Agent tools include search, file systems, code execution, and external APIs.

Agent memory persists across tasks, not just within one session.

An agent's loop keeps running until the task is finished, not just until one response is generated.

When given a research task, the agent identifies sub-questions, searches for information, evaluates sources, extracts direct answers, and delivers a structured summary.

The summary should use headers, bullet points, and clear sections, explicitly stating uncertainties and avoiding filler.

The writing style should be conversational, direct, use short sentences/paragraphs, include specific numbers/examples, and end with a call to action/thought.

When given a writing task, the agent asks for topic/audience/length, writes a first draft, reviews against style rules, and delivers a final version.

The agent should never add unnecessary introductions and always start with the most important point.

When given a coding task, the agent confirms understanding, writes clear code, identifies edge cases, and systematically debugs errors.

Code rules include writing clean, readable code with meaningful variable names, including error handling, explaining sections in plain language, and asking clarifying questions if unsure.

The communication style should be direct, respectful, without formalities, get to the point, and close with a clear next step.

When given an email task, the agent identifies the goal, writes a subject line, drafts the email in 3-5 short paragraphs, and ends with one clear action item.

The agent should always write ready-to-send emails, not templates with blanks.

When tasks/goals are shared, the agent identifies urgency, suggests realistic sequences, estimates time, and flags items for delegation/elimination.

When a project is described, the agent breaks it into next actions, identifies the most important first step, and creates a simple checklist.

The agent should keep planning practical, provide only the plan, and avoid motivational filler.

Once the VPS server is set up, install Claude Code by running npm i -g @anthropic-ai/claude-code in the terminal.

The bot requirements include Python language, python-telegram-bot library, claude-sonnet-4-6 model, message handling, conversation history per user, and /start and /clear commands.

Claude Code should create main bot file, requirements.txt, and a .env file, reading API keys from environment variables.

The service should start automatically on boot, restart on crashes, load environment variables from .env, and save logs to a file.

The exact terminal commands for installing and starting the service should also be provided.

Conversation history for each user should be saved to a JSON file on disk, with a maximum length of the last 20 messages.

Web search capability should be added using the Tavily API, and Claude's tool use feature should be implemented cleanly.

The bot should decide on its own when to search for current information or answer from memory.

A note-saving feature should be added, triggered by phrases like "save this," "remember this," or "note:", along with /notes and /clearnotes commands.

User restriction should be added, using the Telegram user ID from the .env file as ALLOWED_USER_ID, and responding with "This bot is private" to unauthorized users.

Instructions should be provided on how to find the Telegram user ID, either through the bot or otherwise.

Basic cost tracking should be added, logging input and output tokens after each Claude API response, maintaining a running total in a costs.json file.

A /costs command should be added to display total tokens used today and overall.

Every day at 8:00 am, the bot should send a message containing a motivational one-liner, a reminder for the top priority, and a random productivity/AI fact.

The briefing should be sent to the Telegram user ID from the .env file, using schedule or APScheduler library.

Long tasks can exceed Claude's context limit, causing the agent to lose the original goal, past decisions, and constraints from the conversation's beginning.

When a session is closed and reopened, the agent starts from zero, losing all previous context.

If the agent is interrupted mid-task, it has no way of knowing where it stopped, leading to potential rework.

A prompt is provided for the agent to write a brief checkpoint, covering completed items, key decisions, remaining tasks, and necessary context for continuation, under 200 words.

A specific format is provided for the memory note to be written after each major step, including what was done, key decisions, current state, and next steps.

A prompt for resuming a task from a previous session instructs the agent to confirm understanding, identify the next step, and ask questions based on a pasted memory note.

A prompt is provided for compressing a long conversation into a summary of the original goal, what has been done, what was found, and key decisions made.