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ChronoAIProject/lark-im-ops

Envoyer, répondre, réagir, lire et trouver des messages et discussions Lark via les API im/v1 du bot, avec gestion des autorisations manquantes.

Qu'est-ce que lark-im-ops ?

Cette compétence permet à un agent IA d'interagir avec les messages et les discussions de Lark (feishu) en utilisant les API im/v1 via le courtier NyxID. Elle offre des fonctionnalités complètes : envoyer des messages dans un chat ou en MP, répondre dans un fil de discussion, ajouter ou supprimer des réactions émoji, lire un message spécifique, récupérer l'historique d'un chat sur une fenêtre temporelle, rechercher ou lister les chats auxquels le bot participe, et télécharger des images ou fichiers joints. Un point fort technique est la gestion de l'exigence 'content must be a JSON string' (le contenu doit être une chaîne JSON) et la sélection automatique du `receive_id_type` approprié, évitant des erreurs courantes. De plus, elle agit comme une sonde de périmètre IM : en cas d'erreur 403 (autorisation insuffisante), elle signale précisément la permission manquante au lieu d'échouer silencieusement, facilitant le débogage et la configuration des rôles. Elle est conçue pour fonctionner avec des agents comme Claude Code, Cursor, ou Codex, en automatisant les opérations de messagerie collaborative sans intervention humaine.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/ChronoAIProject/nyx-skills/tree/main/lark-im-ops
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Documentation

Que fait lark-im-ops ?

Envoyer, répondre, réagir, lire et trouver des messages et discussions Lark via les API im/v1 du bot utilisant le courtier NyxID — poster de manière proactive dans un chat ou en MP, répondre dans un fil de discussion, ajouter ou supprimer des réactions émoji, lire un message spécifique ou récupérer l'historique d'un chat sur une fenêtre temporelle, rechercher ou lister les chats auxquels le bot participe, et télécharger une image ou un fichier depuis un message. Gère l'exigence « le contenu doit être une chaîne JSON » et la sélection du receive_id_type, et sert également de sonde de périmètre IM : en cas d'erreur 403, elle signale la permission manquante exacte au lieu d'échouer silencieusement.

Individual skills in this repo

This repo contains 9 individual skills — each has its own dedicated page.

ChronoAIProject/aevatar-attachment-probe

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.

ChronoAIProject/aevatar-vision-probe

Verify the read-image (vision) path end to end — the user sends an image plus the trigger, a workflow vision step describes it, and the run output proves the model actually saw the image (a real description, not "no image received"). Run it before any image-dependent workflow (petty-cash receipt, invoice OCR).

ChronoAIProject/codex-formal-answer

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.

ChronoAIProject/nyx-skills

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.

ChronoAIProject/nyx-skills

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.

ChronoAIProject/ornn-agent-manual-cli

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

ChronoAIProject/petty-cash-approval-payload-builder

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.

ChronoAIProject/public-copy-release-guard

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.

ChronoAIProject/research-claim-fidelity-reviewer

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.

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