ModeScheduler

An interactive CLI tool for managing action plans for OSA

Builds and manages a task plan for a selected repository. The plan can be:

• basic — a predefined set of tasks
• auto — generated by an AI model based on the repository contents
• advanced — adjusted by the user during CLI execution

The CLI allows user to:

• View the generated plan
• Confirm or cancel execution
• Edit the plan interactively
• Manage GitHub Actions workflows alongside other tasks

---

How it works?

Here is a short demo:

---

Usage

Run Open-Source-Advisor using the following command:

python -m osa_tool.run -r {repository} [--mode {mode}] [--api {api}] [--base-url {base_url}] [--model {model_name}]

Required Argument

-r, --repository - URL of the repository to be analyzed and processed.

Available Modes

Before starting, the user needs to specify which mode they want to use:

Mode	Description
basic	Runs a predefined set of actions: generates Report, README, Community docs, About section. Organize the repository structure by adding standard 'tests' and 'examples' directories if missing.
auto (default)	Automatically builds a plan using an AI model based on repository analysis.
advanced	Allows the user to manually select and configure which improvements and actions will be applied to the repository.

What Happens Next

Once the CLI command is execute, OSA will:

1. Analyze the repository
2. Display informational tables in the console:
   • Repository and environment info - general project details, active configuration
   • Planned actions — a table listing actions and improvements that are scheduled to be applied.
   • Inactive actions — a table showing actions that will not be applied based on the current plan.

Confirming the Action Plan

Do you want to proceed with these actions? [y/n/custom] (y)

• Enter y or simply press Enter — to accept the proposed plan and continue execution.
• Enter n — to cancel the operation. The program will immediately exit without making any changes
• Enter custom — to enter manual editing mode, where user can adjust the plan interactively.

Manual Plan Editing Mode

In custom mode:

• A list of editable actions is displayed.
• Available commands:
• help or ? — displays a table with descriptions of all editable keys.
• multi-bool — enables batch editing of boolean-type keys.
• done — finishes the editing session and displays the current plan.
• To edit an action, enter its name.
• For each selected action:
• If it is a boolean value (True/False), a prompt will request setting it to:
  • y — enable
  • n — disable
  • skip — leave unchanged
• If it is a string or list:
  • A new value can be entered.
  • Leave empty to skip.
  • Enter None to explicitly disable the value.
• Before entering a new value, the system will display:
  • A short description.
  • Allowed values (if available).
  • An example (if available).
  • The current value.

Once the user type done:

• The updated tables with planned actions and inactive actions will be displayed again.
• The program will prompt the user one more time to confirm the updated plan
• Available options:
• Confirm with y or Enter
• Cancel with n
• Re-enter manual editing mode with custom

This interactive loop continues until the user explicitly confirms or cancels the operation.

This mode allows for precise control over which improvements, documentation, or workflow-related actions will be applied to the repository.

Table of Available Keys

Parameter	Aliases	Type	Description	Default	Choices
repository	-r, --repository	str	URL of the GitHub repository	https://github.com/aimclub/OSA	—
mode	-m, --mode	str	Operation mode for repository processing: basic, auto (default), or advanced.	auto	basic, auto, advanced
branch	-b, --branch	str	Branch name of the GitHub repository	null	—
output	-o, --output	str	Path to the output directory	null	—
config_file	--config-file	str	Path to custom configuration file (TOML format)	null	—
use_single_model	--use-single-model	flag	Use the same model for all tasks (if disabled, use specific models for each task type)	true	—
api	--api	str	LLM API service provider	openai	itmo, openai, ollama
base_url	--base-url	str	URL of the service provider. See available urls	https://openrouter.ai/api/v1	—
model	--model	str	Specific LLM model to use. See available providers and models	gpt-3.5-turbo	—
model_docstring	--model-docstring	str	Specific LLM model for docstring generation tasks	Temprorary inherited from default model	—
model_readme	--model-readme	str	Specific LLM model for README generation tasks	Temprorary inherited from default model	—
model_validation	--model-validation	str	Specific LLM model for validation tasks	Temprorary inherited from default model	—
model_general	--model-general	str	Specific LLM model for general tasks	Temprorary inherited from default model	—
top_p	--top_p	str	Nucleus sampling probability	0.95	—
temperature	--temperature	str	Sampling temperature to use for the LLM output (0 = deterministic, 1 = creative).	0.05	—
max_tokens	--max_tokens	str	Maximum number of tokens the model can generate in a single response	4096	—
context_window	--context_window	str	Total number of model context (Input + Output)	16385	—
attachment	--attachment	str	Path to a local PDF or .docx file, or a URL to a PDF resource	null	—
translate_dirs	--translate-dirs	flag	Enable automatic translation of directory names into English	false	—
convert_notebooks	--convert-notebooks	list	Convert Jupyter notebooks to .py format. Provide paths, or leave empty for repo directory	—	—
delete_dir	--delete-dir	flag	Delete the downloaded repository after processing	false	—
ensure_license	--ensure-license	str	Enable LICENSE file compilation	null	bsd-3, mit, ap2
no_fork	--no-fork	flag	Do not create a public fork to the target repository	false	—
no_pull_request	--no-pull-request	flag	Do not create a pull request to the target repository	false	—
community_docs	--community-docs	flag	Generate community-related documentation files	false	—
docstring	--docstring	flag	Automatically generate docstrings for Python files	false	—
report	--report	flag	Analyze the repository and generate a PDF report	false	—
readme	--readme	flag	Generate a README.md file based on repository content	false	—
refine_readme	--refine-readme	flag	Enable advanced README refinement. This process requires a powerful LLM model (such as GPT-4 or equivalent) for optimal results	false	—
requirements	--requirements	flag	Generate or refine a requirements.txt file based on repository content.	false	—
organize	--organize	flag	Organize the repository by adding standard tests and examples directories if missing	false	—
about	--about	flag	Generate About section with tags	false	—
generate_workflows	--generate-workflows	flag	Generate GitHub Action workflows for the repository	false	—
workflows_output_dir	--workflows-output-dir	str	Directory where workflow files will be saved	.github/workflows	—
include_tests	--include-tests	flag	Include unit tests workflow	true	—
include_black	--include-black	flag	Include Black formatter workflow	true	—
include_pep8	--include-pep8	flag	Include PEP 8 compliance workflow	true	—
include_autopep8	--include-autopep8	flag	Include autopep8 formatter workflow	false	—
include_fix_pep8	--include-fix-pep8	flag	Include fix-pep8 command workflow	false	—
include_pypi	--include-pypi	flag	Include PyPI publish workflow	false	—
python_versions	--python-versions	list	Python versions to test against	[3.9, 3.10]	—
pep8_tool	--pep8-tool	str	Tool to use for PEP 8 checking	flake8	flake8, pylint
use_poetry	--use-poetry	flag	Use Poetry for packaging	false	—
branches	--branches	list	Branches to trigger workflows on	[]	—
codecov_token	--codecov-token	flag	Use Codecov token for coverage upload	false	—
include_codecov	--include-codecov	flag	Include Codecov coverage step in unit tests workflow	true	—
validate_paper	--validate-paper	flag	Check whether the experiments proposed in an attached research paper can be reproduced using the selected repository	false	—
validate_doc	--validate-doc	flag	Check whether the experiments proposed in an attached documentation file can be reproduced using the selected repository	false	—