Example skill

Skill prakhar1114/ai_mime/src/ai_mime/agent_runner/instructions/example_skill

Automation harness that compiles a screen recording into a rerunnable script, with LLMs, only at decision points and self-healing reruns.

Install

npx -y skills add prakhar1114/ai_mime --skill example_skill

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Fetch the current weather forecast for a specified location and return a structured summary.

SKILL.md

2.6 KB, as published. Nobody here has run it

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 $AI_MIME_PYTHON_PATH.
If requirements.txt exists, include these exact build/repair commands for the developer to set up the virtualenv before packaging or for manual troubleshooting:
```
"$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

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").

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.