finance-skills

Name: finance-skills
Author: alirezarezvani/claude-skills

$npx mdskill add alirezarezvani/claude-skills/finance-skills

This plugin bundles the finance skills below plus this router. Each skill is self-contained.

SKILL.md

.github/skills/finance-skillsView on GitHub ↗

---
name: "finance-skills"
description: "Router/index for the finance skills bundled in this plugin: financial-analyst (ratio analysis, DCF valuation, budget variance, rolling forecasts), saas-metrics-coach (ARR/MRR, churn, CAC/LTV, NRR, quick ratio) and month-end-close (close calendar, reconciliations, flux review). Use when a finance request doesn't obviously match one skill and you need to pick the right one (e.g., 'analyze these financials', 'how healthy are my SaaS metrics')."
license: MIT
metadata:
  version: 2.9.0
  author: Alireza Rezvani
  tags:
    - finance
    - financial-analysis
    - dcf
    - valuation
    - budgeting
  agents:
    - claude-code
    - codex-cli
    - openclaw
---

# Finance Skills — Router

This plugin bundles the finance skills below plus this router. Each skill is self-contained.

## Routing table

| Request signals | Skill | Path |
|---|---|---|
| Ratio analysis, DCF valuation, budget variance, driver-based forecasts | financial-analyst | `skills/financial-analyst/` |
| ARR/MRR, churn, CAC/LTV, NRR, quick ratio, SaaS benchmarks | saas-metrics-coach | `skills/saas-metrics-coach/` |
| Month-end close, close calendar, bank or sub-ledger reconciliation, flux / variance explanations | month-end-close | `skills/month-end-close/` |

If both match (e.g., "value my SaaS company"), ask whether the user wants statement-level analysis (financial-analyst) or SaaS operating metrics (saas-metrics-coach).

## Quick start

```bash
# Example: route a statement-analysis request
cat finance/skills/financial-analyst/SKILL.md
python3 finance/skills/financial-analyst/scripts/ratio_calculator.py --help

# Or a SaaS metrics request
python3 finance/skills/saas-metrics-coach/scripts/metrics_calculator.py --help
```

## Related (packaged separately, not in this bundle)

- `finance/business-investment-advisor/` — investment thesis evaluation, ROI modeling (prompt-only skill, separate nested plugin)
- Root commands `/financial-health` and `/saas-health` wrap these skills' scripts.

## Rules

- Route to exactly one skill, then follow that skill's workflow. This router ships no tools of its own.
- Always validate financial outputs against the user's source data; outputs are analysis support, not investment advice.

More from alirezarezvani/claude-skills

Skill	Description
a11y-audit	Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
ab-test-setup	When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
ad-creative	When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
adversarial-reviewer	Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
aeo	Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
agent-decision-receipts	Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
agent-designer	Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
agent-harness	Turn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify every task with machine-run checks, retry with caps, escalate to a human when budgets exhaust, and refuse to close until everything is verified or explicitly waived. Use when you want an agent or subagent to pick up a goal and drive it to a verified close across one of this repo's 18 domains ('run this goal through the engineering harness', 'set up an agentic loop for marketing work', 'make the finance domain self-verifying'). NOT for authoring Claude Code Workflow-tool .js scripts (workflow-builder), N-agent tournaments on one task (agenthub), single-file metric optimization (autoresearch-agent), or discovering published loop recipes (loop-library).
agent-launcher-orchestrator	Use when a user wants to build, launch, grade, or schedule a Claude Managed Agent (CMA) in their own Anthropic account — "build me an agent", "launch this as a managed agent", "run this on a schedule", "grade my agent against a rubric", "set up a nightly worker". Reads the per-session goal (./my-agent/goal.json), routes deterministically to one of five phase sub-skills (interview → stage-launch → grade-iterate → run-without-you → wrap-up) via goal_router.py, and compiles the goal+phase into an execution shape (single-pass workflow / bounded grade→iterate loop / recurring cron deployment loop) via loop_compiler.py. Forks context so heavy intake (build sheets, payloads, eval cases) stays out of the parent thread. All launches are emitted as BYOK curl the user runs with their own key; no tool makes API calls. Inspired by anthropics/launch-your-agent (Apache-2.0). Distinct from engineering/agent-harness (generic domain loop) and engineering/write-a-skill (authors Claude Code skills, not CMAs).
agent-memory	Use when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts / L1 candidates / L2 project context / L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.