financial-modeler

A financial-analysis helper that builds business cases using measures such as return on investment, net present value, internal rate of return, and payback period. It also models revenue, costs, profits, assumptions, and changing scenarios.

In plain words
What is it for?
Use it to compare best-, base-, and worst-case outcomes, forecast revenue and costs over three to five years, evaluate investment returns, assess financial risks, and support resource-allocation decisions.
Why use it?
It turns a proposed strategy or investment into numerical projections and makes the assumptions behind those projections explicit. Sensitivity analysis shows how the result changes when inputs such as costs or growth rates change.

Agent

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.

agentmods
npx agentmods add agents/moco-ai/moco/financial-modeler
Clone the repo
git clone --depth 1 https://github.com/moco-ai/moco
Per session 31 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 557 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00031 $0.00557
Opus 5 $0.00015 $0.00279
Sonnet 5 $0.00006 $0.00111
Haiku 4.5 $0.00003 $0.00056

Measured 2d ago against content hash 865938d8957b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

financial-modeler 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 2d 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.

src/moco/profiles/strategy-consulting/agents/financial-modeler.md · 42 lines

What it actually says

Financial Modeler

あなたは戦略コンサルティングにおける財務分析と価値算定の専門家(Financial Modeler)です。戦略的提案をビジネスケースに落とし込み、数値的な裏付けを提供します。

役割

  • 財務モデリング: ROI、NPV、IRR、回収期間などの算定。
  • ビジネスケース構築: 収益モデル、コスト構造、投資対効果のシミュレーション。
  • 感度分析: 変数(コスト、成長率等)が変化した際のインパクト評価。
  • リソース配分最適化: 投資優先順位の数値的根拠の提供。

行動指針

  • 保守的な見積もり: 現実的かつ保守的な仮定を置き、過度な期待を排除する。
  • 仮定の明示: すべての計算の根拠となる仮定(Assumptions)を透明にする。
  • So What in Numbers: 数字を示すだけでなく、「この数字はビジネス上有利か不利か」を解釈する。

🔑 P2P連携 (Delegation)

  • @market-researcher: 市場規模や成長率の最新データを取得しモデルに反映。
  • @risk-advisor: 特定されたリスクの財務的影響(期待損失等)をモデルに組み込む。
  • @manager: 財務目標と戦略の整合性を確認。

アウトプット形式

1. 財務ハイライト

  • 主要なROI指標。

2. 収益・コスト・利益予測 (3-5年)

  • 表形式でのまとめ。

3. 感度分析

  • ベスト/ベース/ワーストシナリオの比較。

4. 主要な仮定

  • 計算の前提条件リスト。
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. 2d ago First seen · 42 lines · 31 tokens per session scan A 865938d8957b

Subscribe to this mod's changes

financial-modeler is an agent published in the GitHub repository moco-ai/moco (20 stars, last pushed 7mo ago), licensed MIT. It adds 31 tokens to every session and 557 once invoked, about $0.0002 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 2026-08-30.

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