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 vivy-yi/finance-skills --skill investor-relations-mastergit clone --depth 1 https://github.com/vivy-yi/finance-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/vivy-yi/finance-skills/investor-relations-master)<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/investor-relations-master"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/investor-relations-master/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/vivy-yi/finance-skills/investor-relations-master"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/investor-relations-master.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 | $0.00129 | $0.01636 |
| Opus 5 | $0.00064 | $0.00818 |
| Sonnet 5 | $0.00026 | $0.00327 |
| Haiku 4.5 | $0.00013 | $0.00164 |
Grade A, and why
investor-relations-master 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
投资者关系 Master Skill
能力描述
这是什么: 投资者关系场景的主技能,整合信息披露管理、分析师关系维护、投资者沟通与重大事件 IR 应对,为 CFO 和 IR 团队提供完整的投资者关系管理能力。
解决什么问题: 当用户需要管理信息披露、处理分析师问询、组织业绩路演、或应对投资者重大关切时,调用本技能获取完整的 IR 管理方案。
边界: 不涉及具体业务运营,专注于投资者沟通与信息披露管理。
触发条件
满足以下任一场景时调用本技能:
- 用户提到"投资者关系"、"IR"、"分析师"、"路演"
- 用户提到"信息披露"、"机构投资者"、"股东"
- 用户要求"管理 IR"、"组织路演"、"处理投资者问询"
Examples
→ 示例:用户说"下个月有业绩路演,帮我准备一下Q&A里可能被问到的财务问题",系统应调用本技能,进入投资者沟通路径。
→ 示例:用户说"有分析师发了份负面报告,我们需要准备一个官方回应",系统应调用本技能,进入分析师关系路径。
→ 示例:用户说"有重大收购要公告,需要确保信息披露合规,不出现选择性披露",系统应调用本技能,进入信息披露管理路径。
输入
□ 场景类型:[信息披露/分析师关系/投资者沟通/事件应对]
□ 活动类型:[业绩发布/路演/NDR/股东大会]
□ 涉及对象:[机构投资者/分析师/散户/监管]
□ 时间:[YYYY-MM-DD]
□ 已获取信息:[描述]
原子能力调用顺序
路径一:信息披露管理
Step 1 → [披露合规检查](disclosure-compliance-check)
→ 输出:合规清单
Step 2 → [披露内容准备](disclosure-content-preparation)
→ 输出:披露草稿
Step 3 → [披露审批](disclosure-approval)
→ 输出:审批记录
Step 4 → [披露发布](disclosure-publishing)
→ 输出:发布确认
路径二:业绩路演
Step 1 → [路演材料准备](roadshow-material-preparation)
→ 输出:路演 Deck
Step 2 → [投资者名单确认](investor-list-confirmation)
→ 输出:投资者清单
Step 3 → [路演执行](roadshow-execution)
→ 输出:路演纪要
Step 4 → [路演后跟进](roadshow-follow-up)
→ 输出:跟进清单
路径三:投资者关切处理
Step 1 → [投资者问询分类](investor-query-classification)
→ 输出:问询分类
Step 2 → [敏感信息评估](sensitive-information-assessment)
→ 输出:披露评估
Step 3 → [问询回应准备](query-response-preparation)
→ 输出:回复材料
Step 4 → [问询记录归档](query-record-filing)
→ 输出:归档记录
路径四:重大事件应对
Step 1 → [事件IR影响评估](event-ir-impact-assessment)
→ 输出:影响评估
Step 2 → [IR应对方案](ir-response-plan)
→ 输出:应对方案
Step 3 → [沟通材料准备](communication-material-preparation)
→ 输出:沟通材料
Step 4 → 汇总输出事件 IR 应对报告
输出格式
投资者关系管理报告
====================
【投资者结构】
□ 机构投资者:[X] 家 — 持股 [X]%
□ 公募基金:[X] 家 — 持股 [X]%
□ 散户:[X]% — 持股 [X]%
【分析师覆盖】
□ 覆盖分析师:[X] 家
□ 评级分布:买入 [X]/持有 [X]/卖出 [X]
□ 目标价区间:[X] 元 - [X] 元
【近期 IR 活动】
| 活动 | 日期 | 对象 | 反馈 |
|---|---|---|---|
| [路演/业绩发布] | [日期] | [投资者类型] | [反馈摘要] |
【信息披露】(近期)
□ 披露数量:[X] 份
□ 类型:定期报告 [X]/临时公告 [X]
【投资者关切】(如有)
□ 关切事项:[描述]
□ 关切程度:[高/中/低]
□ 处理措施:[描述]
□ 跟进状态:[处理中/已解决]
【升级事项】(如有)
□ 疑似泄露事件:[描述] — 状态 [处理中]
□ 负面分析师报告:[描述] — 应对 [已回应/不回应]
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 · 185 lines · 129 tokens per session scan A 819c6afbdc50
investor-relations-master is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 129 tokens to every session and 1,636 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 2026-09-03.
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