PythonMuse Workflow Kit

A ready-to-use project template for AI-assisted accounting workflows. Clone it, fill in your plan, drop in your data, and start working.

Built on the PythonMuse methodology for controlled, repeatable, and audit-ready AI workflows.

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Use This Template

On GitHub, click "Use this template" to create your own copy. Or clone it directly:

Windows (PowerShell) — standard:

git clone https://github.com/PythonMuse/pythonmuse-workflow-kit.git ~\Documents\my-project
cd ~\Documents\my-project
code .

Windows (PowerShell) — OneDrive users: If your Documents folder is synced via OneDrive, use the full path:

git clone https://github.com/PythonMuse/pythonmuse-workflow-kit.git "$env:USERPROFILE\OneDrive\Documents\my-project"
cd "$env:USERPROFILE\OneDrive\Documents\my-project"
code .

Mac / Linux / Git Bash:

git clone https://github.com/PythonMuse/pythonmuse-workflow-kit.git ~/Documents/my-project
cd ~/Documents/my-project
code .

🎥 Video Walkthrough

Prefer to watch instead of read? Full walkthrough below:

https://youtu.be/O1E1mMKWp2s

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Requirements

• Python 3.10+
• Dependencies listed in requirements.txt:

python -m venv .venv
.venv\Scripts\activate      # Windows
source .venv/bin/activate   # Mac/Linux
pip install -r requirements.txt

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What Is Inside

pythonmuse-workflow-kit/
  .vscode/
    extensions.json         recommended VS Code extensions
    settings.json           workspace settings

  CLAUDE.md                 instructions for AI (rules, data locations, tone)
  plan.md                   project blueprint (fill this in first)
  status_update.md          rolling progress tracker

  data/
    raw/                    original source files (never modified)
    processed/              cleaned or transformed data

  src/                      scripts and logic files

  outputs/                  generated reports and results

  evidence/
    run-logs/               saved AI run logs for audit trail

  skills/
    bank-reconciliation/
      SKILL.md              reusable bank reconciliation workflow
    margin-analysis/
      SKILL.md              reusable margin analysis workflow
    pdf-extract/
      SKILL.md              PDF-to-Markdown extraction workflow
    scheduled-dashboard/
      SKILL.md              daily sales & service dashboard — runs on a schedule

  docs/                     notes, memos, reference material

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How It Works

1. Edit plan.md -- describe your objective, source data, steps, and assumptions
2. Drop data into /data/raw/ -- masked or sample data only
3. Start an AI session -- point it at plan.md and CLAUDE.md
4. AI follows the plan -- proposes steps, waits for approval, saves outputs
5. Review and document -- AI updates status_update.md when done

The folder structure is not just organization. It is the context that makes AI effective.

Your folder structure is your prompt.

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Using This with GitHub Copilot

GitHub Copilot reads your project structure, file names, and Markdown files to understand context. This template is designed to give Copilot everything it needs.

Quick Start Prompts

Set up a new project:

"I just cloned the PythonMuse Workflow Kit. Read CLAUDE.md and plan.md. Help me fill in plan.md for a bank reconciliation for March 2026."

Import the structure into an existing project:

"Look at the PythonMuse Workflow Kit at github.com/PythonMuse/pythonmuse-workflow-kit. Set up the same folder structure and template files in my current workspace."

Use a skill:

"Read the bank reconciliation skill in /skills/bank-reconciliation/SKILL.md. Apply it to the files in /data/raw/ and save results to /outputs/."

Start a new session (after a break):

"Read plan.md and status_update.md. Summarize where we left off and confirm the next step."

Save progress before ending:

"Update status_update.md with what was completed, where files are saved, any issues, and next steps."

Why This Works with Copilot

Copilot prioritizes what it can see in your workspace:

What Copilot Reads	What It Learns
Folder names (/data/raw/, /outputs/)	Where to find and save files
CLAUDE.md	Rules and constraints to follow
plan.md	What the project is trying to accomplish
status_update.md	Where things stand right now
skills/*.md	How to execute specific workflow types

A well-structured project replaces dozens of repeated prompts.

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Try the Demo

This kit ships with sample data from two articles — you can explore both without bringing your own data.

Demo 1 — Margin Analysis (Article 01)

The original two sample CSVs from Article 01: AI Co-Pilot for Accounting are in data/raw/. Walk through the full margin-analysis story using docs/demo-prompts.md.

Demo 2 — Scheduled Daily Dashboard (Article 18) ✨ New

Two new sample CSVs (daily_sales.csv, service_calls.csv) are now also in data/raw/. Run the daily dashboard script to see an AI-generated HTML dashboard:

pip install -r requirements.txt
python src/build_daily_dashboard.py

Then open outputs/daily_dashboard/dashboard_YYYYMMDD.html in your browser.

See the full walkthrough: Article 18 — AI That Runs Before You Log In

Use the skill for your own data: skills/scheduled-dashboard/SKILL.md

Walk through it: open docs/demo-prompts.md and run the prompts in order in Copilot Chat with Claude selected.

Step	What you'll prompt	What you'll get
1	Orient on the data	Structural read of both files
2	Calculate margin per order	Low-margin orders flagged
3	Show formulas, tie to source	Validation you can defend
4	Export joined dataset to Excel	Workpaper for manual review
5	Compare 2024 vs 2025	YoY margin compression view
6	Rank reps, vendors, labor	Concentration and inflation patterns
7	Visualize the story	The five article charts as PNGs
8	Promote charting to a skill	Reusable /skills/visualize-margin/
9	Strategic summary	5-point CFO-ready memo
10	Explain it back	Reproducible narrative
11	Validate before repeating	AI validation report + human sign-off
12	Convert to a repeatable script	Production Python in /src/

Step 11 is the gate -- do not script anything you cannot defend by hand. Step 12 is the inflection point: once validated, you prompt Claude to turn the conversation into a versioned, commented, human-editable script -- with a CONFIG block, # >>> HUMAN INPUT NEEDED: markers, input validation, and a paired runbook. See the full prompts in docs/demo-prompts.md.

Real client data? The demo prompts are deliberately short. Before running this flow on anything sensitive, read the Before You Start -- Real-World Safety Note at the top of docs/demo-prompts.md. Mask first, state materiality and period explicitly, and forbid silent assumptions.

Demo 3 — Multi-Agent Orchestration (Article 19) ✨ New

Want to see what a coordinated, multi-agent close workflow looks like before you build one? The multi-agent/ folder is a fully annotated demo:

multi-agent/
  orchestrator.md             master workflow: who does what, in what order
  agents/
    gl-agent.md               GL validation role and rules
    bank-rec-agent.md         bank reconciliation role and rules
    variance-agent.md         variance commentary role and rules
  status/
    workflow-status.md        shared handoff tracker — agents update this as they work
  outputs/                    where each agent saves its results

Start here: Open multi-agent/orchestrator.md and read it top to bottom. It is the whole workflow, in plain language. Then open the agent files to see how each role is defined.

Try it with Copilot:

"Read multi-agent/orchestrator.md. Confirm the source files are present in data/. Begin Step 1 using the instructions in multi-agent/agents/gl-agent.md."

Then watch the workflow move step by step -- each agent produces a defined output, updates the status file, and hands off to the next.

For the full explanation of how and why this works, see Article 19 — When One Agent Is Not Enough.

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From One-Time to Repeatable

A script is the first level of automation, not the last. After Step 9, here is the maturity ladder you can climb -- pick the next move that solves your real pain:

1. Promote to a Skill -- move the logic into /skills/<name>/SKILL.md so any future session can invoke it by name.
2. Pin the prompt -- version-control the prompt sequence under docs/prompts/ so it survives sessions.
3. Externalize config -- lift the CONFIG block out of Python into config.yaml so non-coders own the settings.
4. Add a smoke test -- have Claude diff this run's totals against last month's approved output.
5. Add a CLI wrapper -- python -m src.<script> --period 2025-Q4 so it can be scheduled.
6. Generate the runbook -- ask Claude to produce RUNBOOK.md next to the script, not just the code.
7. Lock the contract -- a schema check that fails loudly when next month's file format drifts.
8. Capture a golden output -- save one approved run as /evidence/golden/ and diff future runs against it.

Full prompts for each move are in docs/demo-prompts.md under Beyond the Script.

For the underlying methodology see Article 11: From One-Time to Repeatable Workflows.

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Using This with Claude Code

If you use Claude Code (inside VS Code), the experience is similar. Claude reads CLAUDE.md automatically and uses plan.md and status_update.md as project context.

Start a session with:

"Read plan.md and status_update.md. Summarize current state and confirm next step."

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Included Skills

Skill	Use Case ID	Description
Bank Reconciliation	UC-001	Match bank to GL, classify exceptions, produce audit-ready summary
Margin Analysis	UC-003	Gross margin by segment, period comparison, concentration risk flags
PDF Extract	UC-004	Convert PDF to Markdown, mask sensitive data, extract structured fields

To add your own skill, create a new folder under /skills/ with a SKILL.md following the same format.

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The Three Memory Files

File	Purpose	When to Update
CLAUDE.md	Instructions for how AI should behave	At project start; adjust as needed
plan.md	Defines the project scope, rules, and steps	At project start; revise as scope changes
status_update.md	Tracks completed work, outputs, issues, next steps	After each major milestone

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Learn More

This template is part of the PythonMuse AI Ledger series -- practical Python, AI, and automation for accounting and finance teams.

• Article 01: AI Co-Pilot for Accounting -- the worked example reproduced by the demo prompts in this kit
• Article 08: Why Claude "Forgets" -- why these three files matter
• Article 11: From One-Time to Repeatable Workflows -- the nine-step workflow pattern
• Article 14: Stop Using One AI Like It Is Excel -- how to use Claude, ChatGPT, and Copilot together
• Article 16: The PDF Token Trap -- why PDF-to-Markdown saves tokens and how the pdf-extract skill works
• Article 19: When One Agent Is Not Enough -- how to design and run orchestrated multi-agent accounting workflows
• AI Accounting Framework -- the full 13-section learning framework

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Contributing

Found a useful skill? Built a workflow template? Contributions are welcome.

See CONTRIBUTING.md for details.

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From PythonMuse -- Practical Python, AI, and automation for accounting and finance teams.