Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Supreme-Ultimate/novel-to-script-team --skill script-analysis-review-skillgit clone --depth 1 https://github.com/Supreme-Ultimate/novel-to-script-teamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/supreme-ultimate/novel-to-script-team/script-analysis-review-skill)<a href="https://agentmods.dev/skills/supreme-ultimate/novel-to-script-team/script-analysis-review-skill"><img src="https://agentmods.dev/badge/skills/supreme-ultimate/novel-to-script-team/script-analysis-review-skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/supreme-ultimate/novel-to-script-team/script-analysis-review-skill"><img src="https://agentmods.dev/badge/skills/supreme-ultimate/novel-to-script-team/script-analysis-review-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.02201 |
| Opus 5 | $0.00026 | $0.01100 |
| Sonnet 5 | $0.00011 | $0.00440 |
| Haiku 4.5 | $0.00005 | $0.00220 |
Grade A, and why
script-analysis-review-skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
导演分析审核技能
必读
../../references/00-first-principles.md../../references/04-review-gates.md../../references/05-compliance-boundaries.md
技能说明
审核导演在阶段一的分析产出,包括导演讲戏本、人物清单和场景清单。这是导演的自审环节,要求导演以批判性视角审视自己的产出,通过逐行比对原始剧本、脑内预演画面效果、逐项评分的方式,确保讲戏质量真正达标,而非仅确认格式完整。
审核对象
outputs/<集数>/01-director-analysis.md
审核流程
第一步:建立整体理解
- 读取原始剧本(
script/<集数>-xxx.md) - 读取待审核的导演分析产出
- 明确审核立场:你的任务是找出问题,而非确认通过
第二步:逐行比对
- 逐行读原始剧本,逐个读讲戏本中的剧情点
- 找出剧本中有但讲戏本中被弱化、遗漏或表达偏离的地方
- 记录所有偏差,无论大小
第三步:脑内预演
- 对每个剧情点,想象一个导演拿着这段讲戏去指导拍摄,画面会是什么样?
- 判断:讲戏中的描述是否足够具体,让拍摄团队不需要再问"你到底要什么效果"?
- 判断:讲戏描述拍出来的画面,是否准确传达了原始剧本的情绪和意图?
第四步:逐项评分
按照下方验收清单,对每个剧情点评分(1-10),并对人物清单、场景清单整体评分
第五步:输出结果
- 计算所有剧情点的平均分
- 平均分 ≥ 8 且无单项低于 6 → PASS
- 平均分 < 8 或任一单项低于 6 → FAIL + 问题清单
验收清单
剧情拆解(整体评估,不单独评分)
- □ 逐行比对:原始剧本中的每一个场景转换、每一句台词、每一个动作指示,是否都有对应的剧情点覆盖?列出遗漏项。
- □ 叙事完整性:每个剧情点是否是一个完整的叙事段落?是否有段落被不必要地拆散(如蒙太奇序列被拆成多个独立剧情点)?
- □ 合并检查:服务于同一叙事目的的相邻段落是否应合并?指出具体是哪些。
- □ 拆分检查:是否有段落包含两个独立的叙事目的,应拆分?指出具体是哪个。
- □ 时长合理性:每个段落的内容密度与建议时长(4-15s)是否匹配?
导演讲戏质量(每个剧情点单独评分 1-10)
评分维度:
- □ 画面感(脑内预演):闭上眼读这段讲戏,你脑中能浮现出清晰、连贯的画面吗?如果有模糊的地方,具体是哪句话不够清晰?
- □ 动作链完整性:人物从 A 状态到 B 状态的物理动作是否有完整的过渡?是否存在"跳跃"——上一秒在做 X,下一秒突然在做 Y,中间缺少衔接?
- □ 节拍密度:每个连续镜头内的节拍密度是否合理(1 拍 ≈ 2.5 秒)?多镜头段落的节拍是否合理分配在各镜头中?
- □ 头尾安全区:Seedance 生成的前 0.5s 和后 0.5s 是否避开了关键内容?
- □ 镜头可执行性:单镜头段落——镜头运动是否能连续实现?多镜头段落——每组镜头各自是否可执行,切换方式是否清晰?
- □ 光影具体性:光线描述是否具体到方向、色温、强度?"光线柔和"不够具体,"灰蓝色的清晨微光从左侧单窗斜射入室内"才够具体。
- □ 情绪传达:这段讲戏是否准确传达了原始剧本中该场景的核心情绪?如果有偏差,指出原始剧本的情绪是什么,讲戏传达的情绪变成了什么。
- □ 衔接自然:与前一个和后一个剧情点之间,画面空间、时间、情绪是否能自然过渡?如果不能,指出断裂点。
评分标准:
- 9-10:讲戏精准,脑内预演画面清晰完整,无需修改
- 7-8:基本到位,但有 1-2 处可以更具体或更准确的地方
- 5-6:有明显的模糊、跳跃或偏离,需要修改
- 1-4:讲戏严重不足,无法指导拍摄
人物清单(整体评分 1-10)
- □ 筛选合理性:人物清单是否只包含需要参考图的角色?检查以下两点:
- 应列入的角色是否遗漏:本集出现 2+ 个剧情点的人物、预计后续集数复现的人物,是否都在清单中?
- 应排除的角色是否误入:本集只出现 1 次且不会再出场的群演/一次性配角(如路人、围观群众),是否被错误地列入了清单?
- □ 群演描述充分性:不在清单中的群演/一次性配角,在讲戏本的导演阐述中是否有足够具体的外观描述?分镜师能否据此直接在提示词中用文字描写?(如"尖脸眯缝眼的白袍学员"够具体,"一个路人"不够具体)
- □ 外观可设计性:对清单中每个人物的外观关键词,判断服化道设计师是否能据此画出一个明确的形象?哪些人物的描述过于模糊?
- □ 一致性:人物清单中的外观描述与讲戏本中对同一人物的描述是否矛盾?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 140 lines · 53 tokens per session scan A f4ed47cc7704
script-analysis-review-skill is a skill published in the GitHub repository Supreme-Ultimate/novel-to-script-team (163 stars, last pushed 4mo ago), licensed MIT. It adds 53 tokens to every session and 2,201 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
webgl-holographic-foil
A self-contained WebGL2 hero: thin-film interference over a crushed-foil surface whose palette shifts with the viewing angle; move the cursor to tilt the film.
general-video
Author or edit a custom HyperFrames composition when no specialized workflow fits, or when BRIEF.md sets flow: companion. Use for longer or multi-scene pieces, brand and sizzle reels, montages, static loops, static title cards, footage remixes, and freeform builds. Use motion-graphics instead for a short unnarrated…
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
html-ppt-taste-brutalist
16:9 HTML deck in tactical-telemetry / CRT-terminal taste. Deactivated-CRT charcoal slides, white-phosphor monospace, hazard-red accent, scanline overlay, ASCII syntax, density over decoration. Distilled from Leonxlnx/taste-skill brutalist-skill (Tactical Telemetry mode).
diagnostic-stem-delivery
Audio production with diagnostic analysis, timecode parsing from documents, and verified export workflow.
chengfeng-check-updates
An environment manager for a video-editing system. It checks whether its skills and runtime—the software needed to run them—are installed and compatible.