Créez des légendes et descriptions précises pour des images, vidéos et documents visuels autorisés sans inventer de détails invisibles.
Image and Video Captioning When to use Create accurate captions and descriptions for authorized images, videos, and visual documents without inventing unseen details. Workflow 1. Confirm the user owns the input or has permission to process it. Collect the source material, audience, output format, and acceptance criteria. 2. Separate observed facts and user-provided constraints from inference. Ask for missing information instead of inventing it. 3.
# Image and Video Captioning ## When to use Create accurate captions and descriptions for authorized images, videos, and visual documents without inventing unseen details. ## Workflow 1. Confirm the user owns the input or has permission to process it. Collect the source material, audience, output format, and acceptance criteria. 2. Separate observed facts and user-provided constraints from inference. Ask for missing information instead of inventing it. 3. Define the requested observations, extraction fields, coordinate system, confidence needs, and unsupported modalities before analysis. 4. Use <skill-tag>skill:e2013556-b0b7-50ca-880d-5cf16661f953</skill-tag> for the connected analysis workflow. Return evidence, confidence, and limitations separately. ## Output contract Return the input assumptions, the approved plan, the generated or analyzed result, uncertainty and limitations, and a concise verification checklist. Preserve source links and provider-returned identifiers when available. Stop if the required connected capability is unavailable; do not substitute local scripts, package installation, or embedded credentials. ## Safety and review Do not upload private or sensitive media without explicit authorization. Do not claim certainty beyond the evidence. Require review before externally visible, paid, destructive, or account-changing actions. ## Source and license This Aident-native re-authoring is based on [the pinned upstream source](https://github.com/zai-org/GLM-skills/tree/2ecd31c37e75671a4767342ba3a68a84c8f1b848/skills/glmv-caption), discovered through [ModelScope](https://modelscope.cn/skills/@zai-org/glmv-caption), and used under Apache-2.0. It preserves the upstream workflow goal while removing local executable, credential, and package assumptions.