zig-core 0.11.0

use std::collections::HashMap;
use std::path::Path;

use serde::Serialize;
use zag_agent::builder::AgentBuilder;
use zag_agent::manpages;

use crate::error::ZigError;
use crate::prompt;

/// Build the system prompt for the create agent by rendering the template
/// with injected variables.
fn build_system_prompt(zag_help: &str, zag_orch: &str, examples_reference: &str) -> String {
    let vars = HashMap::from([
        ("zwf_format_spec", prompt::templates::config_sidecar()),
        ("zag_help", zag_help),
        ("zag_orch", zag_orch),
        ("examples_reference", examples_reference),
    ]);
    prompt::render(prompt::templates::create(), &vars)
}

/// Return the zag CLI reference text previously fetched via `zag --help-agent`.
///
/// Pulled directly from the embedded `zag-agent` manpages so no subprocess or
/// installed `zag` binary is required.
pub(crate) fn get_zag_help() -> String {
    manpages::HELP_AGENT.to_string()
}

/// Return the zag orchestration reference, previously fetched via
/// `zag man orchestration`. Embedded at compile time via `zag-agent`.
pub(crate) fn get_zag_orch_reference() -> String {
    manpages::ORCHESTRATION.to_string()
}

/// Return a short one-sentence guidance about a specific orchestration pattern
/// to append to the initial user prompt when `--pattern` is passed. The full
/// example files live at `~/.zig/examples/` (see [`prompt::examples_reference_block`])
/// so this helper no longer embeds example TOML inline.
fn pattern_guidance(pattern: &str) -> String {
    match pattern {
        "sequential" => {
            "Use the Sequential Pipeline pattern: steps run in order, each feeding \
            its output to the next via inject_context. Structure it as a linear \
            depends_on chain."
        }
        "fan-out" => {
            "Use the Fan-Out / Gather pattern: multiple independent steps run in \
            parallel and a final step synthesizes their results. The gathering step \
            should depends_on all parallel steps with inject_context = true."
        }
        "generator-critic" => {
            "Use the Generator / Critic pattern: a generation step is followed by a \
            critique step that scores the output, looping back via the next field \
            until a quality threshold is met. Use saves to extract score and \
            feedback variables, and a condition to control the loop."
        }
        "coordinator-dispatcher" => {
            "Use the Coordinator / Dispatcher pattern: an initial classification \
            step routes to specialized handler steps via condition expressions. \
            Use json + saves to extract the classification, then condition guards \
            on each handler step."
        }
        "hierarchical-decomposition" => {
            "Use the Hierarchical Decomposition pattern: a planning step breaks the \
            problem into sub-tasks, multiple worker steps handle each sub-task in \
            parallel, and a synthesis step combines the results."
        }
        "human-in-the-loop" => {
            "Use the Human-in-the-Loop pattern: incorporate human approval gates \
            between automated steps. Use condition expressions on approval \
            variables that are set by human review."
        }
        "inter-agent-communication" => {
            "Use the Inter-Agent Communication pattern: agents collaborate via \
            shared variables. Design the vars section carefully so agents can \
            read and write to common state."
        }
        _ => "",
    }
    .to_string()
}

/// Prepared parameters for workflow creation — used by both the CLI
/// (which spawns zag directly) and the API (which returns data to the frontend).
#[derive(Debug, Clone, Serialize)]
pub struct CreateParams {
    pub system_prompt: String,
    pub initial_prompt: String,
    pub output_path: String,
    pub session_name: String,
    pub session_tag: String,
}

/// Prepare the prompts and configuration for workflow creation without
/// launching zag. Returns structured data that can be used by the CLI
/// to spawn zag or by the API to return to the frontend.
pub fn prepare_create(
    name: Option<&str>,
    output: Option<&str>,
    pattern: Option<&str>,
) -> Result<CreateParams, ZigError> {
    // Write example files to ~/.zig/examples/ so the agent can inspect them.
    if let Err(e) = prompt::write_examples_to_global_dir() {
        eprintln!("Warning: could not write example files: {e}");
    }

    let zag_help = get_zag_help();
    let zag_orch = get_zag_orch_reference();
    let examples_reference = prompt::examples_reference_block();
    let system_prompt = build_system_prompt(&zag_help, &zag_orch, &examples_reference);

    let output_path = if let Some(o) = output {
        o.to_string()
    } else {
        let global_dir = crate::paths::ensure_global_workflows_dir()?;
        let filename = name
            .map(|n| format!("{n}.zwf"))
            .unwrap_or_else(|| "workflow.zwf".to_string());
        crate::paths::collapse_home(&global_dir.join(&filename).to_string_lossy())
    };

    let mut initial_prompt = if let Some(n) = name {
        format!(
            "I want to create a workflow called \"{n}\". \
             The output will be saved to {output_path}. \
             Please help me design it — start by asking what process I want to automate."
        )
    } else {
        format!(
            "I want to create a new workflow. \
             The output will be saved to {output_path}. \
             Please help me design it — start by asking what process I want to automate."
        )
    };

    if let Some(p) = pattern {
        let guidance = pattern_guidance(p);
        if !guidance.is_empty() {
            initial_prompt.push(' ');
            initial_prompt.push_str(&guidance);
        }
    }

    Ok(CreateParams {
        system_prompt,
        initial_prompt,
        output_path,
        session_name: "zig-create".to_string(),
        session_tag: "zig-workflow-creation".to_string(),
    })
}

/// Launch an interactive zag session for workflow creation.
///
/// The agent is given full context about zag and the .zwf format, and guides
/// the user through designing their workflow. Driven through
/// [`AgentBuilder::run`], so no external `zag` binary is required.
pub async fn run_create(
    name: Option<&str>,
    output: Option<&str>,
    pattern: Option<&str>,
) -> Result<(), ZigError> {
    let params = prepare_create(name, output, pattern)?;

    AgentBuilder::new()
        .system_prompt(&params.system_prompt)
        .name(&params.session_name)
        .tag(&params.session_tag)
        .run(Some(&params.initial_prompt))
        .await
        .map_err(|e| ZigError::Zag(format!("failed to run agent: {e}")))?;

    // Validate the output if it was created
    if Path::new(&params.output_path).exists() {
        match crate::workflow::parser::parse_file(Path::new(&params.output_path)) {
            Ok(workflow) => {
                if let Err(errors) = crate::workflow::validate::validate(&workflow) {
                    eprintln!("Warning: generated workflow has validation issues:");
                    for e in &errors {
                        eprintln!("  - {e}");
                    }
                }
            }
            Err(e) => {
                eprintln!("Warning: could not parse generated file: {e}");
            }
        }
    }

    Ok(())
}

#[cfg(test)]
#[path = "create_tests.rs"]
mod tests;