Hands-on Python with Claude Code · Challenge 01 · 中文版
Get Claude Code running, install the FlyPython Skill with network access approved, and let the agent fetch this course's files — you download nothing. Then make the starter's failure objective.
By the end of this lesson Claude Code is running, the FlyPython Skill is installed in it (with network access approved), and the agent has fetched this course’s files into your working folder — you downloaded nothing by hand. With the folder in place you run the course’s objective check and explain what “the starter fails” means in test names. No code is changed in this lesson.
Install the Claude Code CLI
(npm install -g @anthropic-ai/claude-code), create an empty working
folder, and start claude in it.
Optional: Step 3’s entry sentence reads the Skill straight from its URL, so the course works without this step — pre-installing just saves a permission round-trip on tools that keep skills between sessions.
Save the Skill as a project skill — run this in the working folder:
mkdir -p .claude/skills/flypython
curl -s https://flypython.com/skills/flypython/SKILL.md -o .claude/skills/flypython/SKILL.md
(A personal copy for all projects: the same command under ~/.claude/skills/.)
Claude Code reaches the web through permission prompts — when it asks to
fetch or curl, approve. If you run it inside a network-blocking sandbox,
run the curl yourself and paste the file instead.
The Skill is one file, the same in every tool: it tells the agent how to authorize you, fetch course files, verify, and submit claim codes.
Start a new session/chat/thread in the working folder and paste this one sentence:
Read https://flypython.com/skills/flypython/SKILL.md and start the FlyPython course
hands-on-python-with-claude-code.
The agent gives you an authorization link with a short code. Open it, log
in, make sure the code matches what the agent showed you, and click
Allow — then tell the agent “done”. It takes a token once and writes this
course’s files to courses/hands-on-python-with-claude-code/.
Prerequisite: these courses need a coding agent that can run commands and reach the network. A chat-only web AI cannot do them.
Most AI-written Python dies the same way: the demo input works, the real input arrives, and the script crashes on row 3 — or worse, quietly produces a wrong number. The fix does not start with a better prompt. It starts with turning “it’s broken” into a command that anyone can run and get the same answer.
Open starter/report_tool.py and read it top to bottom. It looks reasonable:
functions with docstrings, type hints, a CLI. This is exactly what
AI-generated code looks like — clean surface, missing behavior. Now open
TASK.md and keep it next to you.
The course files are in courses/hands-on-python-with-claude-code/ now — no download happened on
your side. Ask the agent to survey them without changing anything:
“Read COURSE.md — it is the teaching contract for this folder. Then read TASK.md and starter/report_tool.py. Do not change any file. Tell me which behaviors the tests expect that the starter does not have.”
While it reads, run the learner’s command — your single check for the rest of the course:
python verify.py
You should see the starter reproduce five real-world failure classes plus two end-to-end consequences — seven named failing tests in all — mapped to test names:
| Failing test | Real-world meaning |
|---|---|
test_load_json_records_returns_list_of_dicts | the tool only handles CSV; the API scenario crashes |
test_unsupported_suffix_raises_value_error | an .xlsx upload becomes a confusing crash instead of a clear rejection |
test_invalid_records_are_isolated_with_reasons | one empty cell aborts the entire run — no partial report, no reason |
test_group_totals_are_rounded_to_two_decimals | 0.1 + 0.2 prints as 0.30000000000000004 in your report |
test_write_report_creates_missing_parent_directories | the tool cannot write into a fresh output directory |
test_run_scenario_writes_report_file, test_main_prints_summary_and_returns_zero | end-to-end consequences of the above |
If you want the missing behaviors as exact failing test names, the maintainer command prints them:
python verify.py starter --expect-failure
(The reference solution/ exists so maintainers can prove the target is
reachable. You never need to run it, and it is never the completion
standard — your implementation in starter/ is.)
That pair is your completion evidence for the whole course.
Pick the scenario skin you most identify with (excel-report,
data-monitor, or api-tool), open its data file, and find the rows that
will be rejected. Write down, before running anything, which test each bad row
will trigger. Then check yourself against the table above.
Run python verify.py in the course folder. You pass this lesson when you
can answer, without looking:
python verify.py exit non-zero on purpose — what state is
it reporting, and why is that a success condition rather than an error?This checkpoint’s claim code is already in the default command’s output — submit it only once you can answer the questions.
The transcript of the default command, and your three answers. Keep them; Lesson 4 will ask for them again.
Hints
That you can reproduce the failure before fixing it: python verify.py must show l03 and l04 as [open] (starter --expect-failure prints the named tests). The checkpoint is seeing the failure objectively, not repairing it.
Each failing test name maps to one line in TASK.md — e.g. test_non_numeric_amount_is_invalid is the contract's 'a non-numeric numeric_field value'. If a name maps to no contract line, that drift is worth noticing.
You can say out loud which nine behaviors the suite proves and why the starter cannot fake them. python verify.py then shows this checkpoint's claim code.
Submit
The 8-character code verify.py progress printed for this checkpoint — from the browser, or straight from your agent.
An account is only needed to keep points and badges — reading and solving stay free.
Hand it this checkpoint (copy button on hover) — it solves, verifies, and submits on its own:
Work on the FlyPython challenge "Hands-on Python with Claude Code" (course id course-claude-code), checkpoint l01.
Machine-readable brief: https://flypython.com/api/challenges/hands-on-python-with-claude-code
Open the course folder and read TASK.md first — it is the contract.
Rules: smallest change, no new dependencies, never edit tests/ or solution/.
Check with python verify.py until its gates pass, then report each claim code to me.
Target: solve only checkpoint l01, and submit it as soon as it passes.With a token from your agent page (env FLYPYTHON_TOKEN), it records the result directly:
curl -X POST https://flypython.com/api/claims \
-H "Authorization: Bearer $FLYPYTHON_TOKEN" \
-H "Content-Type: application/json" \
-d '{"claims":[{"course":"hands-on-python-with-claude-code","checkpoint":"l01","code":"<8-char code>"}]}'Or install the full FlyPython agent skill once and skip the paste.
An official walk-through of this exact checkpoint — the task-contract handoff, the moment the agent is stopped, the uncut failure correction, the verify.py gates. Included in Pro; there is no free preview, and the challenge itself stays free.
Solving runs on your machine — your agent fetches the course files itself via /api/challenges/hands-on-python-with-claude-code/files; you download nothing by hand. Prefer reading the source? Browse the folder on GitHub ↗