6.2 KiB
NightShift Quickstart
This guide runs the current MVP with safe example files.
1. Install for Development
pip install -e .
Or run the module directly:
python -m nightshift.cli --help
2. Create Starter Files
From a project directory:
nightshift init
This creates:
nightshift.yaml
tasks.md
agents/
Existing starter files are not overwritten unless you pass --force.
3. Validate
nightshift validate
Validation checks config structure, task parsing, prompt files, scoped paths, and command safety.
4. Run One Task
Run the next incomplete task:
nightshift run
Run a specific task:
nightshift run --task TASK-001
5. Review Artifacts
After a run, inspect:
.nightshift/runs/<run-id>/
Useful files:
run-summary.md
config.snapshot.yaml
tasks/TASK-001/task.md
tasks/TASK-001/context.md
tasks/TASK-001/plan.md
tasks/TASK-001/test-output.txt
tasks/TASK-001/stage-results.md
tasks/TASK-001/context-out.md
tasks/TASK-001/final-notes.md
Example Templates
Example run files are available in templates/.
They are safe starter examples and use command-backed fake agents.
The repository also includes a complete sample target project:
examples/quickstart-lisp/
Copy that directory elsewhere if you want to test NightShift against a multi-task project.
Quickstart Test Project
A good first real target project is a tiny Lisp interpreter in Python. It is small enough to review, but it naturally breaks into multiple tasks that test NightShift's planning, implementation, command execution, artifacts, reports, and dependency handling.
If you do not want a language interpreter, use a small config parser or markdown todo CLI instead. The Lisp interpreter is the recommended default because it has clear incremental milestones and simple tests.
1. Create a Target Project
mkdir tiny-lisp
cd tiny-lisp
mkdir agents tests
touch lisp.py tests/test_lisp.py
2. Add nightshift.yaml
project:
name: tiny-lisp
root: .
task_file: tasks.md
artifact_dir: .nightshift
safety:
require_clean_worktree: false
scoped_paths:
- .
allowed_commands:
- python -m unittest discover -v
forbidden_commands:
- rm -rf
- git push
- curl | bash
agents:
planner:
backend: command
command: echo
system_prompt: agents/planner.md
implementer:
backend: command
command: echo
system_prompt: agents/implementer.md
reviewer:
backend: command
command: python -c "print('status: pass'); print('reason: quickstart reviewer accepted artifacts')"
system_prompt: agents/reviewer.md
pipeline:
max_task_retries: 1
continue_on_task_failure: false
stages:
- id: plan
type: agent
agent: planner
output: plan.md
- id: implement
type: agent
agent: implementer
output: implementation-log.md
- id: test
type: command
commands:
- python -m unittest discover -v
output: test-output.txt
- id: review
type: agent_review
agent: reviewer
on_fail: implement
output: review.md
- id: summarize
type: summarize
output: final-notes.md
This uses fake command agents so the pipeline is safe and deterministic. Replace command: echo later with your real local agent wrapper.
3. Add tasks.md
# Tasks
- [ ] TASK-001: Parse Lisp expressions
Description:
Implement tokenization and parsing for a tiny Lisp subset.
Acceptance Criteria:
- Parses numbers
- Parses symbols
- Parses nested lists
- Raises useful errors for unbalanced parentheses
- Includes unit tests
- [ ] TASK-002: Evaluate arithmetic forms
Dependencies:
- TASK-001
Description:
Evaluate parsed arithmetic expressions.
Acceptance Criteria:
- Supports `+`, `-`, `*`, and `/`
- Evaluates nested arithmetic
- Includes unit tests
- [ ] TASK-003: Add variables and definitions
Dependencies:
- TASK-002
Description:
Add an environment and support variable lookup and definitions.
Acceptance Criteria:
- Supports symbol lookup
- Supports `(define name value)`
- Keeps environment behavior tested
- [ ] TASK-004: Add conditionals
Dependencies:
- TASK-003
Description:
Implement simple truthiness and `if` expressions.
Acceptance Criteria:
- Supports `(if condition then else)`
- Handles false-like values consistently
- Includes tests for both branches
4. Add Prompt Files
agents/planner.md:
You are the planning agent. Create a small, conservative plan for the task.
Do not write code. Include files to edit, tests to add, and risks.
agents/implementer.md:
You are the implementation agent. Implement the smallest correct change.
Preserve existing behavior and include tests.
agents/reviewer.md:
You are the review agent. Decide whether the task should pass, retry, or fail.
Output:
status: pass | fail | retry | escalate
reason: <short explanation>
next_stage: <optional stage id>
context_update: <compact useful note>
5. Add an Initial Passing Test File
# tests/test_lisp.py
import unittest
class SmokeTests(unittest.TestCase):
def test_smoke(self):
self.assertTrue(True)
if __name__ == "__main__":
unittest.main()
6. Validate and Run
nightshift validate
nightshift status
nightshift run --task TASK-001
Run all currently runnable tasks:
nightshift run --all
Because the example uses fake agents, it will not actually implement the Lisp interpreter by itself. It is meant to verify the pipeline, dependency handling, reports, and artifacts before you connect a real command-backed agent.
7. Review Artifacts
After a run, inspect:
.nightshift/runs/<run-id>/run-summary.md
.nightshift/runs/<run-id>/tasks/TASK-001/plan.md
.nightshift/runs/<run-id>/tasks/TASK-001/implementation-log.md
.nightshift/runs/<run-id>/tasks/TASK-001/test-output.txt
.nightshift/runs/<run-id>/tasks/TASK-001/review.md
.nightshift/runs/<run-id>/tasks/TASK-001/final-notes.md
The useful signal is whether NightShift selected the right task, respected dependencies, ran the command stage, wrote artifacts, updated task completion, and produced a clear summary.