Azure AI Openai Dotnet
microsoft/skills
Azure OpenAI SDK for .NET. An agent skill from microsoft/skills.
Builds a realtime voice-chat app around a talking character portrait made from your photo or a text description, with mouth sprites driven by the audio.
Install Talking Avatar Voice Chat App
$ npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments talking-avatar --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/talking-avatar .claude/skills/talking-avatar && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "talking-avatar" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/talking-avatar into .claude/skills/talking-avatar/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "talking-avatar", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/talking-avatarType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments talking-avatar --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/talking-avatar .agents/skills/talking-avatar && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "talking-avatar" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/talking-avatar into .agents/skills/talking-avatar/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "talking-avatar", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments talking-avatar --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/talking-avatar .cursor/skills/talking-avatar && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "talking-avatar" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/talking-avatar into .cursor/skills/talking-avatar/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "talking-avatar", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/buildfastwithai/gen-ai-experiments.git --path skills/talking-avatar--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments talking-avatar --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/talking-avatar .gemini/skills/talking-avatar && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "talking-avatar" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/talking-avatar into .gemini/skills/talking-avatar/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "talking-avatar", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install buildfastwithai/gen-ai-experiments talking-avatarInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/talking-avatar .github/skills/talking-avatar && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "talking-avatar" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/talking-avatar into .github/skills/talking-avatar/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "talking-avatar", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments talking-avatar --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/talking-avatar .opencode/skills/talking-avatar && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "talking-avatar" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/talking-avatar into .opencode/skills/talking-avatar/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "talking-avatar", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
talking-avatar- GitHub stars
- 785
- Token cost
- ~1.7k tokens
- SKILL.md length
- 832 words
- Files
- 14 (incl. scripts, references, assets)
- Skills in repo
- 13
- Repo updated
- First seen
- Licence
- MIT
At a glance
Builds a realtime voice-chat app around a talking character portrait made from your photo or a text description, with mouth sprites driven by the audio.
- Works in 3 steps: Photograph supplied: inspect it with… → Character described: normalize the… → Establish the character name, concise…
- Creating a talking photo avatar you can speak with in the browser
- SKILL.md covers Coordinate the required skills, Choose the input path, Build the asset set and Build or integrate the app, plus 4 more sections
- Calls python3
What it does
The goal is one focused voice conversation with a fixed character portrait. From a supplied photo, treated as an identity reference, or a described character, the agent generates a canonical front-facing portrait, then three identity-preserving mouth edits named soft, round and open, rejecting any frame where more than the lips changed. The mouth region is cropped identically across the three frames so the app can swap sprites as the remote audio plays.
The app is built on OpenAI Realtime in Vite or Next.js, starting from bundled templates that include a session route and a test file. Image generation is handed to a separate image skill, and the agent is told to check OpenAI's current documentation for the Realtime endpoint, session schema, model and voices instead of trusting the skill's own examples. It covers bring-your-own API key intake, testing and deployment, and adds no backgrounds, wardrobes or extra characters by default.
When your agent uses it
- Creating a talking photo avatar you can speak with in the browser
- Building a character chatbot or roleplay voice companion
- Adding audio-driven mouth animation to a generated character portrait
Example prompts
- “Turn the photo at ./me.jpg into a talking avatar I can chat with by voice.”
- “Build a pirate captain voice companion with an illustrated look and a deep voice.”
- “Add lip sync to this Next.js voice app using three mouth sprites.”
Requirements
- An OpenAI API key supplied by you
- An image generation skill or tool for the portrait and mouth frames
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Photograph supplied: inspect it with view_image, treat it as an identity reference, and preserve face shape, skin tone, hair, glasses…
- Character described: normalize the description into a concrete visual spec. Ask only for a missing detail that materially changes…
- Establish the character name, concise persona, app name, language, and preferred voice. Infer reasonable defaults when safe.
What it can do on your machine
Read from SKILL.md and the folder at commit 7b62043. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Talking Avatar Voice Chat App loads about 1.7k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 119 tokens; SKILL.md has 832 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from buildfastwithai/gen-ai-experiments at commit 7b62043, republished under its MIT licence (© buildfastwithai). 832 words, ~1,747 tokens.
Download SKILL.mdSave it as
.claude/skills/talking-avatar/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.- name
- talking-avatar
- description
- Build or transform lightweight realtime voice-chat apps with a talking character avatar from either a user-supplied photograph or a text description. Use when Codex is asked to create a talking avatar, photo avatar, character chatbot, roleplaying voice companion, mouth-sprite animation, or reusable OpenAI Realtime avatar app in Vite/Next.js, including BYOK API-key intake, identity-consistent image generation, audio-driven lip sync, testing, and deployment.
Talking Avatar
Create one focused voice conversation with a fixed character portrait whose mouth poses follow the actual remote audio. Accept either a photograph or a character description, generate a canonical portrait plus small mouth sprites, and build the app around OpenAI Realtime.
Coordinate the required skills
- Use
imagegenfor every canonical portrait or mouth-frame generation/edit. Read its full instructions before generating. - Use
openai-docsand, when applicable,openai-platform-api-keyto verify the current Realtime WebRTC endpoint, session schema, model, voices, and credential flow. Never rely on this skill's model examples as current truth. - Use the available frontend design skill for interface work.
- If
.openai/hosting.jsonexists, followsites-buildingandsites-hostingthrough deployment. - Do not add backgrounds, wardrobes, role modes, or multiple characters unless the user explicitly asks. The default product is one avatar and one conversation.
Choose the input path
- Photograph supplied: inspect it with
view_image, treat it as an identity reference, and preserve face shape, skin tone, hair, glasses, facial hair, and distinguishing features. - Character described: normalize the description into a concrete visual spec. Ask only for a missing detail that materially changes identity, such as photorealistic versus illustrated. Otherwise proceed with tasteful defaults.
- Establish the character name, concise persona, app name, language, and preferred voice. Infer reasonable defaults when safe.
Read references/image-pipeline.md before creating or editing the avatar assets.
Build the asset set
- Generate one canonical, front-facing, evenly lit, closed-mouth portrait. Lock its canvas, crop, head position, gaze, pose, clothing, background, and lighting.
- From that canonical image, generate three separate identity-preserving edits:
soft: lips slightly parted;round: a natural rounded vowel;open: a natural medium-open vowel.
- Repeat the invariants in every edit prompt. Change only the immediate lips and inner mouth. Reject frames with moved eyes, jaw, glasses, head, beard, clothing, or lighting.
- Crop the smallest practical identical rectangle from all three speech frames. Keep enough surrounding skin for feathering, but do not ship full-frame speech images.
- Save exactly these project assets by default:
public/avatar/avatar-base.jpgpublic/avatar/mouth-soft.pngpublic/avatar/mouth-round.pngpublic/avatar/mouth-open.png
- Run
scripts/validate_avatar_assets.py --dir <project>/public/avatarand fix all failures.
Never animate the complete portrait. Only replace the mouth patch.
Build or integrate the app
Prefer an existing working stack. For a new project, use Next.js or vinext because the user's API key must cross a server route; use plain Vite only when a safe backend already exists.
For a compatible new Next/vinext project, scaffold the proven core:
python3 <skill-dir>/scripts/scaffold_app.py \
--target <project-dir> \
--character-name "Mira" \
--app-name "Mira Live" \
--persona "a concise, curious field researcher" \
--model "<verified-realtime-model>" \
--transcription-model "<verified-transcription-model>" \
--voice "<verified-realtime-voice>"The target must already contain the framework configuration and dependencies. Install framer-motion and @phosphor-icons/react when missing. Use --force only for a newly initialized project after inspecting the files it will replace. For an existing product, copy or adapt individual files from assets/starter/ instead of overwriting its architecture.
Read references/app-contract.md before changing the Realtime route, API-key flow, or event handling.
Show full SKILL.md (370 more words)Show less
Implement natural lip sync
Read references/realtime-lipsync.md and preserve its behavioral contract:
- Analyze the remote output
MediaStream, not microphone input and not Realtime event cadence. - Sample audio every animation frame without updating React state.
- Update the visible mouth pose no faster than about every 96 ms.
- Smooth attack and decay, normalize against a rolling peak, and close after short silence.
- Move through adjacent poses (
closed → soft → round → open) instead of jumping. - Set a DOM
data-mouthattribute and let CSS reveal exactly one patch. - Keep the portrait pixels completely static.
Do not use a canned infinite mouth animation. response.output_audio.delta identifies speaking state but does not provide lip timing.
Preserve the BYOK security boundary
- Ask the user to enter the OpenAI API key inside the app.
- Keep the key in component memory only; never use local storage, session storage, IndexedDB, analytics, logs, or source files.
- Send it once to the same-origin session route over HTTPS, clear client state after negotiation, and return
Cache-Control: no-store. - Never expose a long-lived key directly to the Realtime WebRTC peer connection.
- Map upstream authentication and rate-limit failures to short user-facing errors without echoing response bodies or secrets.
Validate before handoff
- Run the production build.
- Run lint and the starter regression test when available.
- Confirm the source contains an
AnalyserNode,requestAnimationFrame, a 90–110 ms pose interval, adjacent-pose stepping, and no CSS@keyframes mouth-*loop. - Confirm all mouth patches have identical dimensions and align over the canonical mouth at desktop and mobile sizes.
- Confirm only the mouth changes during speech; blinking, head bobbing, pose drift, and full-frame swaps are failures.
- Confirm mic mute, speaker mute, interruption, transcript deltas, typed messages, disconnect cleanup, audio-context cleanup, empty state, connection errors, and invalid-key errors.
- If the user requests visual browser QA, test a live voice turn and tune timing from observed speech. Otherwise do not claim that browser-level lip sync was visually verified.
- Deploy when requested or when the active hosting skill requires it, then return the live URL.
Deliverables
Return the runnable app, the final avatar asset paths, the generation prompt set, whether photo or description mode was used, the Realtime model/voice verified for that run, validation results, and the deployed URL when applicable.
© buildfastwithai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 13 other files (scripts, references, assets) in skills/talking-avatar of buildfastwithai/gen-ai-experiments.
- SKILL.md
- agents/openai.yaml
- assets/starter/app/TalkingAvatarApp.tsx.tmpl
- assets/starter/app/api/realtime/session/route.ts.tmpl
- assets/starter/app/globals.css.tmpl
- assets/starter/app/layout.tsx.tmpl
- assets/starter/app/page.tsx.tmpl
- assets/starter/tests/talking-avatar.test.mjs.tmpl
- references/app-contract.md
- references/image-pipeline.md
- references/realtime-lipsync.md
- scripts
- … and 2 more
Open the folder on GitHubat commit 7b62043
Compare with similar skills
Talking Avatar Voice Chat App next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Talking Avatar Voice Chat App this skillbuildfastwithai/gen-ai-experiments | 785 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Azure AI Openai Dotnetmicrosoft/skills | 3.1k | 5 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Mem0 Provider for Vercel AI SDKmem0ai/mem0 | 67k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Local AI Useamd/skills | 406 | — | ~5k | Automated safety check: Notes | MIT | |
| Gpt Imagenikships/droidproxy | 122 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Fastllm Gatewayazrtydxb/Fastllm-proxy | 108 | — | ~926 | Automated safety check: Pass | Apache-2.0 |
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Related
Questions about Talking Avatar Voice Chat App
What does Talking Avatar Voice Chat App do?
Builds a realtime voice-chat app around a talking character portrait made from your photo or a text description, with mouth sprites driven by the audio. The goal is one focused voice conversation with a fixed character portrait. From a supplied photo, treated as an identity reference, or a described character, the agent generates a canonical front-facing portrait, then three identity-preserving mouth edits named soft, round and open, rejecting any frame where more than the lips changed.
When should I use Talking Avatar Voice Chat App?
Talking Avatar Voice Chat App fits situations like: creating a talking photo avatar you can speak with in the browser; building a character chatbot or roleplay voice companion; adding audio-driven mouth animation to a generated character portrait.
How do I install Talking Avatar Voice Chat App in Claude Code?
Run `npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a claude-code`. Or copy the skill folder (skills/talking-avatar in buildfastwithai/gen-ai-experiments) into .claude/skills/talking-avatar in your project. Claude Code loads it when a task matches its description.
How do I install Talking Avatar Voice Chat App in Codex?
Run `npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a codex`. Or copy the skill folder (skills/talking-avatar in buildfastwithai/gen-ai-experiments) into .agents/skills/talking-avatar in your project. Codex loads it when a task matches its description.
Can I use Talking Avatar Voice Chat App in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatar -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/talking-avatar, .gemini/skills/talking-avatar, .github/skills/talking-avatar and .opencode/skills/talking-avatar in your project.
What does Talking Avatar Voice Chat App need to run?
Going by SKILL.md and its folder, Talking Avatar Voice Chat App needs the command-line tools its instructions call (python3). Our summary lists: An OpenAI API key supplied by you; An image generation skill or tool for the portrait and mouth frames.
Does Talking Avatar Voice Chat App access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Talking Avatar Voice Chat App safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Talking Avatar Voice Chat App use?
Talking Avatar Voice Chat App is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Talking Avatar Voice Chat App use?
About 1.7k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.4k tokens, read only when the agent opens those files.
What are the alternatives to Talking Avatar Voice Chat App?
Skills that share tags, products or a category with Talking Avatar Voice Chat App: Azure AI Openai Dotnet (microsoft/skills, 3.1k stars), Mem0 Provider for Vercel AI SDK (mem0ai/mem0, 67k stars), Local AI Use (amd/skills, 406 stars) and Gpt Image (nikships/droidproxy, 122 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Talking Avatar Voice Chat App?
buildfastwithai (a GitHub organization) maintains it in buildfastwithai/gen-ai-experiments, which has 785 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on September 22, 2026.
Source: buildfastwithai/gen-ai-experiments on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.