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video-forge

视频制作全链路:素材入库 → 剧本冻结 → 全局配音 → 对齐 → 渲染 → 审查 → 交付。 Use when: 已明确要制作视频、做 showcase、做教程视频、录屏剪辑、video review、节奏审查。 Not for: Agent 产品宣传片仍未锁定主角、观众信念与导演语法(先用 agent-product-promo-director); 只找时效性外部参考(用 deep-research);纯代码开发(用 worktree/tdd)、纯文档写作(直接写)、PPT(用 ppt-forge)。 Output: schema 驱动的视频成片 + operator 创意验收 + 风险匹配的技术审查 + 可发布。

68

Quality

85%

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

77%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 a well-engineered production runbook: sequenced routing with owners, executable commands, quantified thresholds, and hard validation gates with failure paths. Its main weaknesses are a dead primary reference (refs/continuity.md is absent from the bundle), inline time-sensitive history, and a couple of abstract spots in the AI-generation and concat workflows.

Suggestions

Ship the missing refs/continuity.md (or fix its path) — the body links to it three times for the continuity checklist and downgrade paths, but the file does not exist in the bundle, breaking the skill's only deep reference.

Show the segments.txt format next to the ffmpeg concat commands (or point to where it is generated) so the copy-paste example is fully executable.

Consolidate the dated 失败史 blockquotes, convergence notes (2026-04-05 / 2026-08-25), and version/license table into a short history or reference section so the main flow stays free of time-sensitive detail.

DimensionReasoningScore

Conciseness

The body is dense and table-driven, assumes competence (no explaining what FFmpeg or TTS is), and nearly every line is operative guidance. However, dated material — "技术收敛纪要: 2026-04-05", three 2026-08-25 失败史 blockquotes, issue #1092, and a version/license table — is inlined rather than confined to a history/deprecated section, which the guidelines penalize. This lands above 'mostly efficient' (3) but short of 'every token earns its place' (5).

4 / 5

Actionability

Most guidance is executable: copy-paste ffmpeg commands (concat and compression with CRF/AAC settings), exact thresholds (>200ms timestamp drift, ≥0.7x speed floor, 30% fill limit), concrete directory layouts, and complete rich-block JSON examples. Minor gaps keep it from 5: the `segments.txt` format consumed by the concat commands is never shown, and the AI-generation flow stays abstract ('每段独立 submit → poll → 获取 resultUrl') without the actual tool calls.

4 / 5

Workflow Clarity

The 场景路由 table lays out a clear A→G sequence with an R patch loop for error recovery, and each risky stage has an explicit validation gate: 审查 Gate F1/F2/F3 with P1/P2 severity tables, the Motion Evidence Gate with a hard BLOCK condition, and the Seam Review Gate's 5-question checklist with a defined 降级路径 on failure. This matches the top anchor: clear sequence, explicit validation, feedback loops, and checklists.

5 / 5

Progressive Disclosure

Section structure is strong (gates, paths, routing, checklists, tech stack) and the main external reference is well signaled with its contents named ('详细的事前预测清单、降级路径和 15s 典型失败案例见 refs/continuity.md'), but that referenced file does not exist anywhere in the skill bundle — following the link three times leads nowhere, and the other two links (../.cat-cafe-shared-refs/narrative-clarity.md, ../../docs/features/F138-video-studio.md) point outside the bundle and cannot be verified. Good structure with a broken navigation target fits the 4 anchor; it is not a 5 because easy navigation actually breaks on the primary reference.

4 / 5

Total

17

/

20

Passed

Description

92%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 states the full production pipeline, gives concrete natural-language triggers, defines the output, and explicitly fences off adjacent skills. The only weakness is a handful of missing trigger synonyms (配音, 剪辑) that keeps trigger coverage just short of comprehensive.

DimensionReasoningScore

Specificity

The description enumerates the complete pipeline concretely — "素材入库 → 剧本冻结 → 全局配音 → 对齐 → 渲染 → 审查 → 交付" — and specifies the deliverable ("schema 驱动的视频成片 + ... 可发布"), giving comprehensive, multi-action coverage of the domain. It is not merely naming the domain; every stage is a distinct concrete action, matching the top anchor rather than the 'several specific actions; minor gaps' level below.

5 / 5

Completeness

It explicitly answers both questions: what (the end-to-end pipeline plus an Output clause) and when (a concrete "Use when:" trigger list), and goes further with an explicit "Not for:" boundary. This clearly matches the top anchor of 'clearly and explicitly answers both what AND when with concrete trigger phrases'.

5 / 5

Trigger Term Quality

"Use when: 已明确要制作视频、做 showcase、做教程视频、录屏剪辑、video review、节奏审查" covers good natural phrasings a user would actually say, but common variants like standalone "配音", "剪辑视频", or "渲染" as triggers are absent. Good coverage with a few natural terms missing fits the 4 anchor; it lacks the exhaustive synonym/extension coverage of a 5.

4 / 5

Distinctiveness Conflict Risk

The "Not for" clause actively disambiguates against named neighboring skills (agent-product-promo-director, deep-research, worktree/tdd, ppt-forge), carving a clear niche with distinct triggers and minimal conflict risk. Voice is third-person throughout, with no first/second-person penalty applying.

5 / 5

Total

19

/

20

Passed

Validation

87%

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

Validation14 / 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: 3 missing, 2 suspicious

Warning

Total

14

/

16

Passed

Repository
zts212653/clowder-ai
Reviewed

Table of Contents

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