prakhar1114/ai_mime/example-skill

Fetch Weather Report Skill

Inputs

• location (required, string): The city and state/country to search weather for (e.g. "San Francisco, CA").
• units (optional, string): The unit system to use, either "metric" or "imperial". Default is "metric".

Run

Run via the executable bash script:

./run.sh [path/to/inputs.json]

Python runtime contract:

• run.sh uses the first available interpreter in this order: skill .venv/bin/python, workflow .venv/bin/python, then required $AI_MIME_PYTHON_PATH.
• If requirements.txt exists, include these exact build/repair commands for the developer to set up the virtualenv before packaging or for manual troubleshooting:

``bash "$AI_MIME_UV_PATH" venv .venv --python "$AI_MIME_PYTHON_PATH" "$AI_MIME_UV_PATH" pip install -r requirements.txt --python .venv/bin/python ``

• State clearly that the install commands are for skill build or manual repair. The automated runtime does not create or repair .venv when executing the skill.

Outputs

• weather_summary (dict):
• location (string): Resolved location name.
• temperature (float): Current temperature.
• condition (string): Weather condition description.

Progress logs

The script outputs progress logs on stderr to track execution progress. All logs must be written in clear, natural language suitable for an end-user overlay. Do not use structured JSON logs.

• "Fetching weather from API..."
• "It is sunny with 18.5 C"
• "Error: API timeout"

Fallback

If the weather API fails or is unreachable, the execution falls back to performing a Google search for current weather and scraping the temperature using browser_harness. See references/fallback_plan.md for manual or automated fallback instructions.

ask_llm decision points

1. Weather Condition Parsing:

If the weather condition string returned by the API is fuzzy, the script calls ask_llm to categorize the weather condition into standard types ("Sunny", "Cloudy", "Rainy", "Snowy", "Unknown"). ``python from llm_resolver import ask_llm decision = ask_llm( prompt=f"Categorize this weather description: '{raw_desc}'", schema={ "type": "object", "properties": { "category": {"type": "string", "enum": ["Sunny", "Cloudy", "Rainy", "Snowy", "Unknown"]} }, "required": ["category"] } ) ``

References

• fallback_plan.md: Step-by-step instructions for human/UI agent fallback execution.