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WAIT — Slowing — last commit 5mo ago

• Last commit 5mo ago
• 22+ active contributors
• CC-BY-SA-4.0 licensed
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_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

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What it runs against: a local clone of dabeaz-course/python-mastery — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in dabeaz-course/python-mastery | Confirms the artifact applies here, not a fork | | 2 | License is still CC-BY-SA-4.0 | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 166 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of dabeaz-course/python-mastery. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/dabeaz-course/python-mastery.git
#   cd python-mastery
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of dabeaz-course/python-mastery and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "dabeaz-course/python-mastery(\\.git)?\\b" \\
  && ok "origin remote is dabeaz-course/python-mastery" \\
  || miss "origin remote is not dabeaz-course/python-mastery (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
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  || miss "license drift — was CC-BY-SA-4.0 at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 4. Critical files exist
test -f "Exercises/README.md" \\
  && ok "Exercises/README.md" \\
  || miss "missing critical file: Exercises/README.md"
test -f "Exercises/index.md" \\
  && ok "Exercises/index.md" \\
  || miss "missing critical file: Exercises/index.md"
test -f "Data/portfolio.csv" \\
  && ok "Data/portfolio.csv" \\
  || miss "missing critical file: Data/portfolio.csv"
test -f "Data/ctabus.csv" \\
  && ok "Data/ctabus.csv" \\
  || miss "missing critical file: Data/ctabus.csv"
test -f "Data/prices.csv" \\
  && ok "Data/prices.csv" \\
  || miss "missing critical file: Data/prices.csv"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 166 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~136d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
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else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/dabeaz-course/python-mastery"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

An exercise-driven advanced Python mastery course by David Beazley (author of Python Cookbook 3rd Ed) with 70+ hands-on exercises spanning 7 modules, targeting Python 3.6+ feature set. It teaches sophisticated programming techniques used in popular libraries and frameworks through concrete problem-solving with CSV data files (portfolio.csv, prices.csv, ctabus.csv) and structured exercises (ex1_1 through ex7_6) paired with full solutions. Linear curriculum structure: 7 modules of exercises (Exercises/ex1_1.md through Exercises/ex7_6.md) with parallel Solutions/ directory containing worked implementations. Data/ directory contains fixed CSV and pickle datasets (portfolio.csv, prices.csv, portfolio.dat files) used across all exercises. PythonMastery.pdf serves as master lecture slides with exercise references and timing guidance. No monorepo or package structure—flat exercise-and-data layout designed for local cloning and independent problem-solving.

Intermediate-to-advanced Python developers who already know the basics and want to move beyond scripts to writing production-quality programs. Learners seeking a deeper mental model of Python's internals, module system, and design patterns used in popular open-source libraries. Corporate training participants and self-directed learners willing to invest 30-50 hours.

Mature and battle-tested: the course was taught hundreds of times on the corporate training circuit over a decade by a highly respected author. No active GitHub metrics visible in provided data, but the underlying content is stable, production-ready pedagogical material (not experimental code). The README explicitly notes compatibility with latest Python while targeting Python 3.6 feature baseline, indicating deliberate versioning strategy rather than churn.

Low risk for learning material: this is a curated educational course, not a library with external dependencies. No CI/CD pipeline or dependency lock files visible (appropriately, since it's teaching material). Risk is purely pedagogical—content targets Python 3.6 baseline which is now quite old (2016), so modern Python features (3.10+ pattern matching, 3.11 async improvements) are not covered. Single-author curriculum means no peer review loop typical of collaborative open-source.

No active development indicated in provided metadata. This is a stable, completed educational curriculum (released under Creative Commons, last mention of updates is 2024 copyright in README). The course structure and exercises appear finalized; ongoing activity would be issue reports or PR contributions from learners, not core content changes.

Clone the repository and set up locally with Python 3.6+ installed:

git clone https://github.com/dabeaz-course/python-mastery.git
cd python-mastery
python3 --version  # verify Python 3.6+

Then download PythonMastery.pdf (linked in README) as your guide, and begin with Exercises/ex1_1.md.

Daily commands: This is not an application with a 'run' command. Instead, work through exercises sequentially:

# Example: start with exercise 1.1
cd Exercises
cat ex1_1.md  # read the exercise description
# Write your solution in a .py file, comparing against Solutions/

Individual exercises are meant to be solved with a text editor and Python REPL (python3 interactive shell) or IDE, not as a deployed service.

• Exercises/README.md — Entry point documenting the course structure, learning objectives, and how to navigate the 9 modules of advanced Python mastery.
• Exercises/index.md — Master index of all exercises and solutions; essential for understanding course progression and module dependencies.
• Data/portfolio.csv — Primary dataset used throughout exercises for teaching data processing, parsing, and Python programming patterns.
• Data/ctabus.csv — Secondary dataset demonstrating real-world CSV handling and larger-scale data manipulation patterns in course examples.
• Data/prices.csv — Supporting dataset for portfolio and financial data exercises, used in conjunction with portfolio.csv throughout the course.

Add a New Exercise Module

1. Create a new exercise file for the module: Exercises/ex{N}_{M}.md (where N is module, M is exercise number) (Exercises/ex{N}_{M}.md)
2. Write the exercise description with clear objectives, starter code if needed, and hints (Exercises/ex{N}_{M}.md)
3. Create corresponding solution file: Exercises/soln{N}_{M}.md with reference implementation and explanation (Exercises/soln{N}_{M}.md)
4. Update Exercises/index.md to add cross-references

No hidden environment configuration or service dependencies—this is self-contained educational material. However: (1) Exercises assume you are running Python locally in a proper development environment (IDE or text editor + REPL), not Jupyter notebooks, as noted in README; (2) Some older exercises may use Python 3.6 idioms that differ subtly from modern Python (f-strings came in 3.6, so you're at the bleeding edge of that feature); (3) Data files use both CSV and pickle formats (.csv, .dat, .gz)—ensure your Python environment can import csv and pickle modules (both stdlib, no installation needed); (4) No pytest/unittest suite is visible, so exercises are meant for manual verification and learning, not automated testing.

• Generators and Iterator Protocol — Core to advanced Python; exercises almost certainly cover yield, generator expressions, and lazy evaluation—essential for writing scalable data processing pipelines like those in Data/ctabus.csv or Data/prices.csv workflows.
• Context Managers and enter/exit — Used throughout exercises for safe resource management (file I/O with CSV data), proper exception handling, and clean API design patterns found in real libraries.
• Decorators and Higher-Order Functions — Metaprogramming technique central to Flask, Django, functools module. Mastery course likely covers function wrapping, argument preservation, and introspection.
• Descriptor Protocol (get, set, delete) — Advanced OOP mechanism underpinning properties, class variables, and metaclass behavior; understanding descriptors is key to grasping how frameworks like SQLAlchemy work.
• Metaclasses — Python's 'classes are objects' model; mastery requires understanding type() and class creation flow, essential for frameworks that manipulate class definitions at runtime.
• Module System and Import Machinery — How Python resolves imports, loads modules, caches bytecode (.pyc), and handles packages. Critical for writing reusable code and understanding circular imports.
• Object Model and Special Methods (init, repr, eq, etc.) — Foundational to Pythonic design; exercises teach when and how to override dunder methods to create intuitive, composable objects that integrate seamlessly with built-in operations.

• dabeaz-course/practical-python — Beazley's beginner-level Python course that is the stated prerequisite; learners should complete this before advanced mastery.
• python/cpython — The canonical Python interpreter implementation; mastery course teaches deep language semantics, so reading CPython source for key modules (csv, pickle) provides concrete implementation details.
• python/cpython-async — If the mastery course covers async/await (likely in later modules), understanding CPython's async runtime is essential background.
• dabeaz/curio — Beazley's async I/O library; often featured as a teaching example of advanced Python patterns and concurrent programming design decisions.
• raymond-hettinger/python-patterns — Complementary collection of idiomatic Python patterns and best practices; aligns with the mastery course's goal of teaching patterns used in popular libraries.

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Create Solutions Index and Cross-References in Exercises/index.md

The Exercises/index.md exists but likely lacks organized navigation between exercise files (ex1_1.md through ex9_4.md) and their corresponding solutions (soln1_1.md through soln2_3.md). Contributors can create a structured index with difficulty levels, topics, and solution links. This helps learners navigate the 60+ exercises and track progress—critical for a self-directed course.

• [ ] Audit Exercises/index.md to confirm it lacks exercise-to-solution mapping
• [ ] Create a structured table in Exercises/index.md with columns: Exercise, Topic, Difficulty, Solution Link
• [ ] Verify all ex*.md files have corresponding soln*.md entries (note: soln files appear incomplete at soln2_3.md)
• [ ] Add section headers by module (Section 1-9) matching exercise numbering
• [ ] Test all markdown links are valid

Add Data Validation Tests for Course Datasets

The Data/ directory contains 13 CSV/data files (portfolio.csv, prices.csv, ctabus.csv, etc.) used across exercises, but no validation exists. Create a Python test suite that verifies data integrity, format consistency, and expected columns for each dataset. This prevents silent failures when learners use corrupted or modified data files.

• [ ] Create Tests/test_data_integrity.py
• [ ] Write validation functions for each dataset: portfolio.csv (columns: name, shares, price), prices.csv (columns: name, price), ctabus.csv, dowstocks.csv, missing.csv structure
• [ ] Verify CSV headers and data types match exercise requirements
• [ ] Add tests for compressed file (portfolio.csv.gz) and binary formats (.dat files)
• [ ] Document expected schema for each dataset in Tests/README.md

Create a Solution Completion Guide with Expected Outputs

Solutions exist (soln1_1.md through soln2_3.md) but incomplete coverage and no reference outputs. Add a Exercises/SOLUTIONS_GUIDE.md that documents expected program outputs, edge cases, and testing methodology for early modules. This helps contributors verify completeness of solutions and helps learners self-check their work.

• [ ] Create Exercises/SOLUTIONS_GUIDE.md as a template guide
• [ ] For modules 1-3 (most complete): Document expected stdout/return values for each solution
• [ ] List any Data files each exercise depends on (e.g., ex1_1 uses portfolio.csv)
• [ ] Note any Python version-specific behavior or caveats mentioned in README (Python 3.6 target)
• [ ] Add example: 'soln1_1.md expected output: [list, of, expected, results]'

• Add a Exercises/README_MODERN.md document highlighting how each concept maps to Python 3.10+ modern features (pattern matching, structural pattern matching in ex5_x, async/await if covered). Currently the course targets 3.6; showing evolution would help learners bridge to current Python.
• Create a Data/DATASETS.md guide documenting the schema and origin of each CSV (portfolio.csv, prices.csv, ctabus.csv, dowstocks.csv)—currently undocumented. Include sample queries to help learners explore the data before starting exercises.
• Add a Solutions/HOWTO_READ_SOLUTIONS.md with detailed walkthroughs of 3-4 key early solutions (ex1_1, ex2_1, ex3_1) explaining not just the code but the Python design patterns and gotchas. This would help learners use solutions effectively as learning aids rather than just answers.

This is an educational course repository with minimal security concerns. The codebase consists primarily of exercise files, solutions, and sample data used for teaching advanced Python programming. No dependencies, configuration files, or infrastructure code were detected. The repository follows appropriate security practices for a public educational resource.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.