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tg buttons

Automatically colors Telegram bot choice buttons as default, destructive (red), or secondary (gray) based on the action's criticality and reversibility.

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High Signal

Automatically colors Telegram bot choice buttons as default, destructive (red), or secondary (gray) based on the action's criticality and reversibility.

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Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
SKILL.sh, SKILL.md, scripts/setup_openclaw_bot.sh

Validation

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  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

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Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.0

Documentation

ClawHub primary doc Primary doc: SKILL.md 16 sections Open source page

Purpose

Every time the bot presents choices to the user, it MUST automatically color each button based on how critical, irreversible, or different the choices are. The bot never sends plain/unstyled choice buttons β€” color is always applied.

Core Rule

Whenever the bot sends 2+ choices to a user, classify each choice and assign a style automatically. This is not optional.

Telegram Bot API β€” Button Styles

Two fields on InlineKeyboardButton and KeyboardButton: style (String, Optional) β€” Button color: (omit) β€” Default accent/blue. The recommended / safe / primary action. "destructive" β€” Red. Irreversible, dangerous, or high-stakes actions. "secondary" β€” Gray/muted. Low-priority, dismiss, skip, or neutral actions. icon_custom_emoji_id (String, Optional) β€” Custom emoji icon on the button.

Automatic Classification Rules

When the bot builds a set of choice buttons, it MUST classify every choice into one of three tiers before sending:

Tier 1 β€” Default (accent/blue): The recommended path

Apply when the choice is: The safest or most common action A positive confirmation ("Yes", "Continue", "Accept", "Start") The action the bot would recommend Moving forward in a flow Do: omit the style field (or set to null).

Tier 2 β€” Destructive (red): High-stakes or irreversible

Apply when the choice: Deletes, removes, or permanently changes something Cancels an in-progress operation that loses work Blocks, bans, or restricts a user Rejects, declines, or refuses something important Spends money, tokens, or credits Cannot be undone easily Do: set "style": "destructive".

Tier 3 β€” Secondary (gray): Low-priority or escape hatch

Apply when the choice: Skips, dismisses, or postpones ("Maybe later", "Not now") Is a neutral fallback ("Back", "Cancel" when nothing is lost) Shows more info without committing ("Details", "Help") Is the least important option in the set Do: set "style": "secondary".

How to Decide β€” Contrast Matters

When choices differ in criticality, the colors MUST reflect that contrast: High contrast β€” choices have very different consequences: "Delete my account" β†’ destructive (red) "Keep my account" β†’ default (blue) Medium contrast β€” one main action, one escape: "Subscribe" β†’ default (blue) "Not now" β†’ secondary (gray) Low contrast β€” choices are roughly equal: "Option A" β†’ default (blue) "Option B" β†’ default (blue) "Skip" β†’ secondary (gray) Multiple tiers in one set: "Confirm purchase" β†’ default (blue) β€” recommended "Change amount" β†’ secondary (gray) β€” neutral/back "Cancel order" β†’ destructive (red) β€” loses progress

Classification Examples

Bot asks: "Approve this document?" [ [{"text": "βœ… Approve", "callback_data": "approve"}, {"text": "❌ Reject", "callback_data": "reject", "style": "destructive"}], [{"text": "⏭ Review later", "callback_data": "skip", "style": "secondary"}] ] Bot asks: "Pick a plan:" [ [{"text": "Free Plan", "callback_data": "free"}, {"text": "Pro Plan", "callback_data": "pro"}], [{"text": "Compare plans", "callback_data": "compare", "style": "secondary"}] ] (Equal choices = both default; info link = secondary) Bot asks: "Delete all messages in this chat?" [ [{"text": "πŸ—‘ Delete all", "callback_data": "delete_all", "style": "destructive"}], [{"text": "Keep messages", "callback_data": "keep"}] ] (Destructive action is red; safe action is the default blue) Bot asks: "Transfer 500 tokens to @user?" [ [{"text": "Send 500 tokens", "callback_data": "send", "style": "destructive"}, {"text": "Cancel", "callback_data": "cancel", "style": "secondary"}] ] (Spending = destructive since it costs something; cancel = secondary)

Implementation β€” Python Auto-Classifier

The bot MUST use a classifier function to determine style. Here is the reference implementation: import re # Keywords that signal each tier (case-insensitive, matched against button text + callback_data) DESTRUCTIVE_SIGNALS = [ r"\bdelete\b", r"\bremove\b", r"\bban\b", r"\bblock\b", r"\breject\b", r"\bdecline\b", r"\brevoke\b", r"\bterminate\b", r"\bcancel order\b", r"\bcancel subscription\b", r"\bunsubscribe\b", r"\bdestroy\b", r"\bpurge\b", r"\bspend\b", r"\btransfer\b", r"\bpay\b", r"\bsend.*tokens?\b", r"\breset\b", r"\bclear all\b", r"\bwipe\b", r"\bleave\b", r"\bquit\b", r"\bdisconnect\b", ] SECONDARY_SIGNALS = [ r"\bskip\b", r"\bnot now\b", r"\bmaybe later\b", r"\blater\b", r"\bback\b", r"\bdismiss\b", r"\bclose\b", r"\bdetails\b", r"\bmore info\b", r"\bhelp\b", r"\babout\b", r"\bno thanks\b", r"\bnevermind\b", r"\bcancel$", # plain "cancel" (no lost work) = secondary, not destructive ] def classify_button_style(text: str, callback_data: str = "", context_hint: str = "") -> str | None: """ Automatically determine the button style based on its text and context. Returns: "destructive" β€” red button (irreversible / high-stakes) "secondary" β€” gray button (low-priority / dismiss) None β€” default blue button (primary / recommended) context_hint: optional extra context like "this action costs money" """ combined = f"{text} {callback_data} {context_hint}".lower() # Check destructive first (higher priority) for pattern in DESTRUCTIVE_SIGNALS: if re.search(pattern, combined): return "destructive" # Then secondary for pattern in SECONDARY_SIGNALS: if re.search(pattern, combined): return "secondary" # Default = primary (blue) return None def build_choice_buttons(choices: list[dict]) -> list[list[dict]]: """ Takes a list of raw choices and returns Bot API inline_keyboard rows with styles automatically assigned. Each choice dict: text (str): Button label (required) data (str): callback_data (required unless url is set) url (str): URL button (optional, mutually exclusive with data) style (str|None): Override style β€” if set, skip auto-classification context (str): Extra hint for classifier (e.g. "costs money") emoji_id (str): Custom emoji ID (optional) row (int): Force button into a specific row (optional) Returns list of rows suitable for inline_keyboard. """ # Group by row row_map: dict[int, list[dict]] = {} auto_row = 0 for i, choice in enumerate(choices): btn: dict = {"text": choice["text"]} # Action if "url" in choice: btn["url"] = choice["url"] else: btn["callback_data"] = choice.get("data", choice["text"].lower().replace(" ", "_")) # Style β€” use override if provided, else auto-classify if "style" in choice and choice["style"] is not None: btn["style"] = choice["style"] else: auto_style = classify_button_style( choice["text"], choice.get("data", ""), choice.get("context", ""), ) if auto_style: btn["style"] = auto_style # Custom emoji if "emoji_id" in choice: btn["icon_custom_emoji_id"] = choice["emoji_id"] # Row assignment target_row = choice.get("row", auto_row) row_map.setdefault(target_row, []).append(btn) # Auto-advance row every 2 buttons if len(row_map.get(auto_row, [])) >= 2: auto_row += 1 return [row_map[k] for k in sorted(row_map.keys())]

Using the classifier in the bot:

import requests def send_choices(bot_token, chat_id, text, choices, parse_mode="HTML"): """Send a message with auto-colored choice buttons.""" keyboard = build_choice_buttons(choices) payload = { "chat_id": chat_id, "text": text, "parse_mode": parse_mode, "reply_markup": {"inline_keyboard": keyboard}, } url = f"https://api.telegram.org/bot{bot_token}/sendMessage" resp = requests.post(url, json=payload) resp.raise_for_status() return resp.json() # The bot just passes raw choices β€” colors are assigned automatically: send_choices(TOKEN, chat_id, "Approve this document?", [ {"text": "βœ… Approve", "data": "approve"}, {"text": "❌ Reject", "data": "reject"}, # auto β†’ destructive (red) {"text": "⏭ Review later", "data": "later"}, # auto β†’ secondary (gray) ])

Usage with python-telegram-bot library

If the library version does not yet expose style, pass it via api_kwargs. from telegram import InlineKeyboardButton, InlineKeyboardMarkup def auto_button(text, callback_data, context=""): style = classify_button_style(text, callback_data, context) kwargs = {"style": style} if style else {} return InlineKeyboardButton(text, callback_data=callback_data, api_kwargs=kwargs) keyboard = InlineKeyboardMarkup([ [auto_button("Approve", "approve"), auto_button("Reject", "reject")], [auto_button("Skip", "skip")], ]) await update.message.reply_text("Pick an option:", reply_markup=keyboard)

Reply Keyboard β€” Same Rules Apply

{ "chat_id": "<CHAT_ID>", "text": "Delete your data?", "reply_markup": { "keyboard": [ [ {"text": "Keep my data"}, {"text": "Delete everything", "style": "destructive"} ] ], "resize_keyboard": true, "one_time_keyboard": true } }

Custom Emoji on Buttons

Can be combined with style on the same button: {"text": "Boost", "callback_data": "boost", "style": "destructive", "icon_custom_emoji_id": "5368324170671202286"}

Shell Helper

Quick test with colored buttons: ./SKILL.sh <BOT_TOKEN> <CHAT_ID>

References

Telegram Bot API: https://core.telegram.org/bots/api Bot API Changelog: https://core.telegram.org/bots/api-changelog Telegram Blog Announcement: https://telegram.org/blog/crafting-android-design-and-more

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
2 Scripts1 Docs
  • SKILL.md Primary doc
  • scripts/setup_openclaw_bot.sh Scripts
  • SKILL.sh Scripts