CONTEXT AS A SUPERPOWER: AUTOMATIC STATE INJECTION IN EVERY LLM MESSAGE

# Stamp format:
[F|HH:MM TZ|Day|DX/1096|Project|Task|PX|WS]

# Fields:
# F = User identifier (Frank)
# HH:MM TZ = Local time with timezone
# Day = Abbreviated weekday
# DX/1096 = KayrOs day (how much of the horizon has elapsed)
# Project = Active project name
# Task = Active task or cell ID
# PX = Pomodoro number in current session
# WS = Active workspace (WS1, WS2, WS3)

# Real examples:
[F|10:30 COT|Thu|D4/1096|TeliOs|TELIO-U003|P1|WS1]
[F|14:45 COT|Tue|D18/1096|FocOs|FOCOS-B012|P3|WS2]
[F|09:00 COT|Mon|D1/1096|Novel-Cycles|CHAP-07|P1|WS1]
[F|22:30 COT|Fri|D365/1096|InfoGraTech|POST-14|P2|WS3]

# main.py — FocOsAPI

def get_context(self) -> dict:
    '''
    Builds the full context for the active project.
    Automatically injected into every LLM call.
    '''
    import datetime
    conn = sqlite3.connect(DB_PATH)

    # Active workspace data
    ws = conn.execute(
        'SELECT project_id FROM workspaces WHERE ws_id=1'
    ).fetchone()
    project_id = ws[0] if ws else None

    # Project data
    project = None
    if project_id:
        row = conn.execute(
            'SELECT name, status, meta FROM projects WHERE id=?',
            (project_id,)
        ).fetchone()
        if row:
            project = {
                'name': row[0],
                'status': row[1],
                'meta': json.loads(row[2] or '{}'),
            }

    # Active task
    task = conn.execute(
        'SELECT title FROM tasks WHERE status="active" LIMIT 1'
    ).fetchone()
    conn.close()

    # Build stamp
    now = datetime.datetime.now()
    hora = now.strftime('%H:%M')
    dia = now.strftime('%a')[:3].capitalize()
    project_name = project['name'] if project else 'No project'
    task_name = task[0][:20] if task else 'No task'
    day_num = self._get_kayros_day()

    stamp = f'[F|{hora} COT|{dia}|D{day_num}/1096|{project_name}|{task_name}|P1|WS1]'

    # Full system prompt with context
    meta = project['meta'] if project else {}
    system = f'''You are Chronos — Frank's development assistant.
Active context: {stamp}

PROJECT: {project_name}
STATUS: {project["status"] if project else "unknown"}
ACTIVE TASK: {task_name}
TYPE: {meta.get("tipo", "general")}
STACK: {meta.get("stack", "undefined")}
PHILOSOPHY: {meta.get("filosofia", "")}

Always respond in the user's language.
Prioritize practical solutions over theory.
If you detect an error, flag it before answering.
Maximum 3 options when alternatives exist.
'''

    return {
        'stamp': stamp,
        'system': system,
        'project': project_name,
        'task': task_name,
        'day_num': day_num,
    }

def send_to_llm(self, message: str, history: list = None) -> dict:
    cfg = self.get_llm_config()
    provider = cfg.get('provider', 'gemini')
    model = cfg.get('model', 'gemini-2.0-flash')

    # AUTOMATIC CONTEXT — always present
    ctx = self.get_context()
    system = ctx['system']
    stamp = ctx['stamp']

    # The message reaching the LLM includes the stamp
    # e.g.: '[F|10:30 COT|Thu|D4/1096|TeliOs|TELIO-U003|P1|WS1]\n\nhow do I implement the pub/sub bus?'
    full_message = f'{stamp}\n\n{message}'

    messages = []
    if history:
        for h in (history if isinstance(history, list) else []):
            if isinstance(h, dict):
                messages.append({
                    'role': h.get('role', 'user'),
                    'content': h.get('content', '')
                })
    messages.append({'role': 'user', 'content': full_message})

    # Dispatch to provider with contextualized system prompt
    return self._dispatch_llm(provider, model, system, messages, cfg)

# WRITER — working on a novel
[F|09:15 COT|Tue|D22/1096|Novel-Cycles|CHAP-07|P1|WS1]
# Project: Novel — Cycles | Chapter: 7 | Character: Elena
# Tone: melancholic, introspective | POV shifts to first person

# MUSIC PRODUCER — working on an album
[F|23:00 COT|Fri|D45/1096|Album-Roots|TRACK-03|P2|WS1]
# Project: Album — Roots | Track: 03 | BPM: 92 | Key: Am
# Section: bridge | DAW: Ableton | Reference: Nils Frahm

# RESEARCHER
[F|11:30 COT|Wed|D8/1096|Thesis-Ecosystems|CHAP-04|P3|WS2]
# Project: Thesis | Chapter: 4 — Methodology
# Active hypothesis: H3 | Pending source: IEEE 2024

// bridge.js — Display stamp in the LLM panel

async function llmInit() {
  try {
    const api = await FocOs.ready()
    const ctx = await api.get_context()

    // Show stamp in LLM panel header
    const stampEl = document.getElementById('llm-stamp')
    if (stampEl && ctx.stamp) {
      stampEl.textContent = ctx.stamp
      stampEl.title = 'Active context injected in every message'
    }
    const projEl = document.getElementById('llm-project')
    if (projEl) projEl.textContent = ctx.project
  } catch(e) {
    console.log('llmInit:', e.message)
  }
}

async function llmSend(message) {
  const history = llmGetHistory()
  // send_to_llm() injects context automatically — user does nothing
  const result = await FocOs.sendToLLM(message, history)
  if (result.ok) {
    llmAppendMessage('assistant', result.response)
    llmSaveHistory('user', message)
    llmSaveHistory('assistant', result.response)
  } else {
    llmShowError(result.error)
  }
}