Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add buildfastwithai/gen-ai-experiments --skill talking-avatargit clone --depth 1 https://github.com/buildfastwithai/gen-ai-experimentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/buildfastwithai/gen-ai-experiments/talking-avatar)<a href="https://agentmods.dev/skills/buildfastwithai/gen-ai-experiments/talking-avatar"><img src="https://agentmods.dev/badge/skills/buildfastwithai/gen-ai-experiments/talking-avatar/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/buildfastwithai/gen-ai-experiments/talking-avatar"><img src="https://agentmods.dev/badge/skills/buildfastwithai/gen-ai-experiments/talking-avatar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00096 | $0.01457 |
| Opus 5 | $0.00048 | $0.00728 |
| Sonnet 5 | $0.00019 | $0.00291 |
| Haiku 4.5 | $0.00010 | $0.00146 |
Grade A, and why
talking-avatar scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 232 B
- assets/starter/app/api/realtime/session/route.ts.tmpl 3.0 KB
- assets/starter/app/globals.css.tmpl 19 KB
- assets/starter/app/layout.tsx.tmpl 1.9 KB
- assets/starter/app/page.tsx.tmpl 383 B
- assets/starter/app/TalkingAvatarApp.tsx.tmpl 27 KB
- assets/starter/tests/talking-avatar.test.mjs.tmpl 2.8 KB
- references/app-contract.md 2.9 KB
- references/image-pipeline.md 3.6 KB
- references/realtime-lipsync.md 2.7 KB
- scripts/scaffold_app.py 3.2 KB runs code
- scripts/validate_avatar_assets.py 3.6 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 101 lines · 96 tokens per session scan A 45d581446690
talking-avatar is a skill published in the GitHub repository buildfastwithai/gen-ai-experiments (763 stars, last pushed yesterday), licensed MIT. It adds 96 tokens to every session and 1,457 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
webgl-holographic-foil
A self-contained WebGL2 hero: thin-film interference over a crushed-foil surface whose palette shifts with the viewing angle; move the cursor to tilt the film.
general-video
Author or edit a custom HyperFrames composition when no specialized workflow fits, or when BRIEF.md sets flow: companion. Use for longer or multi-scene pieces, brand and sizzle reels, montages, static loops, static title cards, footage remixes, and freeform builds. Use motion-graphics instead for a short unnarrated…
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
html-ppt-taste-brutalist
16:9 HTML deck in tactical-telemetry / CRT-terminal taste. Deactivated-CRT charcoal slides, white-phosphor monospace, hazard-red accent, scanline overlay, ASCII syntax, density over decoration. Distilled from Leonxlnx/taste-skill brutalist-skill (Tactical Telemetry mode).
diagnostic-stem-delivery
Audio production with diagnostic analysis, timecode parsing from documents, and verified export workflow.
chengfeng-check-updates
An environment manager for a video-editing system. It checks whether its skills and runtime—the software needed to run them—are installed and compatible.