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 62656456/ai-film-skills --skill d-official-market-analysisgit clone --depth 1 https://github.com/62656456/ai-film-skillsWrote 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/62656456/ai-film-skills/d-official-market-analysis)<a href="https://agentmods.dev/skills/62656456/ai-film-skills/d-official-market-analysis"><img src="https://agentmods.dev/badge/skills/62656456/ai-film-skills/d-official-market-analysis/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/62656456/ai-film-skills/d-official-market-analysis"><img src="https://agentmods.dev/badge/skills/62656456/ai-film-skills/d-official-market-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00136 | $0.01362 |
| Opus 5 | $0.00068 | $0.00681 |
| Sonnet 5 | $0.00027 | $0.00272 |
| Haiku 4.5 | $0.00014 | $0.00136 |
Grade A, and why
d-official-market-analysis 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 7d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
D｜官方影视市场分析
只回答公开、可验证的证据目前能够证明什么。不要把经验、宣传或合理猜测伪装成官方结论。
启动任务
- 记录执行日期、抓取日期、数据截止日期、统计周期、地区、平台和内容类型。
- 明确用途：选题、立项、创作、投资或平台适配；记录团队规模、预算和制作限制。
- 用户未指定时，采用中国大陆、30天/90天/12个月、微短剧/剧情短视频/漫剧/AI影视/长视频、个人或小团队。
- 先列数据源计划与访问缺口，再抓数据；不要先写结论。
- 读取 evidence-and-sources.md 执行证据分级、来源优先级和交叉验证。
- 读取 data-contract.md 建立原始数据表并统一字段。
- 涉及排名、跨平台比较或项目决策时，读取 analysis-and-report.md。
- 涉及具体平台时，读取 platform-metrics.md，不得把平台热度值等同播放量。
- 接入专属数据服务或 MCP 时，读取 connector-contract.md，只接受保留原始来源与口径的响应。
本包内完整分析能力
本 Skill 按 references/analysis-capability-contract.md 独立完成以下步骤：
- 用本文件的研究边界问句补齐范围；范围已清楚时不要重复追问。
- 按
references/data-contract.md检查缺失、重复、异常、样本偏差、口径冲突和更新时间差。 - 按
references/analysis-and-report.md评估题材、受众、平台、商业价值与进入策略。 - 分析指标变化驱动因素，禁止把相关性写成因果。
- 只有用户要求市场规模时才计算 TAM、SAM、SOM，并明确假设。
- 需要图表时直接生成保留原始指标单位和口径的图表。
- 有重要清洗、排名或计算时创建可重跑的 SQL/Python/Jupyter 工作稿，保留输入、清洗规则、公式、环境和输出。
- 报告完成前按本包质量门复核来源、数据、计算、表格、结论和限制。
- 分析阶段只输出正式报告与独立待批准记录；后续写入是新的独立任务，本 Skill 不连接任何外部语义层。
抓取与证据规则
优先顺序：官方 API或授权连接器 → 官方网页 → 官方 PDF/报告 → 财报公告 → MCP → 用户文件 → 专业平台授权接口 → 可验证公开榜单。打开原始页面或原始文件，不以搜索摘要作为证据。
对 PDF 读取相关完整页面，并核对图表、表格、脚注、统计口径和发布日期。无法确定日期、范围、单位或口径的数据只能进入缺口清单，不能进入正式结论。
关键结论原则上需要两个相互独立的可靠来源。仅有一个官方来源时标记“单一官方来源”。官方与第三方冲突时并列展示，不平均、不强行合并。平台未公开某指标时原样写“官方未公开该项数据”。
同时研究头部和普通/低表现样本。若公开渠道无法识别低表现总体，明确说明样本不可得，不用虚构失败率补表。
分析顺序
- 清洗日期、地区、平台、内容类型、金额、播放单位和标签。
- 分离播放次数、有效播放、观看人数、热度值、观看时长、完播、互动、付费、票房和收入。
- 检查缺失、重复、极端爆款、样本量、更新滞后、口径变更、宣传偏差和来源冲突。
- 分别计算30天即时热点、90天持续性和12个月稳定趋势；三年数据只作历史对照。
- 至少覆盖题材、剧情发动机、人物关系、情绪价值、开场钩子、冲突速度、反转、单集时长、结尾悬念、平台差异、成本、AI适配、饱和度和低播放风险。
- 将结果分为：已被官方或多来源确认、初步趋势、数据不足、暂无官方证据、分析推断。
- 具体项目只能给出七种结论之一：数据充分支持、数据部分支持、数据支持但市场拥挤、有潜力但证据不足、需要调整后再评估、暂不建议立项、暂无官方数据可以判断。
质量门
运行：
python scripts/validate_dataset.py <dataset.csv|dataset.jsonl>
修复所有 error；warning 可保留，但必须在报告局限中解释。随后复核反例、口径、日期、图表和结论强度。
输出
按 analysis-and-report.md 的固定章节和表格生成正式报告与“待批准记录”。待批准记录不是持久写入。输出后停止；用户后续明确批准时，由新的独立写入任务处理，本 Skill 不连接包外语义层。
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 273 B
- references/analysis-and-report.md 3.0 KB
- references/analysis-capability-contract.md 2.2 KB
- references/connector-contract.md 916 B
- references/data-contract.md 1.4 KB
- references/evidence-and-sources.md 2.1 KB
- references/platform-metrics.md 1.1 KB
- scripts/validate_dataset.py 5.2 KB runs code
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.
- 7d ago Changed d42268d782c5
- 12d ago First seen · 69 lines · 136 tokens per session scan A 2c9c38923a8c
d-official-market-analysis is a skill published in the GitHub repository 62656456/ai-film-skills (17 stars, last pushed yesterday), licensed Apache-2.0. It adds 136 tokens to every session and 1,362 once invoked, about $0.0007 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 2026-08-31.
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