Struct Args 

pub struct Args {

    pub prompt: String,
    pub transform: String,
    pub model: String,
    pub provider: Provider,
    pub visual: bool,
    pub heatmap: bool,
    pub orchestrator: bool,
    pub web: bool,
    pub port: u16,
    pub research: bool,
    pub runs: u32,
    pub output: String,
    pub system_a: Option<String>,
    pub top_logprobs: u8,
    pub system_b: Option<String>,
    pub db: Option<String>,
    pub significance: bool,
    pub heatmap_export: Option<String>,
    pub heatmap_min_confidence: f32,
    pub heatmap_sort_by: String,
    pub record: Option<String>,
    pub replay: Option<String>,
    pub rate: Option<f64>,
    pub seed: Option<u64>,
    pub log_db: Option<String>,
    pub baseline: bool,
    pub prompt_file: Option<String>,
    pub diff_terminal: bool,
    pub json_stream: bool,
    pub completions: Option<Shell>,
    pub rate_range: Option<String>,
    pub dry_run: bool,
    pub template: Option<String>,
    pub min_confidence: Option<f64>,
    pub format: String,
    pub collapse_window: usize,
    pub orchestrator_url: String,
    pub max_retries: u32,
    pub anthropic_max_tokens: u32,
    pub synonym_file: Option<String>,
    pub api_key: Option<String>,
    pub replay_speed: f64,
    pub timeout: u64,
    pub export_timeseries: Option<String>,
    pub json_schema: bool,
    pub list_models: Option<String>,
    pub validate_config: bool,
}

Fields

prompt: String

Input prompt to send to the LLM (optional when using –web)

transform: String

Transformation type (reverse, uppercase, mock, noise)

model: String

Model name (e.g. gpt-4, claude-sonnet-4-20250514)

provider: Provider

LLM provider: openai or anthropic

visual: bool

Enable visual mode with color-coded tokens

heatmap: bool

Enable token importance heatmap

orchestrator: bool

Route through tokio-prompt-orchestrator MCP pipeline at localhost:3000

web: bool

Launch web UI on localhost instead of terminal output

port: u16

Port for the web UI server

research: bool

Enable headless research mode — runs N times and outputs JSON stats

runs: u32

Number of runs in research mode

output: String

Output file path for research JSON (defaults to stdout)

system_a: Option<String>

System prompt A for A/B experiment mode

top_logprobs: u8

Number of top alternative tokens to return per position (OpenAI only, 0–20)

system_b: Option<String>

System prompt B for A/B experiment mode

db: Option<String>

Path to SQLite database for persisting experiment results (optional)

significance: bool

Compute statistical significance (two-sample t-test) when ≥2 A/B runs available

heatmap_export: Option<String>

Export per-position token confidence heatmap to CSV at this path

heatmap_min_confidence: f32

Minimum average confidence to include a position in heatmap CSV export (0.0–1.0)

heatmap_sort_by: String

Sort heatmap CSV rows by “position” (default) or “confidence”

record: Option<String>

Record token events to a JSON replay file at this path

replay: Option<String>

Replay token events from a JSON file (bypasses live LLM call)

rate: Option<f64>

Fraction of tokens to intercept and transform (0.0–1.0, default 0.5). At 0.5 every other token is transformed; at 0.3 roughly one in three. Uses a deterministic Bresenham spread so results are reproducible when combined with –seed.

Stored as Option<f64> so the config-file loader can distinguish “the user explicitly passed –rate” from “the user left it at the default”. The effective value is rate.unwrap_or(0.5).

seed: Option<u64>

Fixed RNG seed for reproducible Noise/Chaos transforms. Omit to use entropy-seeded randomness (default behaviour).

log_db: Option<String>

Path to SQLite experiment log database (requires sqlite-log feature)

baseline: bool

Enable per-position confidence baseline comparison (research mode)

prompt_file: Option<String>

Path to a file with one prompt per line for batch research

diff_terminal: bool

Run two parallel streams (OpenAI + Anthropic) and print side-by-side diff in terminal

json_stream: bool

Print one JSON line per token to stdout instead of colored text

completions: Option<Shell>

Generate shell completions for the given shell and exit

rate_range: Option<String>

Rate range for stochastic experiments, e.g. “0.3-0.7”. When set, the interceptor randomly picks a rate in [min, max] for each run. Overrides –rate when provided. Format: “MIN-MAX” (e.g. “0.2-0.8”).

dry_run: bool

Dry-run mode: show what transforms would be applied without calling any API. Applies the configured transform to a sample token list and prints results.

template: Option<String>

Prompt template with {input} placeholder. When set, the positional prompt is substituted into the template. Example: “Answer this: {input}”

min_confidence: Option<f64>

Only transform tokens whose API confidence is below this threshold. Tokens with confidence >= threshold are passed through unchanged. When no confidence data is available (Anthropic), falls back to rate-based selection. Range: 0.0–1.0. Example: –min-confidence 0.8

format: String

Output format for research mode: “json” (default), “jsonl” (one JSON object per line).

collapse_window: usize

Number of consecutive low-confidence tokens to consider a “collapse” in research mode. Default: 5.

orchestrator_url: String

Base URL for the MCP orchestrator pipeline (default: http://localhost:3000).

max_retries: u32

Maximum API retry attempts on 429/5xx errors (default: 3).

anthropic_max_tokens: u32

Maximum tokens in the Anthropic response (default: 4096). Ignored when using the OpenAI provider.

synonym_file: Option<String>

Path to a TSV or key=value file of additional synonym pairs to merge with the built-in map. Format: one word\treplacement or word = replacement pair per line.

api_key: Option<String>

Optional API key required for /api/ endpoints in web UI mode. When set, requests to /api/* must include Authorization: Bearer <key>.

replay_speed: f64

Replay speed multiplier for –replay mode. 1.0 = real-time, 2.0 = double speed, 0.0 = instant.

timeout: u64

Stream hang timeout in seconds. The stream is forcibly dropped if no token arrives within this duration. Default: 120 (2 minutes). Set to 0 to disable.

export_timeseries: Option<String>

Export per-run timeseries data to a CSV file at this path. Columns: run,token_index,confidence,perplexity

json_schema: bool

Print the embedded research JSON schema and exit.

list_models: Option<String>

List known models for a provider: “openai”, “anthropic”, or “all”.

validate_config: bool

Validate configuration (print resolved values and exit).

Trait Implementations

impl Args for Args

fn group_id() -> Option<Id>

Report the ArgGroup::id for this set of arguments

fn augment_args<'b>(__clap_app: Command) -> Command

Append to Command so it can instantiate Self via FromArgMatches::from_arg_matches_mut

fn augment_args_for_update<'b>(__clap_app: Command) -> Command

Append to Command so it can instantiate self via FromArgMatches::update_from_arg_matches_mut

impl CommandFactory for Args

fn command<'b>() -> Command

Build a Command that can instantiate Self.

fn command_for_update<'b>() -> Command

Build a Command that can update self.

impl FromArgMatches for Args

fn from_arg_matches(__clap_arg_matches: &ArgMatches) -> Result<Self, Error>

Instantiate Self from ArgMatches, parsing the arguments as needed.

fn from_arg_matches_mut( __clap_arg_matches: &mut ArgMatches, ) -> Result<Self, Error>

Instantiate Self from ArgMatches, parsing the arguments as needed.

fn update_from_arg_matches( &mut self, __clap_arg_matches: &ArgMatches, ) -> Result<(), Error>

Assign values from ArgMatches to self.

fn update_from_arg_matches_mut( &mut self, __clap_arg_matches: &mut ArgMatches, ) -> Result<(), Error>

Assign values from ArgMatches to self.

impl Parser for Args

fn parse() -> Self

Parse from std::env::args_os(), exit on error.

fn try_parse() -> Result<Self, Error>

Parse from std::env::args_os(), return Err on error.

fn parse_from<I, T>(itr: I) -> Self

where I: IntoIterator<Item = T>, T: Into<OsString> + Clone,

Parse from iterator, exit on error.

fn try_parse_from<I, T>(itr: I) -> Result<Self, Error>

where I: IntoIterator<Item = T>, T: Into<OsString> + Clone,

Parse from iterator, return Err on error.

fn update_from<I, T>(&mut self, itr: I)

where I: IntoIterator<Item = T>, T: Into<OsString> + Clone,

Update from iterator, exit on error.

fn try_update_from<I, T>(&mut self, itr: I) -> Result<(), Error>

where I: IntoIterator<Item = T>, T: Into<OsString> + Clone,

Update from iterator, return Err on error.