Initializes an AI development session by reading workflow guides, developer identity, git status, active tasks, and project guidelines from .trellis/. Classifies incoming tasks and routes to brainstorm, direct edit, or task workflow. Use when beginning a new coding session, resuming work, starting a new task, or re-establishing project context.
Initialize your AI development session and begin working on tasks.
| Marker | Meaning | Executor |
|---|---|---|
[AI] | Bash scripts or tool calls executed by AI | You (AI) |
[USER] | Skills executed by user | User |
[AI]First, read the workflow guide to understand the development process:
cat .trellis/workflow.md
Follow the instructions in workflow.md - it contains:
python3 ./.trellis/scripts/get_context.py
This shows: developer identity, git status, current task (if any), active tasks.
python3 ./.trellis/scripts/get_context.py --mode packages
This shows available packages and their spec layers. Read the relevant spec indexes:
cat .trellis/spec/<package>/<layer>/index.md # Package-specific guidelines
cat .trellis/spec/guides/index.md # Thinking guides (always read)
Important: The index files are navigation — they list the actual guideline files (e.g.,
error-handling.md,conventions.md,mock-strategies.md). At this step, just read the indexes to understand what's available. When you start actual development, you MUST go back and read the specific guideline files relevant to your task, as listed in the index's Pre-Development Checklist.
Report what you learned and ask: "What would you like to work on?"
When user describes a task, classify it:
| Type | Criteria | Workflow |
|---|---|---|
| Question | User asks about code, architecture, or how something works | Answer directly |
| Trivial Fix | Typo fix, comment update, single-line change, < 5 minutes | Direct Edit |
| Simple Task | Clear goal, 1-2 files, well-defined scope | Quick confirm → Task Workflow |
| Complex Task | Vague goal, multiple files, architectural decisions | Brainstorm → Task Workflow |
If in doubt, use Brainstorm + Task Workflow.
Task Workflow ensures code-specs are injected to the right context, resulting in higher quality code. The overhead is minimal, but the benefit is significant.
Subtask Decomposition: If brainstorm reveals multiple independent work items, consider creating subtasks using
--parentflag oradd-subtaskcommand. See the brainstorm skill's Step 8 for details.
For questions or trivial fixes, work directly:
$finish-workFor simple, well-defined tasks:
For complex or vague tasks, automatically start the brainstorm process — do NOT skip directly to implementation.
See $brainstorm for the full process. Summary:
prd.mdWhy this workflow?
From Brainstorm (Complex Task):
PRD confirmed → Research → Configure Context → Activate → Implement → Check → Complete
From Simple Task:
Confirm → Create Task → Write PRD → Research → Configure Context → Activate → Implement → Check → Complete
Key principle: Research happens AFTER requirements are clear (PRD exists).
PRD and task directory already exist from brainstorm. Skip directly to Phase 2.
Step 1: Confirm Understanding [AI]
Quick confirm:
If unclear, ask clarifying questions.
Step 2: Create Task Directory [AI]
TASK_DIR=$(python3 ./.trellis/scripts/task.py create "<title>" --slug <name>)
Step 3: Write PRD [AI]
Create prd.md in the task directory with:
# <Task Title>
## Goal
<What we're trying to achieve>
## Requirements
- <Requirement 1>
- <Requirement 2>
## Acceptance Criteria
- [ ] <Criterion 1>
- [ ] <Criterion 2>
## Technical Notes
<Any technical decisions or constraints>
Both paths converge here. PRD and task directory must exist before proceeding.
Step 4: Code-Spec Depth Check [AI]
If the task touches infra or cross-layer contracts, do not start implementation until code-spec depth is defined.
Trigger this requirement when the change includes any of:
Must-have before proceeding:
Step 5: Research the Codebase [AI]
Based on the confirmed PRD, run a focused research pass and produce:
.trellis/spec/Use this output format:
## Relevant Specs
- <path>: <why it's relevant>
## Code Patterns Found
- <pattern>: <example file path>
## Files to Modify
- <path>: <what change>
Step 6: Configure Context [AI]
Initialize default context:
python3 ./.trellis/scripts/task.py init-context "$TASK_DIR" <type>
# type: backend | frontend | fullstack
Add specs found in your research pass:
# For each relevant spec and code pattern:
python3 ./.trellis/scripts/task.py add-context "$TASK_DIR" implement "<path>" "<reason>"
python3 ./.trellis/scripts/task.py add-context "$TASK_DIR" check "<path>" "<reason>"
Step 7: Activate Task [AI]
python3 ./.trellis/scripts/task.py start "$TASK_DIR"
This sets .current-task so hooks can inject context.
Step 8: Implement [AI]
Implement the task described in prd.md.
Step 9: Check Quality [AI]
Run a quality pass against check context:
Step 10: Complete [AI]
$record-session to record this sessionIf get_context.py shows a current task:
prd.md to understand the goaltask.json for current status and phaseIf yes, resume from the appropriate step (usually Step 7 or 8).
[USER]| Skill | When to Use |
|---|---|
$start | Begin a session (this skill) |
$finish-work | Before committing changes |
$record-session | After completing a task |
[AI]| Script | Purpose |
|---|---|
python3 ./.trellis/scripts/get_context.py | Get session context |
python3 ./.trellis/scripts/task.py create | Create task directory |
python3 ./.trellis/scripts/task.py init-context | Initialize jsonl files |
python3 ./.trellis/scripts/task.py add-context | Add spec to jsonl |
python3 ./.trellis/scripts/task.py start | Set current task |
python3 ./.trellis/scripts/task.py finish | Clear current task |
python3 ./.trellis/scripts/task.py archive | Archive completed task |
[AI]| Phase | Purpose | Context Source |
|---|---|---|
| research | Analyze codebase | direct repo inspection |
| implement | Write code | implement.jsonl |
| check | Review & fix | check.jsonl |
| debug | Fix specific issues | debug.jsonl |
Code-spec context is injected, not remembered.
The Task Workflow ensures agents receive relevant code-spec context automatically. This is more reliable than hoping the AI "remembers" conventions.