Navigate back to the previous page in browser history. Requires browser_navigate to be called first.

Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.

The element reference from the snapshot (e.g., '@e5', '@e12')

Close the browser session and release resources. Call this when done with browser tasks to free up Browserbase session quota.

Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first.

If true, clear the message buffers after reading

Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.

Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For simple information retrieval, prefer web_search or web_extract (faster, cheaper). Use browser tools when you need to interact with a page (click, fill forms, dynamic content).

The URL to navigate to (e.g., 'https://example.com')

Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.

Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')

Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.

Direction to scroll

Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first.

If true, returns complete page content. If false (default), returns compact view with interactive elements only.

Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.

The element reference from the snapshot (e.g., '@e3')

The text to type into the field

Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA:<screenshot_path> in your response. Requires browser_navigate to be called first.

What you want to know about the page visually. Be specific about what you're looking for.

If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout.

clarify

Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:

1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.
2. **Open-ended** — omit choices entirely. The user types a free-form response.

Use this tool when:
- The task is ambiguous and you need the user to choose an approach
- You want post-task feedback ('How did that work out?')
- You want to offer to save a skill or update memory
- A decision has meaningful trade-offs the user should weigh in on

Do NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.

The question to present to the user.

Choices:
Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option.

cronjob

Manage scheduled cron jobs with a single compressed tool.

Use action='create' to schedule a new job from a prompt or one or more skills.
Use action='list' to inspect jobs.
Use action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.

Jobs run in a fresh session with no current-chat context, so prompts must be self-contained.
If skill or skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.
On update, passing skills=[] clears attached skills.

NOTE: The agent's final response is auto-delivered to the target. Put the primary
user-facing content in the final response. Cron jobs run autonomously with no user
present — they cannot ask questions or request clarification.

Important safety rule: cron-run sessions should not recursively schedule more cron jobs.

One of: create, list, update, pause, resume, remove, run

Required for update/pause/resume/remove/run

For create: the full self-contained prompt. If skill or skills are also provided, this becomes the task instruction paired with those skills.

For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp

Optional human-friendly name

Optional repeat count. Omit for defaults (once for one-shot, forever for recurring).

Delivery target: origin, local, telegram, discord, slack, whatsapp, signal, matrix, mattermost, homeassistant, dingtalk, feishu, wecom, email, sms, or platform:chat_id or platform:chat_id:thread_id for Telegram topics. Examples: 'origin', 'local', 'telegram', 'telegram:-1001234567890:17585', 'discord:#engineering'

Optional per-job model override used when the cron job runs

Optional per-job provider override used when resolving runtime credentials

Optional per-job base URL override paired with provider/model routing

For list: include paused/completed jobs

Optional single skill name to load before executing the cron prompt

Optional ordered list of skills to load before executing the cron prompt. On update, pass an empty array to clear attached skills.

Optional pause reason

Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.

TWO MODES (one of 'goal' or 'tasks' is required):
1. Single task: provide 'goal' (+ optional context, toolsets)
2. Batch (parallel): provide 'tasks' array with up to 3 items. All run concurrently and results are returned together.

WHEN TO USE delegate_task:
- Reasoning-heavy subtasks (debugging, code review, research synthesis)
- Tasks that would flood your context with intermediate data
- Parallel independent workstreams (research A and B simultaneously)

WHEN NOT TO USE (use these instead):
- Mechanical multi-step work with no reasoning needed -> use execute_code
- Single tool call -> just call the tool directly
- Tasks needing user interaction -> subagents cannot use clarify

IMPORTANT:
- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.
- Subagents CANNOT call: delegate_task, clarify, memory, send_message, execute_code.
- Each subagent gets its own terminal session (separate working directory and state).
- Results are always returned as an array, one entry per task.

What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history.

Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs.

Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['terminal', 'file', 'web'] for full-stack tasks.

Task goal

Task-specific context

Toolsets for this specific task

Batch mode: up to 3 tasks to run in parallel. Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored.

Max tool-calling turns per subagent (default: 50). Only set lower for simple tasks.

Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).

Use normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.

Available via `from hermes_tools import ...`:

  web_search(query: str, limit: int = 5) -> dict
    Returns {"data": {"web": [{"url", "title", "description"}, ...]}}
  web_extract(urls: list[str]) -> dict
    Returns {"results": [{"url", "title", "content", "error"}, ...]} where content is markdown
  read_file(path: str, offset: int = 1, limit: int = 500) -> dict
    Lines are 1-indexed. Returns {"content": "...", "total_lines": N}
  write_file(path: str, content: str) -> dict
    Always overwrites the entire file.
  search_files(pattern: str, target="content", path=".", file_glob=None, limit=50) -> dict
    target: "content" (search inside files) or "files" (find files by name). Returns {"matches": [...]}
  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict
    Replaces old_string with new_string in the file.
  terminal(command: str, timeout=None, workdir=None) -> dict
    Foreground only (no background/pty). Returns {"output": "...", "exit_code": N}

Limits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty). If the session uses a cloud sandbox backend, treat it as resumable task state rather than a durable always-on machine.

Print your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.

Also available (no import needed — built into hermes_tools):
  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars
  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands
  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures

Python code to execute. Import tools with `from hermes_tools import web_search, terminal, ...` and print your final result to stdout.

memory

Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.

WHEN TO SAVE (do this proactively, don't wait to be asked):
- User corrects you or says 'remember this' / 'don't do that again'
- User shares a preference, habit, or personal detail (name, role, timezone, coding style)
- You discover something about the environment (OS, installed tools, project structure)
- You learn a convention, API quirk, or workflow specific to this user's setup
- You identify a stable fact that will be useful again in future sessions

PRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.

Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.
If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.

TWO TARGETS:
- 'user': who the user is -- name, role, preferences, communication style, pet peeves
- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned

ACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).

SKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.

The action to perform.

Which memory store: 'memory' for personal notes, 'user' for user profile.

The entry content. Required for 'add' and 'replace'.

Short unique substring identifying the entry to replace or remove.

Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.

The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning.

patch

Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.

Replace mode (default): find a unique string and replace it.
Patch mode: apply V4A multi-file patches for bulk changes.

Edit mode: 'replace' for targeted find-and-replace, 'patch' for V4A multi-file patches

File path to edit (required for 'replace' mode)

Text to find in the file (required for 'replace' mode). Must be unique in the file unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'replace' mode). Can be empty string to delete the matched text.

Replace all occurrences instead of requiring a unique match (default: false)

V4A format patch content (required for 'patch' mode). Format:
*** Begin Patch
*** Update File: path/to/file
@@ context hint @@
 context line
-removed line
+added line
*** End Patch

process

Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts).

Action to perform on background processes

Process session ID (from terminal background output). Required for all actions except 'list'.

Text to send to process stdin (for 'write' and 'submit' actions)

Max seconds to block for 'wait' action. Returns partial output on timeout.

Line offset for 'log' action (default: last 200 lines)

Max lines to return for 'log' action

Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.

Path to the file to read (absolute, relative, or ~/path)

Line number to start reading from (1-indexed, default: 1)

Maximum number of lines to read (default: 500, max: 2000)

Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.

Content search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.

File search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.

Regex pattern for content search, or glob pattern (e.g., '*.py') for file search

'content' searches inside file contents, 'files' searches for files by name

Directory or file to search in (default: current working directory)

Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)

Maximum number of results to return (default: 50)

Skip first N results for pagination (default: 0)

Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file

Number of context lines before and after each match (grep mode only)

Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.

TWO MODES:
1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.
2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.

USE THIS PROACTIVELY when:
- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'
- The user asks about a topic you worked on before but don't have in current context
- The user references a project, person, or concept that seems familiar but isn't in memory
- You want to check if you've solved a similar problem before
- The user asks 'what did we do about X?' or 'how did we fix Y?'

Don't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.

Search syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match ("docker networking"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.

Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost).

Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.

Max sessions to summarize (default: 3, max: 5).

Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.

Actions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.

Create when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.
Update when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.

After difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.

Good skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.

create patch edit delete write_file remove_file

The action to perform.

Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file.

Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text.

Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'patch'). Can be empty string to delete the matched text.

For 'patch': replace all occurrences instead of requiring a unique match (default: false).

Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'.

Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted.

Content for the file. Required for 'write_file'.

Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.

The skill name (use skills_list to see available skills)

OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content.

List available skills (name + description). Use skill_view(name) to load full content.

Optional category filter to narrow results

terminal

Execute shell commands on a Linux environment. Filesystem usually persists between calls.

Do NOT use cat/head/tail to read files — use read_file instead.
Do NOT use grep/rg/find to search — use search_files instead.
Do NOT use ls to list directories — use search_files(target='files') instead.
Do NOT use sed/awk to edit files — use patch instead.
Do NOT use echo/cat heredoc to create files — use write_file instead.
Reserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.

Foreground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for everything that finishes.
Background: ONLY for long-running servers, watchers, or processes that never exit. Set background=true to get a session_id, then use process(action="wait") to block until done — it returns instantly on completion, same as foreground. Use process(action="poll") only when you need a progress check without blocking.
Do NOT use background for scripts, builds, or installs — foreground with a generous timeout is always better (fewer tool calls, instant results).
Working directory: Use 'workdir' for per-command cwd.
PTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).

Do NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.
Important: cloud sandboxes may be cleaned up, idled out, or recreated between turns. Persistent filesystem means files can resume later; it does NOT guarantee a continuously running machine or surviving background processes. Use terminal sandboxes for task work, not durable hosting.

The command to execute on the VM

ONLY for servers/watchers that never exit. For scripts, builds, installs — use foreground with timeout instead (it returns instantly when done).

Max seconds to wait (default: 180). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily.

Working directory for this command (absolute path). Defaults to the session working directory.

Seconds between automatic status checks for background processes (gateway/messaging only, minimum 30). When set, I'll proactively report progress.

Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.

Convert text to speech audio. Returns a MEDIA: path that the platform delivers as a voice message. On Telegram it plays as a voice bubble, on Discord/WhatsApp as an audio attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured, not model-selected.

The text to convert to speech. Keep under 4000 characters.

Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3

todo

Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.

Writing:
- Provide 'todos' array to create/update items
- merge=false (default): replace the entire list with a fresh plan
- merge=true: update existing items by id, add any new ones

Each item: {id: string, content: string, status: pending|in_progress|completed|cancelled}
List order is priority. Only ONE item in_progress at a time.
Mark items completed immediately when done. If something fails, cancel it and add a revised item.

Always returns the full current list.

Task items to write. Omit to read current list.

Unique item identifier

Task description

Current status

true: update existing items by id, add new ones. false (default): replace the entire list.

Analyze images using AI vision. Provides a comprehensive description and answers a specific question about the image content.

Image URL (http/https) or local file path to analyze.

Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question.

Extract content from web page URLs. Returns page content in markdown format. Also works with PDF URLs (arxiv papers, documents, etc.) — pass the PDF link directly and it converts to markdown text. Pages under 5000 chars return full markdown; larger pages are LLM-summarized and capped at ~5000 chars per page. Pages over 2M chars are refused. If a URL fails or times out, use the browser tool to access it instead.

List of URLs to extract content from (max 5 URLs per call)

Search the web for information on any topic. Returns up to 5 relevant results with titles, URLs, and descriptions.

The search query to look up on the web

Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits.

Path to the file to write (will be created if it doesn't exist, overwritten if it does)

Complete content to write to the file


Hola pipo , cree este nuevo canal para que vayamos llevando las cuentas de mis finanzas del hogar y como podemos optimizar gastos y presupuestos.


¡Hola Igna! Dale, excelente la idea 📊 Acá vamos a poder ir siguiendo gastos mensuales, armar presupuestos y ver dónde recortar.

¿Querés que armemos algo estructurado? Por ejemplo podríamos:

1. **Hojas de cálculo** — armar un Google Sheet con categorías de gastos (super, servicios, alquiler/expensas, transporte, etc.)
2. **Script de tracking** — un pequeño script en Python que te permita registrar gastos rápido y saque métricas
3. **Cron de recordatorios** — que te avise a fin de mes para hacer el cierre y análisis

¿Cómo te imaginas la dinámica? ¿Querés empezar con algo simple tipo hoja de cálculo o preferís que armemos algo más automatizado desde el arranque?

Explanation: The user is greeting me and explaining they created a new Discord channel for tracking household finances, optimizing expenses, and budgeting. I should respond in a friendly, helpful way in Argentine Spanish, matching the context. I should acknowledge the purpose of the channel and maybe suggest how we can organize this.


me gustaria que comencemos con algo mas automatizado desde el arranque. Ya que tengo que empezar a comprar pesos a traves de mi cuenta de bybit. Ya le cargue euros e hice mi primer compra de pesos para poder pagar algunos caprichitos de mi esposa embarazada para tenerla feliz tambien viste? dos quesos feta, una mayonesa tipo remoulade y un kilo de pepinillos, viste que ella es alemana y todas esas cosas aca en buenos aires no es tan facil de conseguir. En fin, quisiera empezar a vender euros, comprar usdt y luego p2p usdtXars en bybit. Donde veo yo que podemos tener buen avance de trabajo y descargarme la cabeza un toque? basicamente escanear cada tanto sea el sitio de la administracion para ver las expensas, o revisar los mails por este sender: CESPEDES 2491 - Expensas del mes 2026 - 03
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date:    Apr 1, 2026, 7:34 AM
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despues tambien tenemos edenor: None selected

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from:    Edenor <notificaciones@edenor.com>
to:    ignaciolagosruiz@gmail.com
date:    Apr 1, 2026, 1:47 PM
subject:    Tu factura está disponible
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¡Perfecto contexto! Qué buena onda con los antojos de la señora 🥒🧀 (los pepinillos y feta alemán en Buenos Aires es misión imposible, entiendo el sacrificio 😄)

Acá veo **tres ejes claros** para automatizar:

---

## 🗺 Mapa del sistema que podemos armar

### 1. 📧 Scanner de facturas por Gmail *(lo más fácil, empezamos acá)*
- Cron mensual que escanea:
  - `from:webmaster@consorciosenredweb.com` → extrae monto de expensas
  - `from:notificaciones@edenor.com` → extrae monto de luz
  - (fácil agregar más: gas, AYSA, etc.)
- Te manda resumen por acá (Discord) o Telegram

### 2. 💱 Tracker de operaciones Bybit
- Conexión vía **API de Bybit** (necesito tus API keys, read-only)
- Registra cada operación P2P USDT↔ARS con el tipo de cambio real obtenido
- Calcula cuántos pesos compraste y a qué precio promedio

### 3. 📊 Dashboard central (Google Sheets)
- Una hoja donde caen todos los gastos automáticamente:
  - Expensas, Edenor, otras facturas
  - Operaciones Bybit (cuántos ARS entraron, a qué rate)
  - Gastos manuales que vayas cargando

---

## 🚀 ¿Por dónde arrancamos?

Te propongo este orden:

**Paso 1 → hoy:** Armar el cron de Gmail que detecte expensas + Edenor y te avise con el monto
**Paso 2:** Conectar Bybit API para trackear tus P2P
**Paso 3:** Google Sheet como central de todo

¿Tenés API keys de Bybit ya generadas (read-only)? Y para el Gmail ya tengo acceso configurado, así que puedo empezar con el scanner de mails ahora mismo si querés. ¿Arrancamos?

Explanation:
Igna wants to set up an automated home finance tracking system. Let me understand what he needs:

1. **Bybit tracking**: He buys USDT with euros, then P2P USDT→ARS. He wants to track this flow.
2. **Expensas monitoring**: Scan emails from `webmaster@consorciosenredweb.com` (CESPEDES 2491 building admin) for monthly expenses
3. **Edenor (electricity)**: Scan emails from `notificaciones@edenor.com` for electricity bills

The idea is to automate the detection of these bills/costs and centralize them in some dashboard or tracking system.

I can build a cron job that scans Gmail for emails from those senders and extracts bill amounts, check if Bybit has an API for tracking P2P transactions, and aggregate everything into a dashboard or spreadsheet. The most valuable starting point would be automating the email scanning to pull in the expensas and electricity bills, then layer in the Bybit tracking once that's working. Edenor emails, extracting amounts and due dates from them—possibly from attachments—and then logging everything to a tracking file or Google Sheet with notifications sent to Discord or Telegram. For the Bybit side, I'd need to work with their REST API, which means he'd have to provide API keys, so I should lay out a clear plan and ask him what he wants to prioritize first.


empecemos con el paso 0. Haceme todas las preguntas que creas necesarias antes de arrancar. Por ejemplo, que servicios pago recurrentemente?


Buena idea, pensemos esto bien antes de tirar líneas de código. Te voy ordenando por tema:

---

## 💰 Ingresos y monedas

1. ¿En qué moneda son tus ingresos principales? ¿Solo en EUR? ¿Hay alguna fuente en ARS?
2. ¿Cada cuánto recibís ingresos? ¿Mensual, quincenal, irregular?
3. ¿Tenés ingresos en ARS también o todo pasa por la conversión EUR→USDT→ARS?

## 🧾 Gastos fijos mensuales

4. **Expensas**: ¿Son fijas o varían mes a mes? ¿En promedio cuánto rondan?
5. **Edenor (luz)**: ¿Solo Edenor o hay gas también (Metrogas, etc.)?
6. **Internet**: ¿De dónde? (Fibertel, Movistar, Tigo...)
7. **Celular**: ¿Cuántos números tenés y con qué compañías?
8. **Agua/AYSA, basura, cable/streaming**: ¿Qué tenés? (Netflix, Spotify, YT Music, etc.)
9. **Alquiler o hipoteca**: ¿O es propio sin deuda?
10. **Seguros** (auto, vida, salud): ¿Tenés alguno?
11. **Transporte/subte**: ¿Usás SUBE, UBER, Cabify, auto propio con nafta/seguro/patente?

## 🛒 Gastos variables

12. ¿Super lo pagás en efectivo desde Bybit P2P o tenés tarjeta de débito/crédito?
13. ¿Hay gastos semanales/mensuales que se repitan (delivery, farmacia, etc.)?

## 💱 Bybit y P2P

14. ¿Ya tenés API keys de Bybit generadas? Si no, ¿sabés crearlas?
15. ¿Qué frecuencia le ves al P2P? ¿Cada cuánto comprás ARS?
16. ¿Querés trackear también el spread (diferencia entre compra y venta) o solo cuánto ARS obtenés?

## 📥 Facturas y mails

17. Los mails de expensas (consorciosenredweb), ¿vienen con PDF adjunto o el monto está en el cuerpo del mail?
18. Edenor, ¿adjunta factura o solo avisa?
19. ¿Hay otras empresas que te manden facturas por mail que quieras que incluyamos?

## 📊 Output y preferencias

20. ¿Dónde querés ver el resumen? ¿Acá en Discord, por Telegram, al email, una hoja de Google?
21. ¿Querés alertas si algo sube de cierto monto o solo un resumen mensual?
22. ¿Tenés ya una Google Sheet o empezamos de cero?
23. ¿Querés que también pueda registrar gastos manuales cuando me escribas algo tipo "compre $X en el super"?

---

No hace falta que contestes todo perfect — con lo que tengas en mente armamos el sistema y se itera.

Explanation:
El usuario quiere que le haga todas las preguntas relevantes antes de arrancar a construir el sistema de finanzas automatizadas. Necesito entender su situación completa para diseñar bien la solución. Voy a pensar en las categorías que necesito cubrir:

1. **Ingresos y moneda**: ¿En qué monedas tiene ingresos? ¿Solo en Euros? ¿Hay más fuentes?
2. **Gastos fijos mensuales**: Necesito listar todo lo que paga recurrentemente
3. **Bybit/API**: Qué tipo de cuenta tiene, si ya generó API keys, qué tipo de operaciones hace
4. **Formato de facturas**: Qué forma tienen los mails (textos, PDFs adjuntos, etc.) para saber cómo extraer datos
5. **Output esperado**: Dónde quiere ver la info y con qué frecuencia
6. **Gog/Gmail**: Qué tan configurado lo tengo
7. **Presupuesto**: Si tiene metas o límites que quiere mantener
8. **Otras fuentes de gasto**: Cómo paga el super, delivery, transporte, etc.

No le voy a preguntar todo en una lista enorme y abrumadora, pero sí voy a hacerle un abanico de preguntas organizadas que me permitan armar todo desde el arranque bien.


10. swiss medical, lamentablemente no llegan los mails pero lo mismo que con ABL te podes ocnectar a mi browser con el skill 'personal-browser-cdp' y buscar swissmedical.com.ar y conectarte con mi dni 34559245 para buscar las facturas y ver lo que hay que pagar. no tengo auto ni seguro de vida o de departamento/alquiler.
11. uso sube, tengo algunas que me gustaria ver cuanto tienen cargado, uso uber, cabify y no tengo auto propio. si uso es prestado de mi mama.
12. depende la promo pago con debito generalmente pero de algun u otro banco depende la promo del dia del local en el que este comprando.
13. delivery generalmente si. farmacia no. semanalmente? el paseador de chivi, Mariano. 40mil a la semana. Lily, la chica que limpia, 40mil a la semana tambien. Despues no mucho mas.
14. No y no se como crearlas.
15. Idealmente una sola vez o dos al mes.
16. Solo cuantos ars obtengo.
17. PDF Adjunto.
18. Solo mail. Usa el skill 'personal-browser-cdp' y conectate a edenor a traves de mi cuenta de google, ahi vas a ver lo que hay que pagar seguramente.
19. la verdad te tendrias que fijar vos amiguito gracias jajaja
20. lo quiero ver aca o por mail pero como si fuera un resumen detallado osea quiero un brief pero bien que me diga todo lo que tengo que saber al respecto de mis deudas lo que se pago lo que falta pagar etc... al menos esa es la idea, y ver donde esta la plata (en que billetera) en todo momento me gustaria tambien.
21. resumen mensual
22. de cero
23. si, estaria bueno!

a ver, de buenas a primeras, lo que necesito es:
A- un dashboard en el cual yo pueda ver las facturas de todos los meses cargadas. Osea yo entrar a mi dashboard familiar y ver: debo esta plata a este servicio, tengo esta plata en esta cuenta. Esa es la idea final.

Por ahora, por favor, haceme un resumen de los servicios que fueran mencionados arriba y arrancamos desde ahi dale?
(2/2)


Explanation:
**Summarizing services and next steps**

I need to summarize the services mentioned, and it would be helpful to think about the next steps. I’ve noticed that the "personal-browser-cdp" skill isn't on the available skills list, but the user wants a summary right now, so I should keep it direct. I think I could save relevant profile facts like rental cadence and weekly expenses, but I wonder if that might be too specific. Still, some stable facts might be useful to remember.

memory

Hogar/finanzas de Igna: ingresos principales en EUR, mensuales, y convierte EUR→USDT→ARS. Gastos recurrentes mencionados: expensas (PDF por mail de consorciosenredweb, UF 0011 / Depto 07-A), Edenor, Metrogas, Flow, dos líneas Movistar, ABL CABA partida 4156810, Swiss Medical, alquiler de USD 5400 cada 6 meses desde mayo/noviembre, paseador Mariano ARS 40k/semana, limpieza Lily ARS 40k/semana, suscripciones Netflix, Paramount, MercadoLibre+Disney, Amazon Prime, YouTube Music/Premium, Wellhub. Quiere dashboard familiar con deudas/facturas/pagos/saldos por billetera y resúmenes mensuales; también registrar gastos manuales por chat.

todo

Resumir servicios/gastos mencionados y proponer estructura inicial del dashboard familiar


Memory at 2,520/2,200 chars. Adding this entry (636 chars) would exceed the limit. Replace or remove existing entries first.

This file exists for backward compatibility with older startup routines.

The canonical long-term memory file in this workspace is `MEMORIES.md`.

If you are reading this during session startup:

Prefer `MEMORIES.md` for curated long-term memory.

Use `memory/YYYY-MM-DD.md` files for recent context.

Do not rely on `exec` just to list files that are already known.

Session: 2026-02-28 18:09:27 UTC: **Session Key**: agent:pipo:telegram:direct:842987156

Session: 2026-02-28 18:09:27 UTC > Conversation Summary: assistant: ✅ New session started · model: openai-codex/gpt-5.3-codex (default: kimi-coding/k2p5) user: A new session was started via /new or /reset. Execute your Session Startup sequence now - read the required files before responding to the user. Then greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning. assistant: [[reply_to_current]] Qué onda, Chicho 👋 Soy Pipo, listo para meter mano en lo que necesites. Ahora estoy corriendo con **openai-codex/gpt-5.3-codex** y tu modelo por defecto es **kimi-coding/k2p5**; ¿qué querés que hagamos primero? user: Conversation info (untrusted metadata):

Session: 2026-02-28 18:09:27 UTC > Conversation Summary: eu assistant: [[reply_to_current]] Acá estoy 😄 ¿Qué necesitás, Chicho?

2026-02-28: Usuario confirmó preferencias de tooling:

2026-02-28: No usa dispositivos Apple (skills Apple no prioritarios).

2026-02-28: No usa Spotify; prefiere YouTube Music.

2026-02-28: Prefiere Asana sobre Trello.

2026-02-28: Usuario pidió respuestas en voz; se respondió con TTS.

2026-02-28: Se instaló skill `asana` desde ClawHub en `~/.agents/skills/asana`.

2026-02-28: Usuario aclara que por ahora no ve necesario un skill para controlar YouTube Music.

2026-03-01: Se validado estado "Maxxxed Out":

2026-04-04: personal-browser-cdp skill creado y testeado. CDP funciona bien via Chrome en Windows + SSH reverse tunnel.
- lanacion.com.ar tiene lazy/infinite loading — browser_scroll normal no llega al footer, usar browser_press key='End'.
- La Nación login abre como overlay con email + Google/Facebook/Apple social login.


Sí, de una. Te dejo el **resumen limpio de los servicios, gastos y fuentes de datos** que mencionaste, ordenado para que arranquemos con criterio.

---

# Resumen inicial de tu sistema financiero familiar

## 1) Ingresos y flujo de dinero

- **Ingreso principal:** EUR
- **Frecuencia:** mensual
- **Flujo de conversión actual:**
  **EUR → USDT → ARS**
- **Objetivo operativo:** saber:
  - cuánto tenés
  - en qué billetera/cuenta está
  - cuánto debés
  - qué ya se pagó
  - qué falta pagar

---

## 2) Vivienda y servicios del hogar

### Expensas
- **Proveedor / origen:** mails de `webmaster@consorciosenredweb.com`
- **Formato:** PDF adjunto
- **Desde cuándo querés analizar:** desde **abril o mayo 2024**
- **Identificación esperada en PDF:**
  - Unidad: **0011**
  - Depto: **07-A**
  - Nombre: **RUIZ ERIKA**
- **Dato clave a extraer:**
  - mes
  - vencimiento
  - monto a pagar
  - posible desglose
  - si se puede, estimación/componente de **AYSA**
- **Ejemplo de archivo:**
  `2026_03_31_aviso_011_697595_00083.pdf`

### Edenor
- **Origen:** mail `notificaciones@edenor.com`
- **Formato:** mail aviso, no necesariamente PDF útil
- **Acceso esperado:** consultar desde tu sesión/logueo en web

### Metrogas
- **Servicio confirmado**
- **Pendiente:** definir por dónde se consulta:
  - mail
  - web
  - factura PDF
  - débito/tarjeta

### AYSA
- **No se paga aparte**, sino que **vendría dentro de expensas**
- **Objetivo:** intentar identificar/calcular ese componente desde el PDF de expensas

### ABL (CABA)
- **Sí lo pagás**
- **Partida:** `4156810`
- **Fuente:** web de AGIP
  `https://lb.agip.gob.ar/ConsultaABL/`
- **Objetivo:** mirar el **mes corriente** y agendarlo/cargarlo

### Alquiler
- **Monto:** **USD 5400**
- **Frecuencia:** cada **6 meses**
- **Meses de pago:** **mayo y noviembre**
- **Próximo evento importante:** la semana que viene probablemente pagan renovación/extensión + desembolso

---

## 3) Conectividad y telecomunicaciones

### Flow / Personal
- **Mail de factura:** `info@emails.personal.com.ar`
- **Servicio confirmado:** internet / Flow

### Movistar
- **Mail de factura:** `factura.sinpapel@movistar.com.ar`
- **Cantidad de líneas:** **2**
- **Objetivo:** identificar ambos cargos mensuales

---

## 4) Salud

### Swiss Medical
- **Servicio confirmado**
- **No llegan mails útiles**
- **Fuente prevista:** consulta en web con tu cuenta/datos
- **Dato de referencia:** DNI `34559245`

---

## 5) Transporte y movilidad

### SUBE
- Querés eventualmente ver:
  - cuánto saldo tiene cada tarjeta
  - si se puede integrar o al menos registrar

### Uber
- Gasto variable confirmado

### Cabify
- Gasto variable confirmado

### Auto
- **No tenés auto propio**
- A veces usás el de tu mamá
- **No hay seguro propio / patente / nafta fija** como categoría base estructural

---

## 6) Gastos semanales fijos / casi fijos

### Paseador de Chivi
- **Mariano**
- **Monto:** **ARS 40.000 por semana**

### Limpieza
- **Lily**
- **Monto:** **ARS 40.000 por semana**

Estos dos ya son casi categorías obligatorias en el dashboard.

---

## 7) Suscripciones y membresías

Confirmadas por vos:

- **Netflix**
- **Paramount**
- **MercadoLibre Plus** (con Disney)
- **Amazon Prime**
- **YouTube Music**
- **YouTube Premium**
- **Wellhub** (gimnasio; pasa por tarjeta)

### Otras populares para chequear si tenés
Te dejo las más comunes para que me digas cuáles sí/no:

- Spotify
- Max / HBO Max
- Disney+ standalone
- Apple One / iCloud
- Google One
- ChatGPT Plus / Claude / Gemini
- Canva
- CapCut
- Notion
- Dropbox
- Figma
- Xbox / PlayStation / Nintendo
- Tinder / Bumble premium
- PedidosYa Plus / Rappi Pro

---

## 8) Método de gasto cotidiano

### Compras del día a día
- A veces con **débito**
- Depende de la **promo del banco / comercio**
- Esto implica que después conviene separar:
  - gastos automáticos
  - gastos por tarjeta
  - gastos manuales cargados por chat

### Delivery
- **Frecuente**
- Conviene que sea categoría aparte

### Farmacia
- No es gasto importante recurrente, según lo que dijiste

---

## 9) Bybit y conversión de moneda

### Situación actual
- Querés usar Bybit para:
  - vender EUR
  - comprar USDT
  - hacer P2P **USDT → ARS**

### Estado actual
- Ya hiciste una primera compra de pesos
- **No tenés API keys**
- **No sabés crearlas**
- **Frecuencia ideal de operación:** 1 o 2 veces por mes
- **Métrica que querés trackear:**
  **cuántos ARS obtuviste**
- **No hace falta por ahora:** análisis fino de spread

---

# Qué debería mostrar el dashboard final

Tu idea final, resumida bien:

## Vista principal
- **Qué debo hoy**
  - expensas
  - luz
  - gas
  - ABL
  - salud
  - telecom
  - suscripciones
  - alquiler cuando corresponda

- **Qué ya pagué**
  - por mes
  - por categoría

- **Qué falta pagar**
  - vencimientos
  - importes
  - prioridad

- **Dónde está la plata**
  - EUR
  - USDT
  - ARS
  - cuenta/billetera/plataforma

- **Resumen mensual**
  - obligaciones del mes
  - pagos realizados
  - pagos pendientes
  - gastos recurrentes
  - observaciones

---

# Fuentes de datos que ya identificamos

## Automáticas relativamente accesibles
- Gmail:
  - Expensas
  - Edenor
  - Flow
  - Movistar
  - probablemente otras si las buscamos
- PDFs adjuntos:
  - expensas seguro
- Webs a consultar:
  - AGIP ABL
  - Swiss Medical
  - Edenor
  - posiblemente Metrogas
- Bybit:
  - manual al principio
  - API después

---

# Lo que yo priorizaría ahora

## Fase 1
Armar la base con estas categorías:

### Obligaciones mensuales
- Expensas
- Edenor
- Metrogas
- Flow
- Movistar línea 1
- Movistar línea 2
- ABL
- Swiss Medical
- Suscripciones
- Wellhub

### Obligaciones no mensuales
- Alquiler semestral

### Gastos recurrentes informales
- Mariano
- Lily
- Delivery

### Tesorería
- Saldo EUR
- Saldo USDT
- Saldo ARS
- Movimientos de conversión

---

# Tu “paso 0” quedó bastante claro

Antes de automatizar, el inventario base hoy es:

## Servicios/gastos confirmados
1. Expensas
2. Edenor
3. Metrogas
4. Flow
5. Movistar x2
6. ABL
7. Swiss Medical
8. Alquiler semestral
9. Netflix
10. Paramount
11. MercadoLibre Plus / Disney
12. Amazon Prime
13. YouTube Music
14. YouTube Premium
15. Wellhub
16. Paseador Mariano
17. Limpieza Lily
18. Delivery
19. Uber
20. Cabify

---

Si querés, el **siguiente mensaje** te lo puedo hacer en formato más operativo:

## “Plan de implementación v1”
con:
- qué automatizamos primero
- qué queda manual
- qué campos va a tener la tabla maestra
- y cómo sería el dashboard familiar inicial

Y después de eso me meto de una con la **primera tarea real: reconstruir histórico de expensas desde Gmail/PDFs**.


/approve


Explanation:
The user said "/approve" which means they're approving a blocked command. Let me retry the PDF extraction. I need to use a simpler approach - maybe just use `pdftotext` CLI tool or parse the PDF text differently.

Let me check if `pdftotext` is available.

terminal

which pdftotext 2>/dev/null || which pdftoppm 2>/dev/null || dpkg -l | grep poppler 2>/dev/null; echo "---"; which python3; python3 --version


Output:
---
/home/linuxbrew/.linuxbrew/bin/python3
Python 3.14.3


No hay herramientas de PDF instaladas y pip no está disponible. Voy a extraer el texto crudo del raw email (los PDFs a veces tienen texto legible entre el base64) o parsear lo relevante directamente del HTML del mail.

import json
import subprocess
import base64
from email import policy
from email.parser import BytesParser
from html.parser import HTMLParser

def run(cmd):
    r = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=120)
    return r.stdout

msg_ids = [
    ('19d489b8e69c4576', '2026-03'),
    ('19ca50f640ed3b82', '2026-02'),
    ('19c154af5e2a205b', '2026-01'),
    ('19b76b85f4f1abe3', '2025-12'),
    ('19ad752644aacb33', '2025-11'),
    ('19a55b5ae0558462', '2025-10-E7'),
    ('19a3d5f759a98968', '2025-10'),
    ('1999cccf506ca5bf', '2025-09'),
    ('19902e428ba02a0c', '2025-08'),
    ('1987b7f98006bf0e', '2025-07'),
]

# First, let's try installing pypdf via system python
# But let's also try a different approach: extract text from the HTML body first

for msg_id, period in msg_ids[:2]:
    print(f"\n{'='*60}")
    print(f"Processing {period} ({msg_id})...")

    # Get the raw message
    raw_out = run(f"gog gmail get {msg_id} --format raw --json --no-input")
    msg = json.loads(raw_out)
    raw_b64 = msg.get('raw', '')

    if not raw_b64:
        print("  No raw data!")
        continue

    # Parse as email
    raw_bytes = base64.b64decode(raw_b64)
    email_msg = BytesParser(policy=policy.default).parsebytes(raw_bytes)

    # Try to extract PDF data and also check HTML body
    html_body = ""
    pdf_count = 0
    pdf_sizes = []
    pdf_filenames = []

    for part in email_msg.walk():
        content_type = part.get_content_type()
        filename = part.get_filename()

        if content_type == 'text/html':
            html_body = part.get_payload(decode=True).decode('utf-8', errors='ignore')
        elif content_type == 'application/pdf' and filename:
            payload = part.get_payload(decode=True)
            if payload:
                pdf_count += 1
                pdf_sizes.append(len(payload))
                pdf_filenames.append(filename)

    print(f"  HTML body: {len(html_body)} chars")
    print(f"  PDFs: {pdf_count}")
    print(f"  PDF sizes: {pdf_sizes}")
    print(f"  PDF names: {pdf_filenames}")

    # Clean and show HTML body
    import re
    # Remove style blocks
    html_clean = re.sub(r'<style[^>]*>.*?</style>', '', html_body, flags=re.DOTALL)
    # Remove script blocks
    html_clean = re.sub(r'<script[^>]*>.*?</script>', '', html_clean, flags=re.DOTALL)
    # Strip tags
    text = re.sub(r'<[^>]+>', '\n', html_clean)
    # Clean up whitespace
    text = re.sub(r'\n{3,}', '\n\n', text)
    text = text.strip()

    print(f"\n  HTML text content ({len(text)} chars):")
    print(text[:2000])
    print("  ...")


Output:
============================================================
Processing 2026-03 (19d489b8e69c4576)...
  HTML body: 0 chars
  PDFs: 0
  PDF sizes: []
  PDF names: []

  HTML text content (0 chars):

  ...

============================================================
Processing 2026-02 (19ca50f640ed3b82)...

--- stderr ---
Traceback (most recent call last):
  File "/tmp/hermes_sandbox_7ks1d_vr/script.py", line 42, in <module>
    raw_bytes = base64.b64decode(raw_b64)
                ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/.local/share/uv/python/cpython-3.11.15-linux-aarch64-gnu/lib/python3.11/base64.py", line 88, in b64decode
    return binascii.a2b_base64(s, strict_mode=validate)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
binascii.Error: Invalid base64-encoded string: number of data characters (470781) cannot be 1 more than a multiple of 4

Traceback (most recent call last):
  File "/tmp/hermes_sandbox_7ks1d_vr/script.py", line 42, in <module>
    raw_bytes = base64.b64decode(raw_b64)
                ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/.local/share/uv/python/cpython-3.11.15-linux-aarch64-gnu/lib/python3.11/base64.py", line 88, in b64decode
    return binascii.a2b_base64(s, strict_mode=validate)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
binascii.Error: Invalid base64-encoded string: number of data characters (470781) cannot be 1 more than a multiple of 4


El problema es que el base64 de Gmail usa caracteres URL-safe. Voy a corregir el decode y aprovechar también el formato `full`:

Explanation:
The issue is that the raw base64 from Gmail uses URL-safe encoding (+ and / replaced with - and _). I need to convert it back. Also, the `--format raw` might not be returning the right data. Let me try the `full` format with `--format full` which should have the attachment data inline but properly structured.

Actually, looking back at the first raw output, it DID have the PDF data but I couldn't see it because of the truncation. The raw email uses quoted-printable for some parts and base64 for others.

Let me try a different approach: use `gog gmail get` with `--format full` and try to extract the base64 attachment data from the parts.

import json
import subprocess
import base64
from email import policy
from email.parser import BytesParser
import re

def run(cmd):
    r = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=120)
    return r.stdout

msg_ids = [
    ('19d489b8e69c4576', '2026-03'),
    ('19ca50f640ed3b82', '2026-02'),
    ('19c154af5e2a205b', '2026-01'),
    ('19b76b85f4f1abe3', '2025-12'),
    ('19ad752644aacb33', '2025-11'),
    ('19a55b5ae0558462', '2025-10-E7'),
    ('19a3d5f759a98968', '2025-10'),
    ('1999cccf506ca5bf', '2025-09'),
    ('19902e428ba02a0c', '2025-08'),
    ('1987b7f98006bf0e', '2025-07'),
]

# Try getting the BODY parts of the email (HTML/text)
# which sometimes has the expensa info
for msg_id, period in msg_ids[:3]:
    print(f"\n{'='*60}")
    print(f"Processing {period} ({msg_id})...")

    # Get full format
    raw_out = run(f"gog gmail get {msg_id} --format full --json --no-input")
    msg = json.loads(raw_out)
    payload = msg.get('payload', {})
    parts = payload.get('parts', [])

    # Find text/html part and multipart/alternative
    def get_html(parts_list):
        for p in parts_list:
            ct = p.get('mimeType', '')
            if ct == 'text/html':
                body = p.get('body', {}).get('data', '')
                if body:
                    # Gmail uses URL-safe base64, convert
                    body = body.replace('-', '+').replace('_', '/')
                    try:
                        return base64.b64decode(body).decode('utf-8', errors='ignore')
                    except:
                        return ''
            elif ct == 'multipart/alternative':
                sub_parts = p.get('parts', [])
                return get_html(sub_parts)
        return ''

    html = get_html(parts)
    print(f"  HTML length: {len(html)}")

    # Clean HTML to text
    html_clean = re.sub(r'<style[^>]*>.*?</style>', '', html, flags=re.DOTALL)
    html_clean = re.sub(r'<script[^>]*>.*?</script>', '', html_clean, flags=re.DOTALL)
    text = re.sub(r'<[^>]+>', '\n', html_clean)
    text = re.sub(r'\n{3,}', '\n\n', text).strip()
    print(f"  Text length: {len(text)}")
    print(f"  Text preview (first 800):")
    print(text[:800])


Output:
============================================================
Processing 2026-03 (19d489b8e69c4576)...
  HTML length: 5130
  Text length: 1375
  Text preview (first 800):
Notificación de Consorcio

ESTE ES UN MENSAJE AUTOMÁTICO - NO RESPONDER A ESTE CORREO

PARA ENVIAR UN E-MAIL A SU ADMINISTRACIÓN VEA
LA DIRECCIÓN QUE FIGURA EN LA LIQUIDACIÓN.

Para su control, le enviamos una copia de la LIQUIDACIÓN de EXPENSAS y/o los AVISOS DE PAGO de las unidades registradas a su nombre.

NOTA DE RESPONSABILIDAD:

El contenido del presente mensaje y de los archivos adjuntos es confidencial y exclusivo para su destinatario. El emisor no asume responsabilidad ni obligación legal alguna por cualquier información incorrecta o alterada contenida en este mensaje.

============================================================
Processing 2026-02 (19ca50f640ed3b82)...
  HTML length: 5130
  Text length: 1375
  Text preview (first 800):
Notificación de Consorcio

ESTE ES UN MENSAJE AUTOMÁTICO - NO RESPONDER A ESTE CORREO

PARA ENVIAR UN E-MAIL A SU ADMINISTRACIÓN VEA
LA DIRECCIÓN QUE FIGURA EN LA LIQUIDACIÓN.

Para su control, le enviamos una copia de la LIQUIDACIÓN de EXPENSAS y/o los AVISOS DE PAGO de las unidades registradas a su nombre.

NOTA DE RESPONSABILIDAD:

El contenido del presente mensaje y de los archivos adjuntos es confidencial y exclusivo para su destinatario. El emisor no asume responsabilidad ni obligación legal alguna por cualquier información incorrecta o alterada contenida en este mensaje.

============================================================
Processing 2026-01 (19c154af5e2a205b)...
  HTML length: 5130
  Text length: 1375
  Text preview (first 800):
Notificación de Consorcio

ESTE ES UN MENSAJE AUTOMÁTICO - NO RESPONDER A ESTE CORREO

PARA ENVIAR UN E-MAIL A SU ADMINISTRACIÓN VEA
LA DIRECCIÓN QUE FIGURA EN LA LIQUIDACIÓN.

Para su control, le enviamos una copia de la LIQUIDACIÓN de EXPENSAS y/o los AVISOS DE PAGO de las unidades registradas a su nombre.

NOTA DE RESPONSABILIDAD:

El contenido del presente mensaje y de los archivos adjuntos es confidencial y exclusivo para su destinatario. El emisor no asume responsabilidad ni obligación legal alguna por cualquier información incorrecta o alterada contenida en este mensaje.


El HTML es solo el template del mail. La info real está en los PDFs. Voy a instalar `poetry` para poder parsear PDFs, o usar el sistema Python.

Explanation:
The HTML body is just the notification template, not the actual expensa data. The real data is in the PDF attachments. I need to extract and parse the PDFs. Since I don't have pip or pdftotext, let me try another approach:

1. Save the raw PDF files and then use pdftotext via apt or another tool
2. Or try to install pypdf via a different method

Let me check if there's a way to install system packages or use a different Python.

apt list --installed 2>/dev/null | grep -i pdf; which pdftotext 2>/dev/null; which pdfinfo 2>/dev/null; echo "---"; /home/linuxbrew/.linuxbrew/bin/python3 --version 2>/dev/null; /home/linuxbrew/.linuxbrew/bin/uv --version 2>/dev/null


Output:
---
Python 3.14.3
uv 0.10.4 (Homebrew 2026-02-17)


import subprocess

# Use uv to run a one-shot script with pypdf
result = subprocess.run(
    ['uv', 'run', '--with', 'pypdf', 'python', '-c',
     'print("pypdf works!")'],
    capture_output=True, text=True, timeout=120
)
print("stdout:", result.stdout)
print("stderr:", result.stderr[-300:] if len(result.stderr) > 300 else result.stderr)
print("returncode:", result.returncode)


Output:
stdout: pypdf works!

stderr: Installed 1 package in 3ms

returncode: 0


Perfecto, puedo usar `uv run --with pypdf` para los PDFs. Ahora necesito descargarlos. `gog` no tiene comando de attachments, pero el formato `raw` del email ya trae todo en base64. Voy a arreglar el decode:

Explanation:
I have a way forward with `uv` to run pypdf. But first I need to download the PDFs from the emails. The `gog` tool doesn't support `attachment` directly.

However, the raw email format already contains the PDF data encoded in base64 URL-safe format. I just need to properly extract it.

The issue I hit before was with URL-safe base64. Let me fix that and extract the PDFs properly.

import json
import subprocess
import base64
from email import policy
from email.parser import BytesParser
import os
import re

def run(cmd):
    r = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=120)
    return r.stdout

msg_ids = [
    ('19d489b8e69c4576', '2026-03'),
    ('19ca50f640ed3b82', '2026-02'),
    ('19c154af5e2a205b', '2026-01'),
    ('19b76b85f4f1abe3', '2025-12'),
    ('19ad752644aacb33', '2025-11'),
    ('19a55b5ae0558462', '2025-10-E7'),
    ('19a3d5f759a98968', '2025-10'),
    ('1999cccf506ca5bf', '2025-09'),
    ('19902e428ba02a0c', '2025-08'),
    ('1987b7f98006bf0e', '2025-07'),
]

os.makedirs('/tmp/expensas_pdfs', exist_ok=True)

def decode_gmail_b64(s):
    """Decode Gmail's URL-safe base64"""
    s = s.replace('-', '+').replace('_', '/')
    # Pad if needed
    padding = 4 - (len(s) % 4)
    if padding != 4:
        s += '=' * padding
    return base64.b64decode(s)

all_results = []

for msg_id, period in msg_ids:
    print(f"\nProcessing {period} ({msg_id})...")

    raw_out = run(f"gog gmail get {msg_id} --format raw --json --no-input")
    msg = json.loads(raw_out)
    raw_b64 = msg.get('raw', '')

    if not raw_b64:
        print("  No raw data!")
        continue

    # Parse as email using proper decoding
    raw_bytes = decode_gmail_b64(raw_b64)
    email_msg = BytesParser(policy=policy.default).parsebytes(raw_bytes)

    # Find PDF parts
    for part in email_msg.walk():
        ct = part.get_content_type()
        fname = part.get_filename('')
        if ct == 'application/pdf' and fname.endswith('.pdf'):
            payload = part.get_payload(decode=True)
            if payload:
                pdf_path = f'/tmp/expensas_pdfs/{fname}'
                with open(pdf_path, 'wb') as f:
                    f.write(payload)
                print(f"  Saved PDF: {fname} ({len(payload)} bytes)")

                # Extract text using python
                # We'll do this batch after downloading
                all_results.append({'path': pdf_path, 'period': period, 'filename': fname})

print("\n=== Download complete ===")
for r in all_results:
    print(f"  {r['filename']}")


Output:
Processing 2026-03 (19d489b8e69c4576)...
  Saved PDF: 2026_03_31_liq_6955_697595_9695.pdf (37298 bytes)
  Saved PDF: 2026_03_31_aviso_011_697595_00083.pdf (207173 bytes)

Processing 2026-02 (19ca50f640ed3b82)...
  Saved PDF: 2026_02_28_liq_6955_697595_8915.pdf (40190 bytes)
  Saved PDF: 2026_02_28_aviso_011_697595_00083.pdf (207312 bytes)

Processing 2026-01 (19c154af5e2a205b)...
  Saved PDF: 2026_01_31_liq_6955_697595_8276.pdf (40441 bytes)
  Saved PDF: 2026_01_31_aviso_011_697595_00083.pdf (207318 bytes)

Processing 2025-12 (19b76b85f4f1abe3)...
  Saved PDF: 2025_12_31_liq_6955_697595_7561.pdf (40134 bytes)
  Saved PDF: 2025_12_31_aviso_011_697595_00083.pdf (207468 bytes)

Processing 2025-11 (19ad752644aacb33)...
  Saved PDF: 2025_11_30_liq_6955_697595_6784.pdf (40362 bytes)
  Saved PDF: 2025_11_30_aviso_011_697595_00083.pdf (207239 bytes)

Processing 2025-10-E7 (19a55b5ae0558462)...

Processing 2025-10 (19a3d5f759a98968)...
  Saved PDF: 2025_10_31_liq_6955_697595_6107.pdf (40097 bytes)
  Saved PDF: 2025_10_31_aviso_011_697595_00083.pdf (207224 bytes)

Processing 2025-09 (1999cccf506ca5bf)...
  Saved PDF: 2025_09_30_liq_6955_697595_5357.pdf (40059 bytes)
  Saved PDF: 2025_09_30_aviso_011_697595_00083.pdf (207508 bytes)

Processing 2025-08 (19902e428ba02a0c)...
  Saved PDF: 2025_08_31_liq_6955_697595_4674.pdf (41761 bytes)
  Saved PDF: 2025_08_31_aviso_011_697595_00083.pdf (207099 bytes)

Processing 2025-07 (1987b7f98006bf0e)...
  Saved PDF: 2025_07_31_liq_6955_697595_4012.pdf (41653 bytes)
  Saved PDF: 2025_07_31_aviso_011_697595_00083.pdf (207216 bytes)

=== Download complete ===
  2026_03_31_liq_6955_697595_9695.pdf
  2026_03_31_aviso_011_697595_00083.pdf
  2026_02_28_liq_6955_697595_8915.pdf
  2026_02_28_aviso_011_697595_00083.pdf
  2026_01_31_liq_6955_697595_8276.pdf
  2026_01_31_aviso_011_697595_00083.pdf
  2025_12_31_liq_6955_697595_7561.pdf
  2025_12_31_aviso_011_697595_00083.pdf
  2025_11_30_liq_6955_697595_6784.pdf
  2025_11_30_aviso_011_697595_00083.pdf
  2025_10_31_liq_6955_697595_6107.pdf
  2025_10_31_aviso_011_697595_00083.pdf
  2025_09_30_liq_6955_697595_5357.pdf
  2025_09_30_aviso_011_697595_00083.pdf
  2025_08_31_liq_6955_697595_4674.pdf
  2025_08_31_aviso_011_697595_00083.pdf
  2025_07_31_liq_6955_697595_4012.pdf
  2025_07_31_aviso_011_697595_00083.pdf


Explanation: We have 10 pairs of PDFs downloaded. The "aviso" files are the payment notices (with the unit info) and "liq" are the detailed liquidations. Let me also check the 2025-10 E7 one separately. Now let me write and run a script using pypdf via uv run to extract text from all those PDFs.

uv run --with pypdf /dev/stdin << 'SCRIPT'
import os
from pypdf import PdfReader

pdf_dir = '/tmp/expensas_pdfs'
results = {}

for fname in sorted(os.listdir(pdf_dir)):
    if not fname.endswith('.pdf'):
        continue
    fpath = os.path.join(pdf_dir, fname)
    try:
        reader = PdfReader(fpath)
        text = ""
        for i, page in enumerate(reader.pages):
            text += page.extract_text() or ""

        # Print first 2000 chars
        print(f"\n{'='*80}")
        print(f"FILE: {fname}")
        print(f"Pages: {len(reader.pages)}")
        print(f"{'='*80}")
        print(text[:2000])
        print("...")

    except Exception as e:
        print(f"\n{fname}: ERROR - {e}")
SCRIPT


Output:
error: Failed to spawn: `/dev/stdin`
  Caused by: Permission denied (os error 13)


Explanation: La sintaxis heredoc con uv no funciona como esperaba. Necesito usar una otra forma de ejecutar el script Python con uv --with pypdf. Puedo escribir el script en un archivo temporal y ejecutarlo

import os
from pypdf import PdfReader

pdf_dir = '/tmp/expensas_pdfs'

for fname in sorted(os.listdir(pdf_dir)):
    if not fname.endswith('.pdf'):
        continue
    fpath = os.path.join(pdf_dir, fname)
    try:
        reader = PdfReader(fpath)
        text = ""
        for i, page in enumerate(reader.pages):
            text += page.extract_text() or ""

        print(f"\n{'='*80}")
        print(f"FILE: {fname}")
        print(f"Pages: {len(reader.pages)}")
        print(f"{'='*80}")
        print(text[:2500])
        print("...")

    except Exception as e:
        print(f"\n{fname}: ERROR - {e}")


uv run --with pypdf python /tmp/extract_pdfs.py 2>/dev/null