Tg Hub logo

Tg Hub

Organization
OpenMinis
tg-hub

A skill for reading and writing Telegram data with Python and UV. It depends only on Telethon and uses a local-first architecture: messages are synced to SQLite and then queried offline. On first use, you must log in from the terminal with a phone number verification code. After that, the session is persisted and you do not need to log in again. Supports syncing group and channel messages locally, keyword search, multi-keyword filtering, today's messages, recent messages, speaker rankings, timeline statistics, and more. This skill must be triggered whenever the user mentions "Telegram", "TG", "Telegram", "tg-hub", "sync Telegram messages", "search TG groups", "Telegram keywords", "get TG messages", or any scenario that requires programmatically reading or writing Telegram data.

Overview

PublisherOpenMinis
RepositoryMinisSkills
Skill nametg-hub
Stars
543
Forks
86
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by OpenMinis on GitHub. Read the source before you install it.

Installation

Install the Tg Hub AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/OpenMinis/MinisSkills/tree/main/tg-hub
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/OpenMinis/MinisSkills.git /tmp/MinisSkills
mkdir -p .claude/skills
cp -r /tmp/MinisSkills/tg-hub .claude/skills/tg-hub
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tg Hub in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tg Hub on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tg Hub is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

tg-hub

Based on: jackwener/tg-cli (Apache-2.0)

This skill simplifies the original repository as follows:

  • Removed the click / rich / python-dotenv / pyyaml dependencies
  • Kept only telethon as a third-party dependency
  • Removed the CLI layer and encapsulated all functionality as a synchronous Python API
  • Changed the default session/db path to /var/minis/workspace/tg-hub/
  • Changed configuration to read environment variables directly, with no .env file required

Architecture: Local-First

Telegram MTProto (telethon)
    ↓  sync / refresh (incremental)
Local SQLite  ~/.tg-hub/messages.db
    ↓  search / today / recent / filter (offline)
Structured data
  • Read operations (search/today/recent): query the local SQLite database, with no network access, and respond in milliseconds
  • Write operations (sync/refresh): connect to Telegram to fetch new messages and write them incrementally to SQLite
  • Session file: ~/.tg-hub/tg_hub.session

File Structure

/var/minis/skills/tg-hub/
├── SKILL.md
├── pyproject.toml          # telethon only
└── scripts/
    ├── __init__.py
    ├── config.py           # Configuration (environment variables / default paths)
    ├── db.py               # SQLite message storage
    ├── exceptions.py       # Structured exceptions
    └── client.py           # TGClient core class (all APIs)

First-Time Login (Must Be Done in Terminal)

tg-hub uses the MTProto protocol (not the Bot API), so you need to log in with your Telegram account.

Recommendation: Use your own TG_API_ID / TG_API_HASH whenever possible. I have synced the upstream tg-cli anti-risk-control implementation: it uses a Telegram Desktop 5.x fingerprint and prints a warning if you continue using the default api_id=2040. The public app ID is only a fallback. Using your own credentials is still recommended for long-term use.

1. Open Terminal
2. (Recommended) Set your own TG_API_ID / TG_API_HASH first
3. cd /var/minis/skills/tg-hub
4. uv run python -c "
   import sys; sys.path.insert(0,'.')
   from scripts.client import TGClient
   me = TGClient().login()
   print('Login successful:', me)
   "
5. Enter your phone number when prompted (in +86XXXXXXXXXX format)
6. Enter the verification code received in the Telegram app
7. After login succeeds, the session is saved automatically and future logins are not required

If you do not have your own credentials for now, you can log in with the built-in public credentials first. If you encounter login or fetch errors, switch to your own APP ID first.

Open Terminal to log in


Quick Start

Environment Setup

bash
cd /var/minis/skills/tg-hub
uv sync

Python Usage

python
import sys
sys.path.insert(0, "/var/minis/skills/tg-hub")
from scripts.client import TGClient

client = TGClient()

# View the current account
me = client.whoami()
print(me["name"], me["phone"])

# List all conversations (fetched from TG in real time)
chats = client.list_chats()
for c in chats[:10]:
    print(f"  [{c['type']}] {c['name']}  Unread: {c['unread']}")

# Incrementally sync a single group
n = client.sync("Group name or username", limit=1000)
print(f"Added {n} messages")

# Quickly refresh all groups (up to 500 new messages per group)
# Slight throttling is enabled by default; you can also limit this round to only the first 30 chats
result = client.refresh(delay=1.0, max_chats=30)
for name, count in result.items():
    if count > 0:
        print(f"  {name}: +{count}")

# Search by keyword
msgs = client.search("Python", hours=48)
for m in msgs:
    print(f"[{m['chat_name']}] {m['sender_name']}: {m['content'][:80]}")

# Multi-keyword filtering (OR logic)
msgs = client.filter("hiring,remote,part-time", hours=24)

# Today's messages
msgs = client.today()

# Messages from the last 12 hours
msgs = client.recent(hours=12, limit=200)

# Speaker rankings
top = client.top_senders(hours=24)
for t in top[:5]:
    print(f"  {t['sender_name']}: {t['msg_count']} messages")

# Timeline statistics
tl = client.timeline(granularity="hour", hours=48)

# Local database statistics
stats = client.stats()
print(f"{stats['total']} local messages across {len(stats['chats'])} groups")

API Quick Reference

Authentication

MethodDescription
login()Interactive login (first time, requires terminal)
whoami()Get current account information

Sync (Online)

MethodDescription
list_chats(chat_type=None)List all conversations (real time)
sync(chat, limit=5000)Sync a single group to local SQLite
sync_all(limit_per_chat=5000, delay=1.0, max_chats=None)Sync all groups (with throttling/count limit)
refresh(limit_per_chat=500, delay=1.0, max_chats=None)Quick incremental refresh (recommended for daily use)

Query (Local, Offline)

MethodDescription
search(keyword, *, chat, sender, hours, regex, limit)Keyword/regex search
filter(keywords, *, chat, hours)Multi-keyword OR filtering
today(chat=None)Today's messages
recent(hours=24, *, chat, sender, limit)Messages from the last N hours
top_senders(chat, hours, limit)Speaker rankings
timeline(chat, hours, granularity)Timeline statistics
stats()Database statistics
local_chats()List of locally synced groups
delete_chat(chat)Delete local messages for a group

Environment Variables

VariableDefault ValueDescription
TG_API_ID2040 (fallback only)Recommended: replace with your own API ID
TG_API_HASHBuilt in (fallback only)Recommended: replace with your own API Hash
TG_SESSION_NAMEtg_hubSession filename
TG_DATA_DIR~/.tg-hubData directory
TG_DB_PATH{TG_DATA_DIR}/messages.dbSQLite path
TG_DEVICE_MODELDesktopTelethon client device model
TG_SYSTEM_VERSIONmacOS 15.3Telethon client system version
TG_APP_VERSION5.12.1Telethon client version
TG_LANG_CODEenClient language code
TG_SYSTEM_LANG_CODEen-USSystem language code

Account Security Recommendations

  1. Use your own API credentials whenever possible: Go to https://my.telegram.org, create an application, and then set TG_API_ID / TG_API_HASH.
  2. Control sync frequency: Avoid repeatedly running refresh() at high frequency.
  3. Use delay and max_chats: For daily incremental refreshes, we recommend limiting the number of chats synced per round and keeping an interval between chats.
  4. Do not be too aggressive with the first full sync: tg-hub automatically applies a lower fetch limit for the first sync of each chat.
  5. Prefer read operations: Local queries such as search and statistics do not use the network, so they are much lower risk than frequent syncs.

Notes

  • The first login must be completed in an interactive terminal (a verification code is required).
  • Using your own TG_API_ID / TG_API_HASH is strongly recommended to avoid risk-control issues caused by abuse of the public APP ID.
  • tg-hub is aligned with the upstream tg-cli Telegram Desktop 5.x client fingerprint and retains environment variable overrides to reduce the risk of abnormal fingerprints.
  • If you are still using the default api_id=2040, a warning is printed during connection to remind you to switch to your own TG_API_ID / TG_API_HASH.
  • The session file is stored at /var/minis/workspace/tg-hub/tg_hub.session. Keep it safe.
  • The first run of sync_all may take a long time, depending on the number of groups and the amount of historical messages.
  • We recommend using refresh() for daily incremental updates and sync(chat, limit=10000) for the initial full sync.
  • Telegram applies rate limits to API requests. During large syncs, Telethon automatically handles flood waits.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tg Hub AI skill do?

A skill for reading and writing Telegram data with Python and UV. It depends only on Telethon and uses a local-first architecture: messages are synced to SQLite and then queried offline. On first use, you must log in from the terminal with a phone number verification code. After that, the session is persisted and you do not need to log in again. Supports syncing group and channel messages locally, keyword search, multi-keyword filtering, today's messages, recent messages, speaker rankings, timeline statistics, and more. This skill must be triggered whenever the user mentions "Telegram", "TG...

Why use Tg Hub on TypingMind?

Because you install it once and use it with any model. Tg Hub is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tg Hub in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenMinis/MinisSkills/tree/main/tg-hub. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tg Hub?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tg Hub?

As many as you like. As long as a model supports skills, you can use Tg Hub with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tg Hub AI skill free?

Yes. It is published on GitHub by OpenMinis under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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