Verifica o caminho de leitura de imagem (visão) de ponta a ponta — o usuário envia uma imagem mais o gatilho, uma etapa de visão do workflow a descreve, e a saída da execução prova que o modelo realmente viu a imagem (uma descrição real, não "nenhuma imagem recebida"). Execute antes de qualquer workflow dependente de imagem (recibo de caixa pequena, OCR de fatura).
O ChronoAIProject/aevatar-vision-probe é uma skill projetada para validar de forma confiável o pipeline de visão computacional em agentes de IA. Ela permite que desenvolvedores e usuários testem se as funcionalidades de leitura de imagem do agente estão funcionando corretamente, garantindo que o modelo descreva com precisão o conteúdo de uma imagem enviada, em vez de retornar uma mensagem de erro como "nenhuma imagem recebida". Esta skill é particularmente útil para fluxos de trabalho que dependem de análise visual, como processamento de recibos de caixa pequena ou OCR de faturas. Ao executar o probe antes de workflows reais, você pode detectar e depurar problemas de integração com câmeras, uploads ou processamento de imagem, economizando tempo e evitando falhas. A skill suporta plataformas como Claude Code, Cursor e Codex, e funciona integrando-se a workflows que possuem uma etapa de visão dedicada. O usuário envia uma imagem e um gatilho, o workflow processa a imagem através de uma etapa de visão, e a skill verifica se a descrição gerada pelo modelo é coerente com o conteúdo real da imagem. Se a descrição for vaga ou incorreta, o teste falha, indicando um problema no pipeline de visão. Isso torna a skill uma ferramenta essencial para qualquer projeto que utilize IA para análise de imagens, garantindo robustez e confiabilidade.
npx skills add https://github.com/ChronoAIProject/nyx-skills/tree/main/aevatar-vision-probeVerifique o caminho de leitura de imagem (visão) de ponta a ponta — o usuário envia uma imagem mais o gatilho, uma etapa de visão do workflow a descreve, e a saída da execução prova que o modelo realmente viu a imagem (uma descrição real, não "nenhuma imagem recebida"). Execute antes de qualquer workflow dependente de imagem (recibo de caixa pequena, OCR de fatura).
This repo contains 9 individual skills — each has its own dedicated page.
Boundary test for inbound Lark attachments — the user sends an image or file alongside this command and the bot reports exactly what it can observe on the chat path (text only, attachment refs, or nothing), without guessing. Detects when the platform wires chat-path attachment visibility so dependent skills can be upgraded.
Use when answering mathematical, conceptual, philosophical, or metaphysical questions through repository-first scientific reasoning, a clause-complete formal bridge, retained exact Lean specializations, reusable library growth, and an ordinary conversational answer; the formal work stays internal and is shown only when the user asks for it.
Send, reply to, react to, read, and find Lark messages and chats through the bot's NyxID-brokered im/v1 APIs — proactively post to a chat or DM, thread-reply, add or remove emoji reactions, read a single message or pull a chat's history over a time window, search or list the chats the bot is in, and download an image/file from a message. Handles the "content must be a JSON string" footgun and receive_id_type selection, and doubles as the im-scope probe — on a scope 403 it reports the exact missing permission instead of failing silently.
Verify-first promotion intelligence for ANY repo or org. Pulls live GitHub state, runs sshx multi-perspective consensus over it, and returns an evidence-backed board of what is genuinely promotable, how items connect, and what is unverifiable. Use when an agent or operator asks "what's worth promoting / what connects / what's real" for a target repo or org.
Method for producing short vertical videos from ChatGPT image generation through a fixed browser tab, local TTS narration, timed subtitle burn-in, cover exports, and a manifest. Use when an operator needs a reproducible image-to-short workflow with pinned session consistency, per-beat visual prompts, 1080x1920 safe-area rules, local ffmpeg/PIL assembly, and private access kept outside the skill.
The manual an AI agent loads to operate Ornn — the model-agnostic skill-lifecycle API (an npm-style registry + CLI for agent skills) — via the NyxID CLI (`nyxid proxy request ornn-api …`). Load and follow this skill WHENEVER the user asks to do anything with Ornn skills or skillsets. Skills: search Ornn or find a skill, pull or install a skill (or a specific version), run a skill, build and upload a skill, publish a new version, make a skill public / private / shared, run or read a security audit, deprecate or delete a version, diff two versions, check usage analytics, bind a skill to a NyxID service, link a skill to GitHub or sync from source, manage npm-style dist-tags, or transfer skill ownership. Skillsets — curated multi-skill bundles with a required master prompt: bundle skills into a set, create or publish a skillset, resolve its closure in one call, export a skillset as a Claude Code marketplace plugin, transfer skillset ownership, or diagnose why a shared skillset isn't visible (visibility derives fr
Runs the petty-cash approval flow end to end yourself — collect Lark DM/sheet/image context, extract receipt details, build the exact approval body, then SUBMIT the Lark approval instance.
Portable checklist for gating public-facing copy before release. Use when captions, posts, manifests, topics, titles, or launch copy must be checked for generic AI wording, leaked internal labels, forbidden experiment or tool names, platform topic allowlists, CTA spam, and unsupported overclaims.
Severity-weighted BLOCK, WARN, OK reviewer method for technical and research posts. Use when reviewing X threads, announcements, captions, or launch copy for hook strength, thread arc, generic AI vocabulary, source fidelity, claim support, numeric-claim verification, and thread format before recommending publish.
ElevenLabs text-to-speech with mac-style say UX.
Oracle CLI second-model review/debug/refactor/design with selected files, dry-run token checks, API or browser engine.
Capture and automate macOS UI with the Peekaboo CLI.
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
Build a throwaway prototype to flesh out a design — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".