Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Teamnpx agentmods add skills/aaaaqwq/agi-super-team/company-investment-researchWrote 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/aaaaqwq/agi-super-team/company-investment-research)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/company-investment-research"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/company-investment-research/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/aaaaqwq/agi-super-team/company-investment-research"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/company-investment-research.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 | $0.00109 | $0.04582 |
| Opus 5 | $0.00055 | $0.02291 |
| Sonnet 5 | $0.00022 | $0.00916 |
| Haiku 4.5 | $0.00011 | $0.00458 |
Grade A, and why
company-investment-research 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 — 489 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company Investment Research / 公司投资研究框架
This skill provides a systematic framework for conducting comprehensive investment research and due diligence on companies. It structures analysis across 10 critical dimensions to support informed investment decisions.
本技能为公司基本面研究提供一套结构化投研框架，覆盖 10 个关键维度，帮助你从零开始梳理一家公司的商业模式、竞争力、增长与估值，并最终形成一份有逻辑的投资结论。
When to Use / 适用场景
Use this skill when you need to:
- Evaluate a company as a potential investment (public or late-stage private)
- Understand a company's competitive advantages and moat vs peers
- Produce a structured investment memo instead of scattered notes
- Compare two or more companies in the same sector on a consistent framework
适合在以下场景使用：
- 对某家公司做系统性投研/估值评估（上市公司或准上市公司）
- 想明白它相对于同行的护城河与竞争地位
- 需要输出一份结构清晰的投研报告/投资备忘录
- 在同一行业内对比多家公司，希望有统一的分析模板
Quick Usage Examples / 快速使用示例
1. Single-company deep dive / 单公司深度研究
"Analyze NVIDIA (NVDA) as an investment using the company-investment-research framework. Follow all 10 dimensions and end with a clear BUY/HOLD/SELL view, including key risks."
「请基于
company-investment-research投研框架，系统分析英伟达（NVIDIA, NVDA）的投资价值，按 10 个维度展开，最后给出 BUY/HOLD/SELL 判断，并列出关键风险。」
2. Compare two companies in the same sector / 同行业公司对比
"Using the company-investment-research skill, compare NVIDIA vs AMD as AI infrastructure investments. Highlight differences in moat, growth drivers, and valuation, then state which one looks more attractive on a 3–5 year horizon and why."
「使用该投研框架对比分析 NVIDIA 与 AMD 作为 AI 基础设施投资标的的优劣，从护城河、成长驱动、估值三方面重点展开，并给出未来 3–5 年哪个更具吸引力及原因。」
3. Rapid pre-screening / 快速预筛选
"Run a lightweight version of the company-investment-research framework on Snowflake. Focus on competitive positioning, growth drivers, and valuation to decide whether it deserves full deep-dive research."
「对 Snowflake 做一版简化版投研：重点看竞争地位、成长驱动和估值，判断是否值得投入时间做完整深度研究。」
4. Memo generation for internal discussion / 生成内部讨论用 Memo
"Create a 2–3 page investment memo for Tesla using the company-investment-research structure. The target audience is an investment committee; keep language concise but include key numbers and scenarios (base/bull/bear)."
What ships with it
4 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.
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 First seen · 489 lines · 109 tokens per session scan A 2fc35a8c6c5b
company-investment-research is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (92 stars, last pushed yesterday), licensed MIT. It adds 109 tokens to every session and 4,582 once invoked, about $0.0005 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 2026-09-05.
Other skills, from other repositories
modeling-unit-economics
Use when modeling unit economics is required during product work, especially when the result must be traceable, independently reviewable, and safe to hand to another agent.
generated_skill_1775724185480
Parse XBRL financial documents and extract key taxonomy elements into structured JSON format.
mission-control
Interact with Mission Control — AI agent orchestration dashboard. Use when registering agents, managing tasks, syncing skills, or querying agent/task status via MC APIs.
blocknative-openapi-skill
Operate Blocknative gas intelligence APIs through UXC with a curated OpenAPI schema, API-key auth, and read-first guardrails.
a-share-swing-sniper
A rule-based method for selecting Chinese stocks near the market close and deciding whether to trade or sell on the following day.
ai-financial-analysis-stock-research
AI financial analysis and stock research powered by CellCog. Stock analysis, valuation models, portfolio optimization, earnings breakdowns, investment research, financial statements, tax planning, and DCF modeling. Wall Street-grade deliverables — interactive dashboards, PDF rep…