accessibility
FreeEnsure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
The English summary is being prepared. Showing the original description.
Check if voice message handling is configured — verify Whisper, ElevenLabs/OpenAI TTS keys, and required tools.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Check if voice message handling is properly configured. Use ONLY the Read tool.
Read .env in the project root. Check for:
ELEVENLABS_API_KEY — ElevenLabs TTS key (primary)ELEVENLABS_VOICE_ID — optional, defaults to George voiceOPENAI_API_KEY — OpenAI TTS fallback (needed if no ElevenLabs key)WHISPER_MODEL — optional, defaults to mediumTELEGRAM_BOT_TOKEN — required (same as base telegram setup)Read .claude/skills/enable-telegram/SKILL.md to confirm the skill exists.
Telegram Voice Setup:
[x] Base Telegram: configured (run /setup-telegram for details)
[x] ElevenLabs API key: configured
[ ] ElevenLabs voice ID: using default (George - JBFqnCBsd6RMkjVDRZzb)
[ ] OpenAI API key: not set (optional fallback)
[x] Whisper model: medium (default)
[x] Voice skill: installed
ELEVENLABS_API_KEY=<key> (from elevenlabs.io) or OPENAI_API_KEY=<key> (from platform.openai.com) to .env"transcribe-anything pip package is needed for speech-to-text. Run pip install transcribe-anything to install."/setup-telegram first to configure the bot."Tell the user: "Voice pipeline ready! Run /enable-telegram to activate. Voice messages sent to the bot will be transcribed and responded to with audio."
/enable-telegram)No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
The English summary is being prepared. Showing the original description.
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
The English summary is being prepared. Showing the original description.
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
The English summary is being prepared. Showing the original description.
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
The English summary is being prepared. Showing the original description.
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
The English summary is being prepared. Showing the original description.
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
The English summary is being prepared. Showing the original description.