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.
git clone --depth 1 https://github.com/brainbytes-dev/everything-claude-financeWrote 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/commands/brainbytes-dev/everything-claude-finance/investment-memo)<a href="https://agentmods.dev/commands/brainbytes-dev/everything-claude-finance/investment-memo"><img src="https://agentmods.dev/badge/commands/brainbytes-dev/everything-claude-finance/investment-memo/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/commands/brainbytes-dev/everything-claude-finance/investment-memo"><img src="https://agentmods.dev/badge/commands/brainbytes-dev/everything-claude-finance/investment-memo.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 | $0.00007 | $0.01664 |
| Opus 5 | $0.00003 | $0.00832 |
| Sonnet 5 | $0.00001 | $0.00333 |
| Haiku 4.5 | $0.00001 | $0.00166 |
Grade A, and why
investment-memo 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 8d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/investment-memo — Write Investment Memo
What This Command Does
Produces a structured investment memorandum suitable for investment committee presentation. Covers business overview, market opportunity, financial analysis, deal terms, key risks, and recommendation. Follows institutional investment memo standards with clear thesis articulation and risk/return framework.
When to Use
- Investment committee decision papers
- LP co-investment memos
- Internal deal screening documentation
- Follow-on investment justification
- Portfolio review and re-underwriting
- Exit recommendation memos
How It Works
- Executive Summary: One-page investment thesis with key metrics
- Business Analysis: Product, market, competitive landscape, team assessment
- Financial Review: Historical performance, projections, unit economics
- Deal Analysis: Valuation, terms, comparable transactions, return expectations
- Risk Framework: Key risks with mitigants and residual exposure
- Recommendation: Clear invest/pass recommendation with conditions
Example Usage
Input:
/investment-memo company:"DataLayer AI" type:vc-series recommendation:invest
Output:
# Investment Memorandum — DataLayer AI
## Series B | Recommendation: INVEST | Date: 2026-03-14
## CONFIDENTIAL — Investment Committee Use Only
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Executive Summary
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
**Thesis**: DataLayer AI is the leading data integration platform for
mid-market enterprises, growing 85% YoY with strong unit economics.
The Series B at $180M pre represents a fair entry for a company
approaching $30M ARR with a clear path to $100M+ ARR.
| Key Metric | Value |
|-------------------------|------------------------------------|
| ARR (current) | $28M (85% YoY growth) |
| Net Revenue Retention | 135% |
| Gross Margin | 78% |
| Burn Multiple | 1.2x (efficient growth) |
| Round Size | $45M |
| Pre-Money Valuation | $180M (6.4x ARR) |
| Our Allocation | $10M (22% of round) |
| Ownership (post) | 4.4% (fully diluted) |
| Target Return | 5-8x MOIC / 40-55% IRR (5yr) |
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Business Overview
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
**Product**: AI-powered data integration platform that enables mid-market
companies to connect, transform, and orchestrate data across 200+
enterprise applications. Replaces manual ETL processes and legacy
middleware.
**Market**: $35B TAM (data integration + iPaaS), growing 18% CAGR.
Mid-market segment ($50M-$500M revenue companies) is underserved
by enterprise vendors (Informatica, MuleSoft) and too complex for
SMB tools (Zapier, Make).
**Competitive Position**: #1 in mid-market data integration (G2 leader).
Key differentiation: AI-driven schema mapping (90% automation vs.
30% for competitors), 3x faster implementation.
**Team**: CEO (ex-Snowflake VP Eng), CTO (ex-Google Brain), 120 FTEs.
Strong engineering culture, 65% of team in R&D.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Financial Summary
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
| USD M | 2024A | 2025A | 2026E | 2027E | 2028E |
|--------------------|--------|--------|--------|--------|--------|
| ARR | 15.0 | 28.0 | 48.0 | 76.0 | 110.0 |
| Growth | — | 85% | 71% | 58% | 45% |
| Revenue (recognized)| 12.5 | 24.0 | 42.0 | 65.0 | 95.0 |
| Gross Margin | 75% | 78% | 80% | 82% | 83% |
| Net Burn | (8.5) | (10.0) | (12.0) | (8.0) | 2.0 |
| Burn Multiple | 1.5x | 1.2x | 0.7x | 0.3x | N/A |
| Cash (post-round) | — | — | 52.0 | 40.0 | 42.0 |
Unit economics (current):
- CAC: $35K (blended) | Payback: 11 months
- LTV: $210K | LTV/CAC: 6.0x
- Logo churn: 5% annually | Net dollar retention: 135%
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Return Analysis
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
| Exit Scenario | ARR | Multiple | Valuation | MOIC | IRR |
|--------------------|--------|----------|-----------|------|------|
| Bear (2030) | $120M | 8x | $960M | 4.3x | 34% |
| Base (2030) | $160M | 12x | $1,920M | 8.5x | 53% |
| Bull (2030) | $220M | 15x | $3,300M | 14.7x| 71% |
| IPO (2029) | $130M | 14x | $1,820M | 8.1x | 68% |
Probability-weighted MOIC: 7.5x | IRR: 50%
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Key Risks & Mitigants
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
| Risk | Severity | Mitigant |
|--------------------------|----------|------------------------------------|
| Enterprise vendor downmarket| High | 18-month product lead, switching costs|
| Key person risk (CEO/CTO)| Medium | Strong VP layer, vesting in place |
| Market concentration | Medium | No customer > 3% of ARR |
| AI commoditization | Medium | Proprietary training data moat |
| Macro / IT budget cuts | Low | ROI-positive, saves headcount |
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Recommendation
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
**INVEST $10M** in the Series B at $180M pre-money valuation.
Conditions:
1. Board observer seat
2. Pro-rata rights for future rounds
3. Quarterly financial reporting with ARR cohort data
4. Information rights per standard side letter
Requesting IC approval by 2026-03-28.
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.
- 8d ago First seen · 160 lines · 7 tokens per session scan A c3ca7cd0b367
investment-memo is a command published in the GitHub repository brainbytes-dev/everything-claude-finance (5 stars, last pushed 5mo ago), licensed MIT. It adds 7 tokens to every session and 1,664 once invoked, about $0.0000 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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