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 one-by-one-comparison-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/one-by-one-comparison-skill)<a href="https://agentmods.dev/skills/supreme-ultimate/novel-to-script-team/one-by-one-comparison-skill"><img src="https://agentmods.dev/badge/skills/supreme-ultimate/novel-to-script-team/one-by-one-comparison-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/one-by-one-comparison-skill"><img src="https://agentmods.dev/badge/skills/supreme-ultimate/novel-to-script-team/one-by-one-comparison-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 | $0.00044 | $0.06156 |
| Opus 5 | $0.00022 | $0.03078 |
| Sonnet 5 | $0.00009 | $0.01231 |
| Haiku 4.5 | $0.00004 | $0.00616 |
Grade A, and why
one-by-one-comparison-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 11d 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 — 645 lines — stays where its author put it; the contents beside it link to each section on GitHub.
逐一对比技能
必读
../../references/00-first-principles.md../../references/03-script-writing-standard.md../../references/04-review-gates.md
技能说明
将生成剧本与检索到的Top 5参考剧本逐一对比,每次对比都按照统一的评价标准进行深度分析,给出具体的批判和改进建议。
评价标准(100分制)
1. 节奏控制(20分)
评分细则:
- 场景数量(5分):与参考剧本相差±2个场景内得满分
- 场景时长分配(5分):时长分布合理,有快有慢
- 冲突密度(5分):冲突次数/总字数 ≥ 0.03
- 节奏变化(5分):有明显的节奏起伏
对比方法:
# 统计生成剧本
gen_scenes = count_scenes(generated_script)
gen_conflicts = count_conflicts(generated_script)
gen_conflict_density = gen_conflicts / len(generated_script)
# 统计参考剧本
ref_scenes = count_scenes(reference_script)
ref_conflicts = count_conflicts(reference_script)
ref_conflict_density = ref_conflicts / len(reference_script)
# 评分
scene_score = 5 if abs(gen_scenes - ref_scenes) <= 2 else max(0, 5 - abs(gen_scenes - ref_scenes))
conflict_score = 5 if gen_conflict_density >= 0.03 else gen_conflict_density / 0.03 * 5
2. 对话风格(25分)
评分细则:
- 对话比例(10分):70%+ 得满分,每低5%扣2分
- 句长控制(8分):平均10-14字符得满分
- 短句比例(4分):30-40%得满分
- 对话自然度(3分):人物区分度、口语化程度
对比方法:
# 对话比例
gen_dialogue_ratio = calculate_dialogue_ratio(generated_script)
ref_dialogue_ratio = calculate_dialogue_ratio(reference_script)
dialogue_score = 10 if gen_dialogue_ratio >= 0.7 else gen_dialogue_ratio / 0.7 * 10
# 句长
gen_avg_length = calculate_avg_sentence_length(generated_script)
ref_avg_length = calculate_avg_sentence_length(reference_script)
if 10 <= gen_avg_length <= 14:
length_score = 8
else:
length_score = max(0, 8 - abs(gen_avg_length - 12) * 0.5)
3. 视觉化表达(20分)
评分细则:
- 视觉标记密度(10分):3-5个/100字得满分
- 表情描写(4分):冷笑、嗤笑、冷哼等
- 眼神描写(3分):眼神一冷、眸光一沉等
- 动作描写(3分):嘴角勾起、挑眉、转身等
对比方法:
visual_markers = ['冷笑', '嗤笑', '冷哼', '眼神一冷', '眸光一沉', '嘴角勾起', '挑眉', '转身']
gen_marker_density = count_markers(generated_script, visual_markers) / (len(generated_script) / 100)
ref_marker_density = count_markers(reference_script, visual_markers) / (len(reference_script) / 100)
if 3 <= gen_marker_density <= 5:
marker_score = 10
else:
marker_score = max(0, 10 - abs(gen_marker_density - 4) * 2)
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.
- 11d ago First seen · 645 lines · 44 tokens per session scan A 07f66674213f
one-by-one-comparison-skill is a skill published in the GitHub repository Supreme-Ultimate/novel-to-script-team (165 stars, last pushed 4mo ago), licensed MIT. It adds 44 tokens to every session and 6,156 once invoked, about $0.0002 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.
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