huawei-cloud-study-plan-generate

Randomly generate a structured, configurable study-practice plan (学习实践表). Lays the plan out as day x time-slot cells, assigns every cell a study topic and a concrete practice item, keeps topics from repeating until the topic pool is exhausted, keeps a day's time slots non-overlapping, varies each session length inside a reasonable interval, and prints the result as a Markdown table (or plain text / JSON). A --seed flag makes any run reproducible. Implemented with the Python 3 standard library only; it does not call the Huawei Cloud CLI, any Huawei Cloud SDK, or any REST API, and it touches no cloud resource. Triggers include: "生成学习实践表", "随机生成学习计划", "学习安排", "帮我排学习计划", "生成学习表", "study plan", "random study plan", "study schedule", "learning plan", "practice plan".

Study Plan Generator

Overview

This skill produces a random, readable study-practice plan. By default it covers 7 days, each day with 3 time slots (morning / afternoon / evening), and every cell carries a study topic, a practice item, and a session length. It focuses on producing a plan that is random but reasonable:

• Constraint-respecting randomness — the same topic is not reused until the whole topic pool is exhausted; a day's time slots never overlap; session lengths stay inside a bounded interval around --session-minutes.
• Configurable — number of days, slots per day, topic pool, session length, start date and output format are all parameters.
• Reproducible — passing --seed makes a run deterministic; when omitted, a random seed is chosen and printed so the run can be reproduced later.
• Self-contained — pure Python 3 standard library, no network, no credentials.

Prerequisites

• Python 3.8+ available as python3 (or python) on PATH.
• No Huawei Cloud credentials, CLI (KooCLI), SDK, or network access are needed; this skill is entirely local.
• scripts/topics.json must be present (it ships with the skill) unless a custom topic pool is supplied with --topics.

Workflow

1. Load the topic pool — load_topics() reads scripts/topics.json and returns a list of {"topic", "practices"} entries; --topics may override it with a comma-separated topic list or a custom JSON pool.
2. Lay out the grid — build_plan(seed, days, slots_per_day, ...) walks each day and each selected time slot (kept in chronological order). Topics are drawn from a shuffled queue that is refilled only after it is exhausted, so no topic repeats until the pool is used up.
3. Fill each cell — a practice item is chosen at random from the topic's practices, and the session length is drawn from [session_minutes - 10, session_minutes + 10] (clamped to [15, 110], rounded to 5 minutes). All random choices come from random.Random(seed).
4. Render — render_markdown() / render_text() / render_json() turn the plan into readable output (Markdown table by default).
5. Emit — the result goes to standard output, or to a file when --output is given.

Core Commands

bash

# Default: a 7-day plan (3 slots per day), reproducible with the given seed
python3 scripts/generate_study_plan.py --seed 20240501 --start-date 2026-10-24

# Plain text
python3 scripts/generate_study_plan.py --seed 42 --format text

# JSON (machine-readable)
python3 scripts/generate_study_plan.py --seed 42 --format json

# Custom number of days, slots per day, and session length
python3 scripts/generate_study_plan.py --seed 7 --days 3 --slots-per-day 2 --session-minutes 30

# Override the topic pool with a comma-separated list of names
python3 scripts/generate_study_plan.py --seed 7 --topics "英语,算法,系统设计"

# Use a custom topic-pool JSON file and write the result to a file
python3 scripts/generate_study_plan.py --topics /path/to/topics.json --output plan.md

# Run the bundled unit tests
python3 -m unittest discover -s tests -v

The command-line entry point is scripts/generate_study_plan.py; the built-in scripts/topics.json is what the examples above read by default.

Parameter Confirmation

Parameter	Required	Description	Default
--days	No	Number of days to generate	7
--slots-per-day	No	Time slots per day (max 7)	3
--topics	No	Topic pool override: comma-separated names, or a JSON file path	built-in scripts/topics.json
--session-minutes	No	Nominal session length in minutes; actual length varies within ±10	45
--start-date	No	Start date YYYY-MM-DD	today
--seed	No	Integer seed for reproducible output	random (printed)
--format	No	Output format: markdown, text, or json	markdown
--output	No	Write the plan to a file instead of stdout	stdout

Reference Documents

Document	Description
references/usage-guide.md	Command recipes, reproducibility rules, extending the topic pool
references/verification-method.md	How the skill is verified (offline unit tests + spot checks)
references/acceptance-criteria.md	Acceptance criteria and out-of-scope items
references/dataflow-diagram.md	Mermaid data flow diagram
references/iam-policies.md	Permission notes (none required)

Notes

• The generator is read-only with respect to the cloud: it reads only its local topic pool and writes only to standard output or a user-chosen --output file.
• Output ordering: within a day, slots are shown in chronological order; days are labelled by ISO date plus a Chinese weekday.
• Session lengths are always within [15, 110] minutes and vary inside [session_minutes - 10, session_minutes + 10], so the plan stays "random but reasonable" and a day's slots never collide.
• To expand the pool, add {"topic", "practices"} entries to scripts/topics.json (each entry should keep at least one practice item); no code change is required.