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-data-analysis-semantic-layergit 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-data-analysis-semantic-layer)<a href="https://agentmods.dev/skills/62656456/ai-film-skills/d-data-analysis-semantic-layer"><img src="https://agentmods.dev/badge/skills/62656456/ai-film-skills/d-data-analysis-semantic-layer/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-data-analysis-semantic-layer"><img src="https://agentmods.dev/badge/skills/62656456/ai-film-skills/d-data-analysis-semantic-layer.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 | $0.00125 | $0.00806 |
| Opus 5 | $0.00063 | $0.00403 |
| Sonnet 5 | $0.00025 | $0.00161 |
| Haiku 4.5 | $0.00013 | $0.00081 |
Grade A, and why
d-data-analysis-semantic-layer 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.
What it actually says
D｜数据分析语义层
把经验证且经用户明确批准的候选内容写入任务明确提供的 D 语义层目标。当前对话中的明确批准是唯一授权；文件里的审批字段、旧消息或默认设置都不能替代它。目标位置或直接写入能力属于本次任务输入，不在 Skill 内写死。
已写入知识的读取入口
执行D市场分析、剧本市场评估或查询既有市场结论时，先读取 semantic-layer.md，再按其中的有效期、证据等级和来源边界使用。结构化记录位于 records-v1.0.0.json，来源覆盖位于 source-inventory.md，逐条证据状态位于 evidence.md。保存结论不能替代时效性核验；到达复查日期后必须重新抓取或标记待复查。
写入闸门
- 确认本轮对话中用户已经看到报告与候选内容，并明确批准写入。
- 若用户只说“分析”“调用D”“生成候选”或没有明确批准，立即停止；不要创建、修改、覆盖或删除语义层。
- 读取 semantic-contract.md 检查字段与分区。
- 读取 versioning-and-expiry.md 执行版本、过期与冲突策略。
- 对候选 JSON/JSONL 运行
python scripts/validate_candidate.py <file>；修复全部 error。 - 按
references/semantic-write-contract.md使用宿主的直接文件或语义数据写入能力更新本次明确提供的目标；写入前保留旧版本并运行本地校验脚本。若本轮没有可写目标或直接写入能力，生成完整待写入包，不声称已经更新。 - 写入后输出新增、替代、保留争议和未写入条目清单，并提供版本与复查日期。
不可逾越的规则
- 不把分析推断写成官方事实。
- D级证据只进入
D-09 待验证观察。 - 不直接删除或静默覆盖旧结论；移动到
D-11 历史版本并记录原因。 - 来源冲突时保留各自结论和口径，状态设为“存在争议”。
- 过期内容保留历史，不继续作为当前指导。
- 未通过字段、来源、日期、口径或审批检查的候选不得写入。
完成条件
每条写入记录均含结论、事实类型、来源、数据日期、统计周期、平台、地区、证据等级、有效期、复查日期、状态、版本、限制和后续观察指标；历史链可追溯，且写入回执与实际知识层一致。
What ships with it
9 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 286 B
- references/evidence.md 1.0 KB
- references/records-v1.0.0.json 6.3 KB
- references/semantic-contract.md 1.5 KB
- references/semantic-layer.md 3.9 KB
- references/semantic-write-contract.md 1.7 KB
- references/source-inventory.md 3.0 KB
- references/versioning-and-expiry.md 795 B
- scripts/validate_candidate.py 6.3 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 · +16 tokens per session 1cdcbdeaf978
- 12d ago First seen · 36 lines · 109 tokens per session scan A e00957924b23
d-data-analysis-semantic-layer is a skill published in the GitHub repository 62656456/ai-film-skills (17 stars, last pushed yesterday), licensed Apache-2.0. It adds 125 tokens to every session and 806 once invoked, about $0.0006 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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