Information about the context of script execution is available in the env global object.

The env global object contains properties that provide information about the script execution context. env is populated automatically by the GenAIScript runtime.

The env.files array contains all files within the execution context. The context is defined implicitly by the user based on:

• script files option

script({
    files: "**/*.pdf",
})

or multiple paths

script({
    files: ["src/*.pdf", "other/*.pdf"],
})

• the UI location to start the tool

• CLI files arguments.

The files are stored in env.files which can be injected in the prompt.

• using def

• filtered,

def("DOCS", env.files, { endsWith: ".md" })
def("CODE", env.files, { endsWith: ".py" })

• directly in a $ call

$`Summarize ${env.files}.

In this case, the prompt is automatically expanded with a def call and the value of env.files.

// expanded
const files = def("FILES", env.files, { ignoreEmpty: true })
$`Summarize ${files}.

The vars property contains the variables that have been defined in the script execution context.

// grab locale from variable or default to en-US
const locale = env.vars.locale || "en-US"

Read more about variables.

The def("FILE", file) function is a shorthand for generating a fenced variable output.

It renders approximately to

FILE:
```file="filename"
file content
```

or if the model support XML tags (see fence formats):

<FILE file="filename">
file content
</FILE>

The def function can also be used with an array of files, such as env.files.

You can specify the language of the text contained in def. This can help GenAIScript optimize the rendering of the text.

// hint that the output is a diff
def("DIFF", gitdiff, { language: "diff" })

The def function returns a variable name that can be used in the prompt. The name might be formatted differently to accommodate the model’s preference.

const f = def("FILE", file)
$`Summarize ${f}.`

Since a script may be executed on a full folder, it is often useful to filter the files based on

• their extension

def("FILE", env.files, { endsWith: ".md" })

• or using a glob:

def("FILE", files, { glob: "**/*.{md,mdx}" })

By default, if def is used with an empty array of files, it will cancel the prompt. You can override this behavior by setting ignoreEmpty to true.

def("FILE", env.files, { endsWith: ".md", ignoreEmpty: true })

You can extract content around a specific line number using the line option. This is particularly useful when you want to focus on a specific area of interest in large files.

// Focus on line 25 with dynamic context
def("FUNCTION_CODE", fileContent, { line: 25 })

The line option dynamically calculates the surrounding context based on file size:

• Very small files (≤20 lines): Include most content
• Small files (≤100 lines): 15 lines on each side
• Medium files (≤500 lines): 25 lines on each side
• Large files (≤2000 lines): 50 lines on each side
• Very large files (>2000 lines): 75 lines on each side

When combined with maxTokens, the line option performs intelligent token-aware range calculation:

// Focus on line 25 with token budget constraint
def("FUNCTION_CODE", fileContent, { line: 25, maxTokens: 500 })

The implementation:

• Smart Expansion: Starts with the center line and expands alternately up/down until token budget is reached
• Accurate Counting: Uses precise token estimation for better control
• Graceful Fallback: Falls back to file-size-based calculation when no maxTokens specified
• Budget Overflow: Returns just the center line if it already exceeds the token budget

Explicit line ranges take precedence over the line option:

// lineStart/lineEnd override line option and maxTokens
def("EXPLICIT_WINS", codeFile, {
  lineStart: 10,
  lineEnd: 20,
  line: 50,
  maxTokens: 100
}) // Uses lines 10-20

It is possible to limit the number of tokens that are generated by the def function. This can be useful when the output is too large and the model has a token limit. The maxTokens option can be set to a number to limit the number of tokens generated for each individual file.

def("FILE", env.files, { maxTokens: 100 })

When used with the line option, maxTokens controls the total size of the extracted range around the center line rather than truncating individual files.

The def function treats data files such as CSV and XLSX specially. It will automatically convert the data into a markdown table format to improve tokenization.

• sliceHead, keep the top N rows

def("FILE", env.files, { sliceHead: 100 })

• sliceTail, keep the last N rows

def("FILE", env.files, { sliceTail: 100 })

• sliceSample, keep a random sample of N rows

def("FILE", env.files, { sliceSample: 100 })

You can use cacheControl: "ephemeral" to specify that the prompt can be cached for a short amount of time, and enable prompt caching optimization, which is supported (differently) by various LLM providers.

$`...`.cacheControl("ephemeral")

def("FILE", env.files, { cacheControl: "ephemeral" })

Read more about prompt caching.

You can schedule a check for prompt injection/jai break with your configured content safety provider.

def("FILE", env.files, { detectPromptInjection: true })

Some models, like OpenAI gpt-4o and gpt-4o-mini, support specifying a predicted output (with some limitations). This helps reduce latency for model responses where much of the response is known ahead of time. This can be helpful when asking the LLM to edit specific files.

Set the prediction: true flag to enable it on a def call. Note that only a single file can be predicted.

def("FILE", env.files[0], { prediction: true })

The defData function offers additional formatting options for converting a data object into a textual representation. It supports rendering objects as YAML, JSON, or CSV (formatted as a Markdown table).

// render to markdown-ified CSV by default
defData("DATA", data)
// render as yaml
defData("DATA", csv, { format: "yaml" })

The defData function also supports functions to slice the input rows and columns.

• headers, list of column names to include
• sliceHead, number of rows or fields to include from the beginning
• sliceTail, number of rows or fields to include from the end
• sliceSample, number of rows or fields to pick at random
• distinct, list of column names to deduplicate the data based on
• query, a jq query to filter the data

defData("DATA", data, {
    sliceHead: 5,
    sliceTail: 5,
    sliceSample: 100,
})

You can leverage the data filtering functionality using parsers.tidyData as well.

It is very common to compare two pieces of data and ask the LLM to analyze the differences. Using diffs is a great way to naturally compress the information since we only focus on differences!

The defDiff takes care of formatting the diff in a way that helps LLM reason. It behaves similarly to def and assigns a name to the diff.

// diff files
defDiff("DIFF", env.files[0], env.files[1])
// diff strings
defDiff("DIFF", "cat", "dog")
// diff objects
defDiff("DIFF", { name: "cat" }, { name: "dog" })

You can leverage the diff functionality using parsers.diff.