.agents/skills/brainstorm/SKILL.md
Collaborative requirements discovery session optimized for AI coding workflows. Creates task directories, seeds PRDs, runs codebase research, proposes concrete implementation approaches with trade-offs, and converges on MVP scope through structured Q&A. Use when requirements are unclear, multiple implementation paths exist, trade-offs need evaluation, or a complex feature needs scoping before development.
npx skillsauth add basui01/agentflow brainstormInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Guide AI through collaborative requirements discovery before implementation, optimized for AI coding workflows:
Triggered from $start when the user describes a development task, especially when:
Task-first (capture early) Always ensure a task exists at the start so the user's ideas are recorded immediately.
Action before asking If you can derive the answer from repo code, docs, configs, conventions, or quick research — do that first.
One question per message Never overwhelm the user with a list of questions. Ask one, update PRD, repeat.
Prefer concrete options For preference/decision questions, present 2–3 feasible, specific approaches with trade-offs.
Research-first for technical choices If the decision depends on industry conventions / similar tools / established patterns, do research first, then propose options.
Diverge → Converge After initial understanding, proactively consider future evolution, related scenarios, and failure/edge cases — then converge to an MVP with explicit out-of-scope.
No meta questions Do not ask "should I search?" or "can you paste the code so I can continue?" If you need information: search/inspect. If blocked: ask the minimal blocking question.
Before any Q&A, ensure a task exists. If none exists, create one immediately.
TASK_DIR=$(python3 ./.trellis/scripts/task.py create "brainstorm: <short goal>" --slug <auto>)
Create/seed prd.md immediately with what you know:
# brainstorm: <short goal>
## Goal
<one paragraph: what + why>
## What I already know
* <facts from user message>
* <facts discovered from repo/docs>
## Assumptions (temporary)
* <assumptions to validate>
## Open Questions
* <ONLY Blocking / Preference questions; keep list short>
## Requirements (evolving)
* <start with what is known>
## Acceptance Criteria (evolving)
* [ ] <testable criterion>
## Definition of Done (team quality bar)
* Tests added/updated (unit/integration where appropriate)
* Lint / typecheck / CI green
* Docs/notes updated if behavior changes
* Rollout/rollback considered if risky
## Out of Scope (explicit)
* <what we will not do in this task>
## Technical Notes
* <files inspected, constraints, links, references>
* <research notes summary if applicable>
Before asking questions like "what does the code look like?", gather context yourself:
Write findings into PRD:
What I already knowTechnical Notes| Complexity | Criteria | Action | | ------------ | ------------------------------------------------------ | ------------------------------------------- | | Trivial | Single-line fix, typo, obvious change | Skip brainstorm, implement directly | | Simple | Clear goal, 1–2 files, scope well-defined | Ask 1 confirm question, then implement | | Moderate | Multiple files, some ambiguity | Light brainstorm (2–3 high-value questions) | | Complex | Vague goal, architectural choices, multiple approaches | Full brainstorm |
Note: Task already exists from Step 0. Classification only affects depth of brainstorming.
Before asking ANY question, run the following gate:
If answer is available via:
→ Do not ask. Fetch it, summarize, update PRD.
Examples:
→ Do not ask. Take action.
→ Only ask Blocking or Preference.
Add a section in PRD (either within Technical Notes or as its own):
## Research Notes
### What similar tools do
* ...
* ...
### Constraints from our repo/project
* ...
### Feasible approaches here
**Approach A: <name>** (Recommended)
* How it works:
* Pros:
* Cons:
**Approach B: <name>**
* How it works:
* Pros:
* Cons:
**Approach C: <name>** (optional)
* ...
Then ask one preference question:
After you can summarize the goal, proactively broaden thinking before converging.
Future evolution
Related scenarios
Failure & edge cases
I understand you want to implement: <current goal>.
Before diving into design, let me quickly diverge to consider three categories (to avoid rework later):
1. Future evolution: <1–2 bullets>
2. Related scenarios: <1–2 bullets>
3. Failure/edge cases: <1–2 bullets>
For this MVP, which would you like to include (or none)?
1. Current requirement only (minimal viable)
2. Add <X> (reserve for future extension)
3. Add <Y> (improve robustness/consistency)
4. Other: describe your preference
Then update PRD:
RequirementsOut of ScopeOne question per message
Prefer multiple-choice when possible
After each user answer:
Open Questions → RequirementsAcceptance Criteria with testable checkboxesOut of ScopeFor <topic>, which approach do you prefer?
1. **Option A** — <what it means + trade-off>
2. **Option B** — <what it means + trade-off>
3. **Option C** — <what it means + trade-off>
4. **Other** — describe your preference
After requirements are clear enough, propose 2–3 approaches (if not already done via research-first):
Based on current information, here are 2–3 feasible approaches:
**Approach A: <name>** (Recommended)
* How:
* Pros:
* Cons:
**Approach B: <name>**
* How:
* Pros:
* Cons:
Which direction do you prefer?
Record the outcome in PRD as an ADR-lite section:
## Decision (ADR-lite)
**Context**: Why this decision was needed
**Decision**: Which approach was chosen
**Consequences**: Trade-offs, risks, potential future improvements
When open questions are resolved, confirm complete requirements with a structured summary:
Here's my understanding of the complete requirements:
**Goal**: <one sentence>
**Requirements**:
* ...
* ...
**Acceptance Criteria**:
* [ ] ...
* [ ] ...
**Definition of Done**:
* ...
**Out of Scope**:
* ...
**Technical Approach**:
<brief summary + key decisions>
**Implementation Plan (small PRs)**:
* PR1: <scaffolding + tests + minimal plumbing>
* PR2: <core behavior>
* PR3: <edge cases + docs + cleanup>
Does this look correct? If yes, I'll proceed with implementation.
For complex tasks with multiple independent work items, create subtasks:
# Create child tasks
CHILD1=$(python3 ./.trellis/scripts/task.py create "Child task 1" --slug child1 --parent "$TASK_DIR")
CHILD2=$(python3 ./.trellis/scripts/task.py create "Child task 2" --slug child2 --parent "$TASK_DIR")
# Or link existing tasks
python3 ./.trellis/scripts/task.py add-subtask "$TASK_DIR" "$CHILD_DIR"
prd.md should converge to:
# <Task Title>
## Goal
<why + what>
## Requirements
* ...
## Acceptance Criteria
* [ ] ...
## Definition of Done
* ...
## Technical Approach
<key design + decisions>
## Decision (ADR-lite)
Context / Decision / Consequences
## Out of Scope
* ...
## Technical Notes
<constraints, references, files, research notes>
After brainstorm completes (Step 8 confirmation approved), the flow continues to the Task Workflow's Phase 2: Prepare for Implementation:
Brainstorm
Step 0: Create task directory + seed PRD
Step 1–7: Discover requirements, research, converge
Step 8: Final confirmation → user approves
↓
Task Workflow Phase 2 (Prepare for Implementation)
Code-Spec Depth Check (if applicable)
→ Research codebase (based on confirmed PRD)
→ Configure code-spec context (jsonl files)
→ Activate task
↓
Task Workflow Phase 3 (Execute)
Implement → Check → Complete
The task directory and PRD already exist from brainstorm, so Phase 1 of the Task Workflow is skipped entirely.
| Command | When to Use |
|---------|-------------|
| $start | Entry point that triggers brainstorm |
| $finish-work | After implementation is complete |
| $update-spec | If new patterns emerge during work |
development
Multi-agent pipeline orchestrator that plans and dispatches parallel development tasks to worktree agents. Reads project context, configures task directories with PRDs and jsonl context files, and launches isolated coding agents. Use when multiple independent features need parallel development, orchestrating worktree agents, or managing multi-agent coding pipelines.
development
Captures executable contracts and coding knowledge into .trellis/spec/ documents after implementation, debugging, or design decisions. Enforces code-spec depth for infra and cross-layer changes with mandatory sections for signatures, contracts, validation matrices, and test points. Use when a feature is implemented, a bug is fixed, a design decision is made, a new pattern is discovered, or cross-layer contracts change.
development
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.
development
Records completed work progress to .trellis/workspace/ journal files after human testing and commit. Captures session summaries, commit hashes, and updates developer index files for future session context. Use when a coding session is complete, after the human has committed code, or to persist session knowledge for future AI sessions.