Practical Python, AI, and automation for accounting and finance teams. Built for professionals who want to work faster and go deeper — without becoming developers.
Each article includes the full write-up, sample data, and the code to reproduce every chart.
| # | Title | Topics |
|---|---|---|
| 00 | True or False: AI Will Replace My Job? | Career perspective, Python fundamentals, durable skills, why Python Muse exists |
| 00.5 | Where to Start If You’re Ready to Work With AI | Getting started, VS Code, Python setup, Microsoft Copilot, mindset shift |
| 01 | Your AI Co-Pilot for Accounting | Margin analysis, vendor cost inflation, salesperson performance, revenue concentration |
| 02 | Ways to Use Claude: Choosing the Right Interface | Claude.ai, API, Claude Code, interface comparison, getting started |
| 03 | Getting the Right Tools Installed: A Safe Starting Point for Accounting Teams | IT approvals, secure adoption, VS Code and Python setup framing, low-risk pilot use cases |
| 04 | AI in Accounting Is Not the Wild West Anymore | AI governance, COSO framework, PCAOB updates, Big Four guidance, audit-ready workflows |
| 05 | Reproducible Accounting | Accounting as code, audit evidence, version control, reproducible financial reporting |
| 06 | How to Use AI in Accounting Without Sending the Wrong Data | Data masking, local vs cloud processing, safe AI workflows, QuickBooks demo, validation hooks |
| 07 | AI Governance for Controllers | COSO framework, controller governance, use case inventory, AI skills, agents, VS Code |
| 08 | Why Claude “Forgets” – And How to Fix It | Context window, external memory, project files, plan.md, status_update.md, CLAUDE.md |
| 09 | How Accountants Learn AI | Excel-to-AI learning path, 13-skill framework, Markdown, Python, Git, hooks, canary, project hygiene |
| 10 | AI in Accounting: Real Use Cases – and How to Structure Them | Reconciliations, variance analysis, ad hoc analysis, 3-tier classification, exploratory vs repeatable vs audit-ready |
| 11 | From One-Time Analysis to Repeatable Workflows | 9-step workflow pattern, data masking, headers-only, hooks, plan approval, SKILL documentation |
| 12 | When to Trust AI to Run Your Accounting Workflows | Audit-ready framework, COSO mapping, governance controls, logged execution, canary checks |
| 13 | AI in Accounting Isn’t Just About Efficiency – It’s About Control | Zero Trust for AI, OWASP LLM risks, data controls, trust but verify, four-level framework, checklist |
| 14 | Stop Using One AI Like It Is Excel | Claude vs ChatGPT vs Copilot, model orchestration, skills, GitHub as distribution layer, folder structure as prompt |
| 15 | “AI Can’t Work With Our Excel Files”… or Can It? | Excel instruction layer, SKILL for spreadsheets, three-tier data approach, train once reuse forever |
| 16 | The PDF Token Trap | PDF-to-Markdown workflow, token efficiency, data masking, reusable Skills, convert once reuse forever |
| 17 | The Power of Skills and Agents: How Accountants Actually Use AI | Skills, agents, workflow design, internal controls, living documentation |
| 17b | Your First CLAUDE.md: How Accountants Define the Agent | CLAUDE.md structure, governance documentation, accounting-specific rules, workflow kit |
| 18 | Scheduled Workflows for Accounting Teams | Scheduled tasks, cron, automation, recurring workflows |
| 19 | Multi-Agent Orchestration for Accountants | Multi-agent, orchestration, parallel workflows, accounting automation |
| 20 | Version Control for Accountants | Git, version control, audit trail, reproducibility |
| 21 | The 3 Mindsets of AI Adoption in Accounting | Mindset, change management, Builder/Stabilizer/Protector, AI adoption |
| 22 | The Workings Layer: Fitting AI Into the Files You Can’t Change | Workings layer, folder structure, CLAUDE.md placement, legacy files, audit-ready |
| 23 | Don’t Trust the Model to Find What You Already Know Is There | Schema-driven sanitization, PII detection, data privacy, accounting data workflows, Privacy Filter |
| 24 | AI Didn’t Break Your Numbers. Excel Did. | Excel formatting, accounting number normalization, data discipline, Top 10 AI Traps #1 |
| 25 | What the Heck Is a Script? | Scripts explained for accountants, Excel vs Python readability, SOPs for computers, audit-friendly logic |
| 26 | When Your AI Enters Month-End Close Mode | Context drift, session management, context window, accounting analogies, SKILL files, checkpoints |
| 27 | Visual Studio Code Extensions for Accountants | VS Code extensions, governance, security, minimalism, extensions vs libraries, starter stack |
| 28 | Python Libraries for Accountants: Skills You Teach Your Code | Python libraries, pandas, openpyxl, matplotlib, pip install, environments, extensions vs libraries |
| 29 | The Magic Loop: Why Easy to Generate Doesn’t Mean Safe to Run | Workflow automation, YAML configuration, loop governance, review before run, abstraction layers |
| 30 | AI Routines for Accountants: When Your Guidance Starts Checking Itself | AI routines, monitoring workflows, proposed change packages, human review gates, audit evidence, tax guidance example |
| 31 | Metadata Is the Label Maker Your AI Workflow Needs | Metadata governance, SKILL.md, file manifests, scripts vs. controls, hooks, audit trail |
| 32 | From AI Answers to Audit Trails: How Accountants Can Validate AI Output | AI output validation, chat vs. harness, tie-out reports, evidence trails, reviewer checklists |
Each article folder is self-contained:
articles/
└── 01-ai-copilot-for-accounting/
├── README.md ← the article
├── visuals/ ← charts referenced in the article
├── data/ ← sample CSV files
└── generate_visuals.py ← script to reproduce all charts
To reproduce the analysis in any article:
data/ folderContributions are welcome from:
Whether you write code or review accounting logic, there’s a way to help.
See the full list at community/ideas-wanted.md.
Open an issue, comment on an existing request, or submit a pull request.
See CONTRIBUTING.md for details and community/roadmap.md for where the project is headed.
This project uses a dual license:
.py files) is licensed under the MIT License — free to use, modify, and distribute.See LICENSE and LICENSE-CODE for full details.
This repository is provided for educational purposes only.
This repository may include AI-assisted workflows and scripts.
Users must:
PythonMuse LLC is not responsible for any outcomes resulting from use of this code.
By Svetlana Toohey