investor-feedback-tracking

investor-feedback-tracking is a skill for Claude Code from vivy-yi/finance-skills. It costs 89 tokens per session (1,982 once invoked), scanned A, original, MIT.

A workflow for collecting and organising feedback from investors, analysts, roadshows, earnings calls, research reports, and other IR channels. It groups concerns and shows recurring themes and sentiment.

In plain words
What is it for?
It helps build feedback records, classify questions, identify frequently raised topics, analyse investor attitudes, and prepare reports for management.
Why use it?
Investor comments are often scattered across meetings, emails, and reports. This turns them into a structured view that management can review and act on.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions CLAUDE.md.

Good fit It helps build feedback records, classify questions, identify frequently raised topics, analyse investor attitudes, and prepare reports for management.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vivy-yi/finance-skills/investor-feedback-tracking
Install

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.

Any agent
npx skills add vivy-yi/finance-skills --skill investor-feedback-tracking
Clone the repo
git clone --depth 1 https://github.com/vivy-yi/finance-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for investor-feedback-tracking

README.md
[![agentmods](https://agentmods.dev/badge/skills/vivy-yi/finance-skills/investor-feedback-tracking/github.svg)](https://agentmods.dev/skills/vivy-yi/finance-skills/investor-feedback-tracking)
Your own site
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/investor-feedback-tracking"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/investor-feedback-tracking/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.

agentmods 80×15 button for investor-feedback-tracking

Your own site · 80×15
<a href="https://agentmods.dev/skills/vivy-yi/finance-skills/investor-feedback-tracking"><img src="https://agentmods.dev/badge/skills/vivy-yi/finance-skills/investor-feedback-tracking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,982 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00089 $0.01982
Opus 5 $0.00044 $0.00991
Sonnet 5 $0.00018 $0.00396
Haiku 4.5 $0.00009 $0.00198

Measured 9d ago against content hash 47ce1573774e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

investor-feedback-tracking 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.

finance-skills/skills/investor-relations/skills/investor-feedback-tracking/SKILL.md · 232 lines

How it starts

The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.

加载上下文

首次使用时: 读取 ../../CLAUDE.md 获取场景级配置(投资者分类/反馈分析框架/历史基准)。


/investor-feedback-tracking — 投资者反馈追踪

Examples

→ 示例:用户说"这次路演回来,投资人对我们毛利率下降很担心,需要如何回应",系统应调用本技能,分析路演反馈并制定应对策略。

→ 示例:用户说"帮我们建一个投资者反馈数据库,分析这段时间被问最多的问题",系统应调用本技能,建立投资者反馈分析和问题归类。

→ 示例:用户说"有几家机构投资者发了负面报告,需要持续跟踪后续反应",系统应调用本技能,跟踪和分析负面 IR 事件后续影响。

第一步:反馈收集

反馈来源汇总:

□ 路演/会议反馈([X] 次):
  → 投资者会议:[X] 家
  → 机构投资者:[X] 家]
  → 分析师:[X] 位

□ 业绩发布会反馈:
  → 电话会议参会:[X] 家机构
  → Q&A 问题数:[X] 个

□ 日常 IR 反馈:
  → 投资者问询:[X] 个
  → 主要渠道:[邮件/电话/线下]

□ 其他来源:
  → 券商研究报告反馈:[X] 篇
  → 财经媒体评论:[X] 篇

原始反馈记录:

□ 反馈收集完整性:[✅ 完整 / ⚠️ 部分缺失]
□ 收集时间范围:[YYYY-MM-DD 至 YYYY-MM-DD]
□ 参与投资者数量:[X] 家

第二步:反馈分类汇总

反馈分类框架:

□ 业务类反馈:
  → 产品/服务相关:[X] 条
  → 市场/竞争相关:[X] 条
  → 战略相关:[X] 条

□ 财务类反馈:
  → 业绩表现相关:[X] 条
  → 指引/预期相关:[X] 条
  → 资本结构相关:[X] 条

□ ESG/治理类反馈:
  → ESG 相关:[X] 条
  → 公司治理相关:[X] 条

□ IR/披露类反馈:
  → 披露质量相关:[X] 条
  → IR 沟通相关:[X] 条

高频问题统计:

□ 最常被问及的问题(Top 5):
| # | 问题主题 | 被问次数 | 投资者类型 |
|---|---------|---------|-----------|
| 1 | [主题] | [X] 次 | [机构/散户/分析师] |
| 2 | [主题] | [X] 次 | ... |
| 3 | [主题] | [X] 次 | ... |

第三步:情绪与态度分析

投资者情绪评估:

□ 整体情绪分布:
  → 正面/支持:[X]%([X] 条反馈)
  → 中性/观望:[X]%([X] 条反馈)
  → 负面/关切:[X]%([X] 条反馈)

□ 情绪变化趋势(vs 上期):
  → 整体情绪:[改善/持平/恶化]
  → 主要变化点:[描述]

特定议题情绪:

□ [议题,如"毛利率趋势"]:
  → 情绪:[正面/中性/负面]
  → 核心关切:[描述]
  → 被问频率:[X] 次

□ [议题,如"下期指引"]:
  → 情绪:[...]

投资者担忧分析:

□ Top 3 投资者担忧:
  → 担忧 1:[描述] — 涉及投资者数 [X] 家
  → 担忧 2:[描述] — 涉及投资者数 [X] 家
  → 担忧 3:[描述] — 涉及投资者数 [X] 家

第四步:行动项追踪

历史行动项进展:

□ 上期反馈行动项:
| 行动项 | 责任人 | 原计划完成 | 进展 | 状态 |
|--------|--------|-----------|------|------|
| [行动1] | [姓名] | [日期] | [描述] | [✅/⚠️/🔴] |
| [行动2] | [姓名] | [日期] | [描述] | [✅/⚠️/🔴] |

新行动项建议:

□ 本期建议行动项:
  → 行动 1:[描述] — 建议负责人 [部门] — 优先级 [高/中/低]
  → 行动 2:[描述] — 建议负责人 [部门] — 优先级 [高/中/低]

第五步:上报管理层

管理层简报格式:

□ 执行摘要([X] 句话):
  "[核心发现,如'投资者对下期指引普遍关注,多数担忧毛利率压力']"

□ 关键数据点:
  → 参与投资者:[X] 家
  → 正面反馈占比:[X]%
  → 高频问题:[主题 1、主题 2]

□ 须管理层关注:
  → 投资者主要担忧:[描述]
  → 潜在风险信号:[描述]
  → 建议回应策略:[描述]

Read the full file on GitHub · 232 lines

Changes

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.

  1. 9d ago First seen · 232 lines · 89 tokens per session scan A 47ce1573774e

Subscribe to this mod's changes

investor-feedback-tracking is a skill published in the GitHub repository vivy-yi/finance-skills (29 stars, last pushed 3mo ago), licensed MIT. It adds 89 tokens to every session and 1,982 once invoked, about $0.0004 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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