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Skill for the LLM infrastructure module providing local Large Language Model integration via Ollama. Covers client initialization, prompt templates, output validation, manuscript review generation, conversation context, and CLI usage.
Passed 24 of 25 test cases with the skill, and 5 of 25 without it.
and 18 more in the full report
20 fixed4 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 20 test cases the skill fixed. Same model, same prompt, with and without the skill.
Show the terminal command using `uv` to list all currently installed local Ollama models.
| Checked | Without skill | With skill |
|---|---|---|
| Uses uv run python -m infrastructure.llm models | Missed | Passed |
The instructions your model reads when the skill loads.
Local Large Language Model integration for research assistance via Ollama.
flowchart LR
LLM[llm/]
LLM --> CORE[core<br/>client · config · context]
LLM --> TPL[templates<br/>prompt templates for research]
LLM --> VAL[validation<br/>output quality validation]
LLM --> RV[review<br/>manuscript review generation]
LLM --> PR[prompts<br/>fragment composition system]
LLM --> UT[utils<br/>Ollama server management]
LLM --> CLI[cli<br/>command-line interface]
classDef d fill:#0f172a,stroke:#0f172a,color:#fff
classDef pkg fill:#1e3a8a,stroke:#0f172a,color:#fff
class LLM d
class CORE,TPL,VAL,RV,PR,UT,CLI pkg
core/client.py)from infrastructure.llm import LLMClient, OllamaClientConfig, GenerationOptions
# Initialize with defaults
client = LLMClient()
# Custom configuration
config = OllamaClientConfig(default_model="gemma3:4b", temperature=0.7)
client = LLMClient(config)
# Generate a response
response = client.query("Summarize this paper...", options=GenerationOptions(
max_tokens=2000,
temperature=0.3,
))
test cases passed, vs 5 / 25 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.