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video-frame-extractor

影片与视频编辑、自媒体创作者在需要拆解优秀视频、提取分镜参考或反推提示词时,使用本技能可自动抽取视频关键帧,调用视觉大模型批量分析画面,一键生成结构化描述与创作提示词,轻松搞定视频内容提取与场景分析。

61

Quality

72%

Does it follow best practices?

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SecuritybySnyk

High

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Fix and improve this skill with Tessl

tessl review fix ./skills/video-frame-extractor/video-frame-extractor/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%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 is well-structured and highly actionable, with commands that were verified flag-by-flag against the bundled scripts. Its weaknesses are notable redundancy (repeated Coze Bot benefits, duplicated parameter lists, an example section that re-runs the standard flow), missing validation checkpoints in a batch workflow, and two doc/code mismatches (URL input, resolution default). A hardcoded real-looking Coze API key and Bot ID in 前置准备 is also a security liability that does not belong in a skill file.

Suggestions

Add explicit validation between steps — e.g. after 抽帧 verify frame count > 0 before calling the analysis API, and check analysis.json parses before integrating — and an error-recovery note (what to do when the API returns errors or empty results); batch workflows cannot score above 3 without this.

Remove duplication: keep one Coze Bot benefits statement, one parameter list, and cut 示例1 (it repeats the standard flow verbatim); push the JSON output schema and model-comparison table into references/analysis-guide.md and link to them.

Fix the doc/code mismatches: --input does not support URLs (video_frame_extractor.py has no URL handling) and --resolution defaults to keeping original resolution, not 1080P; also strip the hardcoded Coze Bot ID and API key from SKILL.md and reference environment variables only.

DimensionReasoningScore

Conciseness

The body is mostly efficient — no explanations of concepts Claude already knows — but carries clear redundancy: the Coze Bot advantages are stated twice (「Coze Bot的优势」 in 前置准备 and 「Coze Bot优势」 in 技术说明), the frame/analysis parameters are documented in both 操作步骤 and 可选参数, and 使用示例 示例1 nearly duplicates the 步骤1/步骤2 command blocks verbatim. It fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the 4 anchor, because entire repeated sections (not just a few lines) could be cut.

3 / 5

Actionability

Guidance is concrete and mostly copy-paste ready: full bash invocations with every flag, verified against the scripts' actual argparse definitions (--input/--interval/--max_frames/--start_time/--end_time/--resolution in video_frame_extractor.py:153-159; --image/--image_dir/--prompt/--output in coze_bot_client.py:287-295; --detail/--batch_size in visual_analyzer.py:253-267), plus a concrete JSON output schema. Not a 5 because two documented claims don't match the code: the body says --input accepts a URL and that --resolution defaults to 1080P, but video_frame_extractor.py contains no URL handling and defaults to keeping original resolution — executable commands with minor factual gaps.

4 / 5

Workflow Clarity

The three-step sequence (抽帧 → 视觉分析 → 结果整合) is clearly listed with two alternative analysis paths, but there are no validation checkpoints between steps (e.g., verify frames were actually produced before batch analysis, check the analysis JSON is well-formed), and no error-recovery loop. This is a batch-operation skill, and the rubric explicitly caps workflow_clarity at 3 when batch workflows lack validation — the soft note 「建议先用小规模抽帧测试」 is advice, not a checkpoint. Not a 2, because the sequence itself is coherent, well-ordered, and parameterized.

3 / 5

Progressive Disclosure

Structure is good: SKILL.md acts as an overview with a dedicated 资源索引 section linking the three real scripts and the one real reference (references/analysis-guide.md, confirmed present), and the reference is one level deep and clearly signaled. Minor gaps keep it below 5: some material inlined in the body (the full JSON output-format block, the model-comparison detail, the Coze Bot marketing) belongs in references/analysis-guide.md, and the 技术说明 section repeats overview-level information instead of deferring to the reference.

4 / 5

Total

14

/

20

Passed

Description

83%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.

A strong description: it names a concrete multi-step capability, identifies its creator audience, and includes an explicit 'when' trigger clause with natural Chinese keywords. Its weaknesses are marketing padding (一键, 轻松搞定) and slightly thin synonym coverage (no file extensions or alternate phrasings).

Suggestions

Remove marketing fluff (一键, 轻松搞定) — guideline explicitly penalizes vague buzzwords and over-claims.

Add natural trigger synonyms and formats, e.g. 抽帧, 视频截图, .mp4/.mov files, to reach comprehensive keyword coverage.

State the output explicitly as a noun deliverable (e.g. 生成 JSON 格式的分镜分析报告) so the 'what' ends at a concrete artifact.

DimensionReasoningScore

Specificity

Lists several concrete actions — 「自动抽取视频关键帧」 (extract video key frames), 「调用视觉大模型批量分析画面」 (batch-analyze frames with a vision LLM), 「生成结构化描述与创作提示词」 (generate structured descriptions and creation prompts), 「视频内容提取与场景分析」 (video content extraction and scene analysis) — giving broad coverage, but marketing fluff like 「一键」 (one-click) and 「轻松搞定」 (easily handle) pads rather than informs. Not a 5 because the fluff phrases dilute an otherwise comprehensive action list; not a 3 because far more than 1-2 actions are named concretely.

4 / 5

Completeness

Both halves are explicit: the 'what' is the concrete pipeline (抽帧 → 批量分析 → 生成结构化描述与提示词) and the 'when' is a dedicated trigger clause 「在需要拆解优秀视频、提取分镜参考或反推提示词时」 with concrete trigger phrases, matching the 5 anchor pattern 'Extract... Use when... or when the user mentions...'. Not a 4, because the when-clause is explicit and enumerates specific triggering needs rather than being generic.

5 / 5

Trigger Term Quality

Natural phrases a Chinese-speaking creator would actually say are well covered: 「拆解优秀视频」 (break down great videos), 「提取分镜参考」 (extract storyboard references), 「反推提示词」 (reverse-engineer prompts), plus the audience term 「自媒体创作者」. A few natural variations are missing — no file extensions (.mp4/.mov), no plain synonyms like 抽帧/截图, and no English equivalents — so it falls just short of the comprehensive 5 anchor.

4 / 5

Distinctiveness Conflict Risk

The niche — video frame extraction plus vision-model reverse-prompting for content creators — is distinct, with trigger phrases (反推提示词, 分镜参考) unlikely to fire for generic video-editing or image skills. Minor overlap risk remains with general image-analysis or video-summarization skills, so it sits at 'mostly distinct' rather than the fully clear-niche 5.

4 / 5

Total

17

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
anbeime/skill
Reviewed

Table of Contents

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