Borrowing it
Nothing to install: this file belongs to agno-agi/investment-team. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/agno-agi/investment-team/main/CLAUDE.mdgit clone --depth 1 https://github.com/agno-agi/investment-teamWrote 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/instructions/agno-agi/investment-team/claude-md)<a href="https://agentmods.dev/instructions/agno-agi/investment-team/claude-md"><img src="https://agentmods.dev/badge/instructions/agno-agi/investment-team/claude-md/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/instructions/agno-agi/investment-team/claude-md"><img src="https://agentmods.dev/badge/instructions/agno-agi/investment-team/claude-md.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 | $0.02054 | $0.02054 |
| Opus 5.5 | $0.00822 | $0.00822 |
| Sonnet 5.5 | $0.00411 | $0.00411 |
| Haiku 4.5 | $0.00205 | $0.00205 |
Grade A, and why
investment-team CLAUDE.md 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 2mo 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides context for Claude Code when working with this repository.
Project Overview
Agentic Investment Team — A multi-agent system built with Agno that simulates a professional investment team deploying $10M into public equities. Demonstrates 5 multi-agent architectures, three-layer knowledge, and institutional learning.
Architecture
AgentOS (app/main.py)
├── Agents (7 reusable specialists)
│ ├── Market Analyst — macro trends, sector analysis, news (Exa + YFinance)
│ ├── Financial Analyst — fundamentals, valuation, financials (YFinance)
│ ├── Technical Analyst — price action, momentum, entry/exit (YFinance)
│ ├── Risk Officer — downside scenarios, portfolio exposure (YFinance)
│ ├── Knowledge Agent — research library (RAG) + memo archive (FileTools)
│ ├── Memo Writer — synthesizes analysis into formal memos (FileTools)
│ └── Committee Chair — final decisions, capital allocation (Gemini 3.1 Pro)
│
├── Teams (4 architectures)
│ ├── Coordinate Team — Chair orchestrates analysts dynamically
│ ├── Route Team — routes questions to the right specialist
│ ├── Broadcast Team — all analysts evaluate simultaneously
│ └── Task Team — autonomous task decomposition
│
├── Workflows (1 architecture)
│ └── Investment Workflow — deterministic pipeline with parallel steps
│
├── Three-Layer Knowledge
│ ├── Layer 1: Static Context — mandate, risk policy, process (always in prompt)
│ ├── Layer 2: Research Library — company profiles, sector analysis (PgVector RAG)
│ └── Layer 3: Memo Archive — past investment memos (FileTools)
│
└── Institutional Learning — patterns, corrections, insights (LearningMachine)
All specialist agents use:
- Gemini 3 Flash model (
gemini-3-flash-preview) - PostgreSQL database (pgvector) for persistence
- Committee context (Layer 1) in system prompt
- Shared knowledge base (Layer 2) for RAG
- Shared learnings (institutional learning)
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.
- 2mo ago First seen · 245 lines · 2,054 tokens per session scan A e8726ef34f0f
investment-team CLAUDE.md is an instructions file published in the GitHub repository agno-agi/investment-team (172 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 2,054 tokens to every session, about $0.0082 per session on Opus 5.5. 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 2026-08-30.
investment-team CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| investment-team CLAUDE.mdagno-agi/investment-team | Instructions file | 172 | 8mo ago | Apache-2.0 | A | 2,054 | Claude Code |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |