llm-query

name	llm-query
description	Ask an internal LLM a focused question about large text or structured content using the query_with_llm CodeExecution callback. Use when content is too large or noisy to inspect manually and you need a concise answer with line citations.

LLM Query Skill

When to Use

• You already have large text or structured JSON inside CodeExecution and need a focused answer.
• You need line citations into the provided content.
• You want to avoid logging or printing a large payload into CodeExecution output.

Available Functions

query_with_llm({ content, query, truncationStrategy? })

Ask an internal LLM a narrow question about provided content.

Parameters:

• content (string | JSON value, required): content to ask about. String content is used as-is; structured JSON is formatted.
• query (string, required): non-empty question or instruction about the content.
• truncationStrategy ("head_tail" | "head_only" | "none", optional): defaults to "head_tail". Use "none" when the complete content must fit and failing is better than truncating.

Returns: string answer. When referencing specific details, the answer should cite relevant content line numbers.

Example:

const answer = await query_with_llm({
  content: largeLogText,
  query: 'What is the first error and likely root cause?',
});
console.log(answer);

Structured example:

const answer = await query_with_llm({
  content: { files: changedFiles, diagnostics },
  query: 'Which diagnostic should be fixed first?',
  truncationStrategy: 'head_tail',
});
console.log(answer);

Best Practices

1. Ask a narrow question; do not use this callback as a general chat model.
2. Do not echo the full input or full answer unless the user needs it; summarize the relevant result.
3. Use truncationStrategy: "none" when truncation could hide critical data, and handle the size error.
4. The callback redacts tracing for safety, but the answer is still returned to your script. Treat source code, tokens, keys, and secrets carefully.