//! Best-effort brain augmentation for the chat path.
//!
//! Each turn we open the claw's local working `.brain` (cm-brain / ClawhDF5),
//! recall relevant memory, record the turn, and compose a system prompt on top
//! of the claw's Postgres-authoritative one. Any failure falls back to the plain
//! prompt — the brain must never break chat.
//!
//! Skills are **indexed, not inlined**: the prompt lists what the claw has and
//! what each is for, and `skills.read` fetches a body on demand.
//!
//! The local file is a working cache of the claw's brain (canonical home is
//! ClawBrainHub); memory accrues here and is pushed back on save/publish.

use std::path::PathBuf;

use cm_brain::ClawBrain;

/// Where the working `.brain` files live. Shared with `cm_api::mission_memory`,
/// which keeps the per-repository brains beside the per-claw ones.
pub fn brain_dir() -> PathBuf {
    std::env::var("CLAWMATES_BRAIN_DIR")
        .map(PathBuf::from)
        .unwrap_or_else(|_| std::env::temp_dir().join("clawmates-brains"))
}

/// Compose the system prompt for a claw, augmenting `base_prompt` with its
/// identity (AGENTS.md + personality), skills, and recalled memory, and recording
/// the user turn. Best-effort: returns `base_prompt` unchanged on any brain error.
pub fn compose_system(
    agent_id: &str,
    base_prompt: &str,
    skills: &[(String, String, String)], // (title, description, body)
    user_text: &str,
    session_label: &str,
) -> String {
    match try_compose(agent_id, base_prompt, skills, user_text, session_label) {
        Ok(s) => s,
        Err(e) => {
            eprintln!("cm-runtime: brain augmentation skipped for {agent_id}: {e}");
            base_prompt.to_string()
        }
    }
}

/// Record the assistant's reply in the claw's brain so recall returns whole
/// exchanges rather than just the user's half.
///
/// Best-effort and silent on failure, like [`compose_system`] — memory is an
/// enhancement and must never fail a completed turn. Empty replies (a turn that
/// only made tool calls) are skipped so they don't dilute the keyword index.
pub fn remember_reply(agent_id: &str, text: &str, session_label: &str) {
    if text.trim().is_empty() {
        return;
    }
    let path = brain_dir().join(format!("claw_{agent_id}.h5"));
    match ClawBrain::open_or_create(&path, agent_id) {
        Ok(mut brain) => {
            let _ = brain.remember("assistant", text, session_label);
        }
        Err(e) => eprintln!("cm-runtime: brain reply-memory skipped for {agent_id}: {e}"),
    }
}

fn try_compose(
    agent_id: &str,
    base_prompt: &str,
    skills: &[(String, String, String)],
    user_text: &str,
    session_label: &str,
) -> Result<String, cm_brain::BrainError> {
    let path = brain_dir().join(format!("claw_{agent_id}.h5"));
    let mut brain = ClawBrain::open_or_create(&path, agent_id)?;

    // First touch: seed the brain's definition (Postgres stays authoritative;
    // the live prompt below always uses the DB values, so this is just so the
    // .brain is a complete, portable artifact for push-back).
    if brain.system_prompt().is_none() {
        if !base_prompt.is_empty() {
            brain.set_system_prompt(base_prompt)?;
        }
        for (name, _description, body) in skills {
            brain.set_skill(name, body)?;
        }
    }

    let recalled = brain.recall(user_text, 4);
    // Record the user's turn so future sessions can recall it (best-effort).
    let _ = brain.remember("user", user_text, session_label);

    let mut out = String::with_capacity(base_prompt.len() + 512);
    // Base identity: the Postgres-authoritative system prompt, or — if it's empty
    // (e.g. a brain pulled from the Hub that carries its identity in soul_md) —
    // the brain's own system prompt (which falls back to soul_md).
    if base_prompt.trim().is_empty() {
        if let Some(sp) = brain.system_prompt() {
            out.push_str(&sp);
        }
    } else {
        out.push_str(base_prompt);
    }
    // `agent_md` ("how I operate") and `personality` are deliberately NOT
    // injected. Both are standing behavioural instruction — house style, coding
    // preferences, tone — and their bodies are the team template's `brain_seed`
    // prose ("prefer let-else over deep nesting", "anti-patterns: unwrap() in
    // library code"). That is exactly the kind of correction written for weaker
    // models: a current frontier model either does it unprompted or does it
    // fine differently, and the text cost a fixed toll on every single turn.
    //
    // They remain in the brain, editable from the dashboard and carried in the
    // portable artifact — this is about what earns a place in the prompt, not
    // about discarding the data. The claw's DB `system_prompt` still goes in
    // above: identity and purpose are information, not correction.
    // Skills are indexed, not inlined. Bodies average ~3.5 KB (~900 tokens)
    // each and were previously concatenated in full on every turn, unbounded in
    // the number installed — by far the largest thing in the prompt. The claw
    // now sees what it has and what each is for, and calls `skills.read` for a
    // body when one is actually relevant. Same summary-and-fetch contract the
    // mission path already gets from the `clawmates_skills` MCP server.
    if !skills.is_empty() {
        out.push_str("\n\n## Your skills\n");
        out.push_str("Call `skills.read` with a skill's name to read it in full.\n");
        for (name, description, _body) in skills {
            if description.trim().is_empty() {
                out.push_str(&format!("- {name}\n"));
            } else {
                out.push_str(&format!("- {name} — {description}\n"));
            }
        }
    }
    if !recalled.is_empty() {
        out.push_str("\n\n## Relevant memory from past sessions\n");
        for r in &recalled {
            out.push_str("- ");
            out.push_str(r);
            out.push('\n');
        }
    }
    Ok(out)
}