EC1B1 Python Coding SupportFollow-up resources for LSE BSc Economics
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Session 5 Toolkit: Mock Coursework Execution Workflow

Practical guide for running the mock coursework with AGENTS.md, reusable skills, and careful verification.

Updated 2026-02-20

Why this page exists

Session 5 was the final integration session. Instead of introducing one more tool, the focus was to combine everything from the series into one repeatable workflow for coursework-level Python projects.

This page turns that final workflow into a checklist you can reuse.

The Session 5 workflow in one line

Read first, structure second, automate third, verify always.

Step-by-step playbook

1. Set up the project folder

  • Create one dedicated project directory.
  • Put all downloaded coursework files in that directory.
  • Keep raw data separate from derived outputs.

2. Read documents before coding

  • Read the coursework brief fully.
  • Read the data README for variable descriptions.
  • Confirm deliverables and submission format before implementation.

3. Create a short AGENTS.md

  • Define your project goal and scope.
  • Add coding and data rules.
  • Keep it compact and actionable.
  • Include a non-negotiable rule such as: never overwrite raw CSV files.

If you need a template, use the AGENTS.md Writing Guide.

4. Define high-value skills

Start with one repeated workflow, such as data cleaning.

A practical skill pattern:

  1. inspect data quality
  2. generate a report
  3. propose fixes
  4. ask for approval
  5. apply only approved fixes

5. Execute in small chunks

  • Do not ask for the full project in one prompt.
  • Build incrementally and review after each step.
  • Keep commits or checkpoints frequent so you can recover quickly.

6. Verify before accepting outputs

  • Check code correctness and reproducibility.
  • Check economic plausibility of results.
  • Check whether written answers match the actual outputs.

Skills vs sub-agents: when to use each

Common cases:

  • Repeated, structured task (cleaning, formatting, diagnostics): use a skill for predictable workflow and easy reuse.
  • Focused specialist task with autonomy (consistency review, cross-file checks): use a sub-agent for dedicated reasoning.
  • Large project with independent streams: use multiple agents carefully, with clear coordination rules.

Practical recommendation from Session 5:

  • start with skills
  • add one sub-agent for consistency checks
  • only scale to many agents if your structure is already clear

Final session reminders

  • You are responsible for the final output, even if AI generated part of it.
  • Do not submit content you have not read and verified.
  • Keep your economics reasoning in the driver's seat.

Session links