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agentkit-multimedia-shopping

基于ByteDance agentkit-samples多媒体用例的小省导购员数字人带货视频生成技能,整合多模态内容生成能力(图像、视频、音频),支持AI绘画、语音合成、视频生成,与小省导购员人设融合,9:16竖屏适配,直接对接带货视频生成流程

49

Quality

55%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/agentkit-multimedia-shopping/agentkit-multimedia-shopping/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

53%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill is well-organized with a clear five-step workflow and properly signaled one-level-deep references to real bundle files, but it loses points because the referenced scripts are placeholder stubs, validation checkpoints are absent from the pipeline, and content is repeated across three sections.

Suggestions

Replace the placeholder scripts (which return None with TODO blocks) with working implementations, or explicitly document that they are stubs to be wired to the real ByteDance agentkit-samples tools.

Add explicit validation checkpoints between steps — e.g. after step 1 verify the generated image is 9:16 at ≥1080×1920, and after step 3 verify the audio is 16kHz mono wav — before proceeding to video assembly.

Consolidate the redundant workflow restatements in 操作步骤, 使用示例, and 工作流总结 into a single canonical flow to reduce token cost.

DimensionReasoningScore

Conciseness

The body is mostly efficient with clear step-based structure, but it explains basics Claude already knows (e.g. "了解AI绘画、语音合成、视频生成的基本原理" prerequisites) and repeats the full workflow in three places (操作步骤, 使用示例, 工作流总结) that could be consolidated.

3 / 5

Actionability

It points to concrete scripts and references, but the referenced scripts are placeholder stubs returning None with TODO blocks ("# 占位符返回", "return None"), so the guidance is not truly executable end-to-end — high-level steps rather than copy-paste-ready working code.

3 / 5

Workflow Clarity

The image→audio→video sequence is clearly laid out across five numbered steps with inputs/outputs, but there are no validation checkpoints between steps (e.g. verifying the 9:16 resolution or 16kHz wav format before proceeding), so the cap of 3 applies for a multi-step generation pipeline lacking validation.

3 / 5

Progressive Disclosure

Structure is good: the body is an overview with well-signaled one-level-deep references to real bundle files (references/character-profile.md, scene-templates.md, tool-usage-guide.md, scripts/*.py, assets/examples/sample-output.md), each annotated with "何时读取" timing — only minor gaps such as script-templates.md and visual-styles.md being present but not surfaced in the 资源索引.

4 / 5

Total

13

/

20

Passed

Description

56%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description conveys a concrete, narrowly-scoped capability with good specificity and distinctiveness, but it omits an explicit "Use when" trigger clause and leans on technical jargon rather than natural user phrasing.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user needs to generate a 小省导购员 digital-human shopping/livestream video or its multimodal assets (character image, scene background, voiceover, background music)."

Replace some technical terms with natural synonyms users would say (e.g. add 带货视频生成, 竖屏短视频, 数字人口播视频) to broaden natural keyword coverage.

Tighten the long single-sentence description so the what and when are each stated crisply rather than chained together.

DimensionReasoningScore

Specificity

Lists several concrete capabilities — "AI绘画、语音合成、视频生成" and "图像、视频、音频" multimodal generation tied to a 9:16 vertical video output — though it does not enumerate sub-actions like character/scene/music generation that the body covers.

4 / 5

Completeness

It clearly states what the skill does (multimodal content generation for a shopping-avatar livestream video) but there is no "Use when..." clause or equivalent explicit trigger guidance, so the "when" is only weakly implied — capping completeness at 3 per the rubric.

3 / 5

Trigger Term Quality

It contains relevant domain terms (带货视频, 多媒体, AI绘画, 语音合成, 9:16竖屏) but relies on technical jargon and brand-specific language (agentkit-samples, InfiniteTalk) rather than the natural phrases a user would spontaneously say, with no synonyms or common variations.

3 / 5

Distinctiveness Conflict Risk

The niche is fairly specific — a 小省导购员 digital-human shopping带货 video generator tied to ByteDance agentkit-samples and InfiniteTalk — making it mostly distinct with only minor overlap risk against other multimedia or livestream-generation skills.

4 / 5

Total

14

/

20

Passed

Validation

81%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 1 deeper-than-1-level

Warning

referenced_paths_exist

Referenced path issues: 2 deeper-than-1-level

Warning

Total

13

/

16

Passed

Repository
anbeime/skill
Reviewed

Table of Contents

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