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ai-workflow-course/modules/09-issues-and-the-task-layer/lab/example-issues.md
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justin 3221f7abe8
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Use python3 as the canonical command name course-wide (#104)
Most current systems (default Debian/Ubuntu, recent macOS) install Python
only as `python3`, with no bare `python` on PATH, so learners who copied
`python cli.py ...` into their host shell hit "command not found".

Convert host-shell `python <cmd>` -> `python3 <cmd>` across module/lab
READMEs, lab `.py` docstrings & usage strings, blog posts, lab prompt and
instruction files, the M04 verify.sh message, and the M10/M24 lab patches.
Module 01's convention note (and its blog/02 mirror) is rewritten so
`python3` is canonical and `python` is the documented fallback.

Stop-lines respected: Docker image tags (`python:3.12-slim`), `.venv/.../python`
and `...\.venv\Scripts\python.exe` paths, the M20 `"command": "python"`
teaching example and surrounding venv prose, container-internal invocations
(M16/M18 Dockerfiles, M16 README `docker run` examples), and CI-workflow
`run:` steps fed by `actions/setup-python` / `image: python:3.12` are left
as `python` on purpose.

pip was left out of scope: most occurrences are prose or CI/container-internal,
and `pip3` does not fix the PEP 668 externally-managed-environment refusal that
the course already addresses with venvs. The M01 note is worded to stay
consistent with bare `pip` (use whichever pip pairs with your Python).

Build (tools/build_wiki.py) and tools/check.sh both pass.

Closes #104

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GAEzanEoGJT5o1VizQar47
2026-06-23 20:18:04 -04:00

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<!--
Worked example issues for the tasks-app, Module 9 of "The Workflow".
These are a reference / answer key. Write your OWN three issues from issue-template.md FIRST, then
compare. Yours don't need to match word for word; check that each has a specific title, real
context (with repro for the bug), concrete acceptance criteria, and a stated scope.
Note how the routing call is a property of the ISSUE (clear vs. ambiguous), not the model.
Because the tasks-app carries forward across modules, some commands you might reach for (a
`delete` command, task priorities) may already exist from earlier labs. These examples
deliberately target work the app does NOT have yet, so each reads as a genuine open issue.
-->
# Issue 1: bug, route to AGENT
# Title: `done` command crashes on an out-of-range or non-integer index
## Context / problem
`python3 cli.py done 99` on a list with 3 tasks raises an uncaught `IndexError` and dumps a Python
traceback. `python3 cli.py done abc` raises `ValueError` the same way. The user sees a stack trace
instead of a helpful message, and the process exits as if it crashed.
Reproduce:
```
python3 cli.py add "first"
python3 cli.py done 99 # IndexError traceback
python3 cli.py done abc # ValueError traceback
```
## Acceptance criteria
- [ ] `done <index>` with an out-of-range index prints a clear message (e.g. `no task at index 99`)
and exits non-zero, with no traceback.
- [ ] `done <non-integer>` prints a clear message and exits non-zero, with no traceback.
- [ ] A valid `done <index>` still marks the task done exactly as before.
## Out of scope
Changing how tasks are stored, numbered, or displayed.
---
- **Type:** bug
- **Priority:** high
- **Ready:** yes
- **Route to:** agent. Contained, reproducible, and verifiable in seconds; clear acceptance criteria
mean an agent's first pass is very likely correct.
# Issue 2: feature, route to AGENT
# Title: Add an `undone <index>` command to mark a completed task as not done
## Context / problem
You can mark a task `done`, but there's no way to undo it; flag the wrong index by mistake and the
only "fix" is to delete the task and re-add it. The command should mirror the existing `done <index>`
command, which already takes an index and flips a task's state; this is simply its inverse.
## Acceptance criteria
- [ ] `python3 cli.py undone <index>` clears the done flag on the task at that index and saves.
- [ ] `undone` with an out-of-range or non-integer index prints a clear error and exits non-zero
(same behavior as the fixed `done`, see Issue 1).
- [ ] `list` after `undone` shows that task as not done (`[ ]`).
- [ ] Usage text mentions the new `undone` command.
## Out of scope
A general multi-step undo / command history (separate concern). Changing the storage format.
## Proposed approach (optional)
Add a `reopen(index)` method on `TaskList` in `tasks.py` (the inverse of the existing `complete`)
and wire an `undone` branch in `cli.py`, parallel to the existing `done` handling.
---
- **Type:** feature
- **Priority:** med
- **Ready:** yes
- **Route to:** agent. Well-scoped and patterned directly on existing code (the inverse of `done`);
low ambiguity, easy to verify.
# Issue 3: feature, route to HUMAN
# Title: Support due dates on tasks
## Context / problem
Users want to attach a due date to a task so the list can reflect what's coming up, not just what
exists. Today a task is only a title and a done flag. This is desirable but underspecified; several
product decisions have to be made before any code is written.
Open questions (resolve before this is `ready`):
- What date format does the user type, and how forgiving is parsing? (ISO `2026-06-30` only, or
relative like `tomorrow` / `friday`?)
- Does `list` re-sort by due date, group by it, or just display it inline?
- How is a due date set: at `add` time (a flag?) or with a separate command? Can it be cleared?
- How are overdue tasks surfaced (highlighted, flagged, sorted to the top), and in whose timezone?
- How is it stored, and what's the default for the existing tasks that have none?
## Acceptance criteria
- [ ] (Cannot be written yet; depends on the decisions above. Likely splits into 2-3 smaller,
agent-ready issues once the design is settled.)
## Out of scope
TBD until the design questions are answered.
---
- **Type:** feature
- **Priority:** low
- **Ready:** no
- **Route to:** human. Genuine design ambiguity. An agent would answer these questions confidently
and probably wrongly. A person decides the design, then splits this into clear sub-issues (which
may then be agent-ready).