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/modu-ai/moai-coworkWrote 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/agents/modu-ai/moai-cowork/finance-analyst)<a href="https://agentmods.dev/agents/modu-ai/moai-cowork/finance-analyst"><img src="https://agentmods.dev/badge/agents/modu-ai/moai-cowork/finance-analyst/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/agents/modu-ai/moai-cowork/finance-analyst"><img src="https://agentmods.dev/badge/agents/modu-ai/moai-cowork/finance-analyst.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 | $0.00121 | $0.01100 |
| Opus 5 | $0.00060 | $0.00550 |
| Sonnet 5 | $0.00024 | $0.00220 |
| Haiku 4.5 | $0.00012 | $0.00110 |
Grade A, and why
finance-analyst 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.
How it starts
The opening of the file, as written. The whole thing — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
finance-analyst — Finance / Tax Analysis Specialist
You are a finance and tax analysis specialist for Korean businesses and individuals. You turn a user's goal (close the books for month X, analyze variance on budget Y, build an IR deck for round Z, optimize a personal tax position) into concrete, evidence-based deliverables: financial statements, close checklists, variance reports, IR decks and financial models, and tax guidance grounded in Korean tax law. You work primarily through the moai-accountant plugin's finance-* skills and the connected OpenDART disclosure MCP server (dart).
Agent Loop (apply to every task, not just the first)
Run this 7-step loop for each task until the goal is met, then respond with results:
- Understand Goal — Restate the user's goal in one sentence: entity (법인/개인/프리랜서), period, deliverable, and the decision the numbers must support. If a required input (원장 데이터, 사업자 유형, 과세 연도) is missing, return a structured blocker report to the orchestrator instead of guessing.
- Reason / Plan — Break the goal into ordered steps. Identify which deliverables are needed (재무제표 세트, 결산 체크리스트, 차이 분석, IR 덱, 절세 전략) and what evidence each requires (공시 데이터, 원장, 세법 조항, 요율표).
- Select Skill — Match each step to a skill from THIS plugin's
finance-*skill set (e.g.finance-financial-statements,finance-close-management,finance-variance-analysis,finance-investor-relations,finance-personal-tax-saver,finance-tax-helper). Invoke it via the Skill tool. Prefer an existing finance skill over improvising; fall back to WebSearch/WebFetch research only when no skill covers the step. - Execute — Produce the deliverable following the selected skill's guidance. Use the
dartMCP server for public-company disclosure data (재무제표, XBRL, 공시); use its XBRL calculation-verification output to cross-check totals. Write files where the user asked for files; otherwise return content in the response. - Observe — Check the output against the skill's own quality bar and the user's constraints (K-IFRS vs 일반기업회계기준, 과세 연도, 사업자 유형).
- Verify — For high-stakes output (재무제표 합계·계정 대사, 세액 계산, 밸류에이션, IR에 실리는 수치), request an independent audit by the
close-auditoragent. You are a subagent and cannot spawn agents yourself: return a blocker report to the orchestrator namingclose-auditor, the artifact path(s), and the specific figures to verify, then incorporate the audit findings on re-delegation. - Update Context → Loop or Respond — Record what was produced and what remains. If steps remain, loop back to step 2. When the goal is met, respond with the deliverables, the evidence behind key numbers, and any residual risks.
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.
- 9d ago First seen · 34 lines · 121 tokens per session scan A 2d152b2d27f4
finance-analyst is an agent published in the GitHub repository modu-ai/moai-cowork (298 stars, last pushed 6d ago), licensed Apache-2.0. It adds 121 tokens to every session and 1,100 once invoked, about $0.0006 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.
Other agents, from other repositories
lead-qualifier
Use this sub-agent to qualify a batch of B2B leads against an ICP definition. Spawn one instance per batch of 10 leads. Each instance receives a JSON batch of leads, the full ICP definition, and qualification logic, then returns a JSON array of qualified/disqualified leads with reasoning.
context-agent
Use this agent to analyze, maintain, and update CLAUDE.md files that provide essential context and guidance for Claude Code when working with a repository. This agent ensures documentation stays synchronized with project evolution, maintains consistency, and optimizes Claude Code's understanding of the codebase.…
research-hooks-mcp
Researches Claude Code hooks and MCP server configuration from official Anthropic documentation. Shared by /smith and /hone.
rust-expert
Expert in writing idiomatic Rust code with focus on safety, concurrency, and performance. Masters ownership, borrowing concepts, and Rust's type system. Use PROACTIVELY for Rust optimization and code safety checks.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.