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agentkit-multimedia-shopping

电商运营与内容创作者在需要制作带货短视频时,用此技能一键生成9:16竖屏数字人成片。自动编排AI绘画、语音合成与视频生成,快速产出“小省导购员”专属形象与专业配音,让多模态视频制作省时省力。

53

Quality

61%

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 body presents a clear, well-sequenced five-step workflow with explicit inputs/outputs, optional branches, and well-signaled one-level-deep references to real bundle files. Its main weaknesses are missing validation checkpoints, no executable command invocations, redundant workflow restatements, and a few orphaned bundle files that are never linked.

Suggestions

Add explicit validation steps after each generation stage (e.g., verify the output image is ≥1080×1920 and the wav is 16kHz mono before moving to video composition), with a fix-and-retry loop.

Show at least one concrete executable invocation per script with its arguments (e.g., `python scripts/generate_character.py --prompt "..." --aspect_ratio 9:16 --resolution 1080x1920`) so the guidance is copy-paste runnable.

Cut the redundant restatements — replace 使用示例-示例1 and the 工作流总结 ASCII diagram with pointers to a single workflow description, and drop the 前置知识 section's explanations of basic AI/TTS/video principles.

Link the orphaned bundle files (references/script-templates.md, references/visual-styles.md, assets/templates/*.md) from the 资源索引 with when-to-read annotations so they are discoverable.

DimensionReasoningScore

Conciseness

The body is mostly operational, but "前置知识" makes Claude review basic principles of AI painting, speech synthesis, and video generation that it already knows, and the workflow is restated three times (操作步骤, 使用示例-示例1, 工作流总结 ASCII diagram) with key parameters (1080×1920, 16kHz) repeated many times. Not score 2 because the padding is confined to a few sections rather than pervasive; not score 4 because the redundant restatements are a clear trim target.

3 / 5

Actionability

Steps name real scripts and reference files with defined inputs/outputs, but no actual command invocation with arguments is ever shown (e.g., no `python scripts/generate_character.py ...` line), and the scripts themselves contain TODO pseudocode rather than executable calls. Not score 2 because the per-step I/O contracts and file pointers are concrete; not score 4 because the guidance cannot be executed as written.

3 / 5

Workflow Clarity

The image→audio→video sequence is explicit with per-step inputs, outputs, and optional branches, but validation checkpoints are entirely absent (e.g., no step verifies the generated image is actually ≥1080×1920 or that the wav is 16kHz mono before proceeding). This matches the anchor 'sequence present but checkpoints missing or implicit'; not score 4 because the validation gap is not minor.

3 / 5

Progressive Disclosure

The body is a well-organized overview with one-level-deep references explicitly annotated with "何时读取" (when to read) and scripts linked per step — references/script-templates.md, references/visual-styles.md, and assets/templates/*.md exist in the bundle but are never referenced from the body, leaving them undiscoverable. Not score 5 because of these orphaned bundle files; not score 3 because the structure and signaling are otherwise good.

4 / 5

Total

13

/

20

Passed

Description

70%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 clearly communicates a well-scoped niche capability (9:16 digital-human product-selling videos) with an explicit trigger scenario and good natural keywords. Its main weaknesses are the second-person imperative voice ("用此技能"), which triggers the specificity penalty, and a single trigger phrase with no synonyms or variations.

Suggestions

Rewrite the description in third person (e.g., "自动编排AI绘画、语音合成与视频生成,一键产出9:16竖屏数字人带货成片") instead of the second-person imperative "用此技能…", removing the voice penalty on specificity.

Add one or two natural trigger variations to the when-clause (e.g., "当用户提到带货视频、数字人口播、电商短视频、虚拟主播时") to raise both completeness and trigger-term coverage.

Briefly name the background-music and workflow-orchestration capabilities in the same concrete style as the painting/voice/video actions to close the coverage gap.

DimensionReasoningScore

Specificity

The description lists several concrete actions ("自动编排AI绘画、语音合成与视频生成", "生成9:16竖屏数字人成片", "产出专属形象与专业配音"), which matches the score-4 anchor, but "用此技能一键生成" is second-person imperative voice, which the guidelines penalize by reducing specificity by 1. It is not the score-2 level because the actions named go well beyond domain-only naming, and not score 4/5 because of the voice penalty.

3 / 5

Completeness

Both are answered: the "what" is concrete (auto-orchestrating AI painting, speech synthesis, and video generation into a 9:16 digital-human video) and the "when" is explicit ("在需要制作带货短视频时"). It is not score 5 because the when-clause covers only one trigger scenario and could be more specific with additional concrete trigger phrases; not score 3 because the when is explicitly stated, not merely implied.

4 / 5

Trigger Term Quality

"带货短视频", "电商运营", "内容创作者", "数字人", "9:16竖屏", "多模态视频" are natural phrases users would say when needing this skill, giving good keyword coverage. It falls short of score 5 because common variations and synonyms (e.g., 口播, 虚拟主播, product-video phrasings) are missing.

4 / 5

Distinctiveness Conflict Risk

The named "小省导购员" persona plus the 9:16 vertical digital-human e-commerce-video niche gives a clear, mostly distinct trigger profile. It is not score 5 because there is minor overlap risk with generic short-video or digital-human generation skills.

4 / 5

Total

15

/

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.

Validation — 13 / 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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