HIGH SSE endpoints, structured streaming, backpressure handling Local Inference 3 HIGH Ollama setup, model selection, GPU optimization Fine-Tuning 3 HIGH LoRA/QLoRA training, dataset preparation, evaluation Context Optimization 2 HIGH Window management, compression, caching, budget scaling Evaluation 2 HIGH LLM-as-judge, RAGAS metrics, quality gates, benchmarks Prompt Engineering 4 HIGH CoT, few-shot, versioning, DSPy optimization, ReAct, cost optimization
Total: 20 rules across 7 categories
Quick Start
tools = [{
"type" : "function" ,
"function" : {
"name" : "search_documents" ,
"description" : "Search knowledge base" ,
"strict" : True ,
"parameters" : {
"type" : "object" ,
"properties" : {
"query" : {"type" : "string"
"description"
"Search query"
"limit"
"type"
"integer"
"description"
"Max results"
"required"
"query"
"limit"
"additionalProperties"
False
@app.get("/chat/stream"
async def stream_chat (prompt: str ):
async def generate ():
async for token in async_stream(prompt):
yield {"event" : "token" , "data" : token}
yield {"event" : "done" , "data" : "" }
return EventSourceResponse(generate())
llm = ChatOllama(
model="deepseek-r1:70b" ,
base_url="http://localhost:11434" ,
temperature=0.0 ,
num_ctx=32768 ,
)
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/Meta-Llama-3.1-8B" ,
max_seq_length=2048 , load_in_4bit=True ,
)
model = FastLanguageModel.get_peft_model(model, r=16 , lora_alpha=32 )
Function Calling Enable LLMs to use external tools and return structured data. Use strict mode schemas (2026 best practice) for reliability. Limit to 5-15 tools per request, validate all inputs with Pydantic/Zod, and return errors as tool results.
calling-tool-definition.md -- Strict mode schemas, OpenAI/Anthropic formats, LangChain bindingcalling-parallel.md -- Parallel tool execution, asyncio.gather, strict mode constraintscalling-validation.md -- Input validation, error handling, tool execution loops
Streaming Deliver LLM responses in real-time for better UX. Use SSE for web, WebSocket for bidirectional. Handle backpressure with bounded queues.
streaming-sse.md -- FastAPI SSE endpoints, frontend consumers, async iteratorsstreaming-structured.md -- Streaming with tool calls, partial JSON parsing, chunk accumulationstreaming-backpressure.md -- Backpressure handling, bounded buffers, cancellation
Local Inference Run LLMs locally with Ollama for cost savings (93% vs cloud), privacy, and offline development. Pre-warm models, use provider factory for cloud/local switching.
local-ollama-setup.md -- Installation, model pulling, environment configurationlocal-model-selection.md -- Model comparison by task, hardware profiles, quantizationlocal-gpu-optimization.md -- Apple Silicon tuning, keep-alive, CI integration
Fine-Tuning Customize LLMs with parameter-efficient techniques. Fine-tune ONLY after exhausting prompt engineering and RAG. Requires 1000+ quality examples.
tuning-lora.md -- LoRA/QLoRA configuration, Unsloth training, adapter mergingtuning-dataset-prep.md -- Synthetic data generation, quality validation, deduplicationtuning-evaluation.md -- DPO alignment, evaluation metrics, anti-patterns
Context Optimization Manage context windows, compression, and attention-aware positioning. Optimize for tokens-per-task.
context-window-management.md -- Five-layer architecture, anchored summarization, compression triggerscontext-caching.md -- Just-in-time loading, budget scaling, probe evaluation, CC 2.1.32+
Evaluation Evaluate LLM outputs with multi-dimension scoring, quality gates, and benchmarks.
evaluation-metrics.md -- LLM-as-judge, RAGAS metrics, hallucination detectionevaluation-benchmarks.md -- Quality gates, batch evaluation, pairwise comparison
Prompt Engineering Design, version, and optimize prompts for production LLM applications.
prompt-design.md -- Chain-of-Thought, few-shot learning, pattern selection guideprompt-testing.md -- Langfuse versioning, DSPy optimization, A/B testing, self-consistencyprompt-react-pattern.md -- ReAct loop for tool-using agents, thought-action-observation formatprompt-optimization.md -- Token reduction, cost optimization, model tiering, prompt spec format
Upstream coverage (do not restate) These topics are covered by their vendors' own documentation. This skill points
at them instead of teaching them; the rules above keep only our floors, ceilings
and scars. Our delta on all of it is in references/ork-delta.md.
Topic First-party source Strict-mode tool schemas, structured outputs https://platform.openai.com/docs/guides/function-calling Anthropic input_schema / tool_use https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview SSE client mechanics, reconnection, cancellation https://developer.mozilla.org/en-US/docs/Web/API/EventSource LoRA / QLoRA config, target modules, adapter merging https://huggingface.co/docs/peft/developer_guides/lora Unsloth training loop, 4-bit loading https://docs.unsloth.ai/get-started/fine-tuning-llms-guide DPO, preference pairs, beta tuning, RLHF comparison https://huggingface.co/docs/trl/dpo_trainer SFT dataset formats (Alpaca, ChatML) https://huggingface.co/docs/trl/sft_trainer Embedding similarity for dataset dedup https://sbert.net/ Fine-tune vs prompt vs RAG decision framework https://platform.openai.com/docs/guides/optimizing-llm-accuracy Vendor token pricing (never hardcode it here) https://platform.openai.com/docs/pricing
Supporting Files
references/ork-delta.md -- our delta: scars, house ceilings, retired-file provenancereferences/model-selection.md -- local model comparison by task and hardwarescripts/create-lora-config.md -- LoRA config scaffold with auto-detected model type
Key Decisions Decision Recommendation Tool schema mode strict: true (2026 best practice)Tool count 5-15 max per request Streaming protocol SSE for web, WebSocket for bidirectional Buffer size 50-200 tokens Local model (reasoning) deepseek-r1:70bLocal model (coding) qwen2.5-coder:32bFine-tuning approach LoRA/QLoRA (try prompting first) LoRA rank 16-64 typical Training epochs 1-3 (more risks overfitting) Context compression Anchored iterative (60-80%) Compress trigger 70% utilization, target 50% Judge model claude-haiku-4-5-20251001 (cost tier) or gpt-5.5Quality threshold 0.7 production, 0.6 drafts Few-shot examples 3-5 diverse, representative Prompt versioning Langfuse with labels Auto-optimization DSPy MIPROv2
Related Skills
ork:rag-retrieval -- Embedding patterns, when RAG is better than fine-tuningagent-loops -- Multi-step tool use with reasoningllm-evaluation -- Evaluate fine-tuned and local modelslangfuse-observability -- Track training experiments
Capability Details
function-calling Keywords: tool, function, define tool, tool schema, function schema, strict mode, parallel tools
Solves:
Define tools with clear descriptions and strict schemas
Execute tool calls in parallel with asyncio.gather
Validate inputs and handle errors in tool execution loops
streaming Keywords: streaming, SSE, Server-Sent Events, real-time, backpressure, token stream
Solves:
Stream LLM tokens via SSE endpoints
Handle tool calls within streams
Manage backpressure with bounded queues
local-inference Keywords: Ollama, local, self-hosted, model selection, GPU, Apple Silicon
Solves:
Set up Ollama for local LLM inference
Select models based on task and hardware
Optimize GPU usage and CI integration
fine-tuning Keywords: LoRA, QLoRA, fine-tune, DPO, synthetic data, PEFT, alignment
Solves:
Configure LoRA/QLoRA for parameter-efficient training
Generate and validate synthetic training data
Align models with DPO and evaluate results
