trellis-brainstorm

name	trellis-brainstorm
description	Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex task.

Trellis Brainstorm

Non-Negotiable Interview Contract

Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.

Ask the questions one at a time.

Non-Negotiable Evidence Rule

If a question can be answered by exploring the codebase, explore the codebase instead.

This is mandatory. Before asking the user a question, first check whether the answer is already available in code, tests, configs, docs, existing specs, or task history.

Do not ask the user to confirm facts that the repository can answer. Ask only for product intent, preference, scope, risk tolerance, or decisions that remain ambiguous after inspection.

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Use this skill during Phase 1 planning to turn the user's request into clear requirements and planning artifacts.

Preconditions

Use this skill only after task-creation consent has been given and the user is ready to enter Trellis planning.

If no task exists yet, create one:

TASK_DIR=$(python3 ./.trellis/scripts/task.py create "<short task title>" --slug <slug>)

Use a concise title from the user's request. Use a slug without a date prefix. task.py create adds the MM-DD- directory prefix automatically.

task.py create creates the default prd.md. Update that file with the current understanding before asking follow-up questions.

Planning Flow

1. Capture the user's request and initial known facts in prd.md.
2. Inspect available evidence before asking questions:
   • code, tests, fixtures, and configs
   • README files, docs, existing specs, and domain notes
   • related Trellis tasks, research files, and session history when present
3. Separate what you found into:
   • confirmed facts
   • product intent still needed from the user
   • scope or risk decisions still needed from the user
   • likely out-of-scope items
4. Ask the single highest-value remaining question.
5. Include your recommended answer with the question.
6. After each user answer, update prd.md before continuing.
7. For complex tasks, create or update design.md and implement.md before implementation starts.
8. Before final review or task.py start, run the PRD convergence pass below.

Do not invent a project-specific product/spec hierarchy. If the repository already has product, domain, or spec docs, use them. If it does not, proceed with the evidence that exists.

Question Rules

Ask only one question per message.

Each question must include:

• the decision needed
• why the answer matters
• your recommended answer
• the trade-off if the user chooses differently

Do not ask process questions such as whether to search, inspect files, or continue brainstorming. Do the evidence work directly. Ask the user only when the remaining issue is a product decision, preference, scope boundary, or risk tolerance choice.

Thinking Framework: First Principles Analysis

When requirements are vague, solutions feel over-engineered, or you're about to add complexity "because everyone does" — decompose to fundamental truths before reasoning upward.

Step 1: Restate the Problem

Strip away implementation details to one sentence.

Bad: "We need to add Redis caching to the user profile endpoint" Good: "User profile data takes too long to load"

Step 2: List Fundamental Truths

What is absolutely true (not opinion or convention)?

Category	Examples
Physical constraints	Network latency ≥ 0, disk I/O has limits
Business rules	"Users must see their own data"
Technical invariants	"Data must be consistent"
User needs	"The user wants X within Y seconds"

Step 3: Challenge Assumptions

For each component of the current plan:

• Fact or convention? "We always use REST" — why?
• What if we removed this? If nothing breaks, it's unnecessary.
• Solving the actual problem or a symptom? Trace the causal chain.
• Who benefits from this complexity? If "nobody", simplify.

Step 4: Build Up from Truths

1. Start with the minimum viable mechanism satisfying all truths
2. Add complexity only when a specific truth demands it
3. Each addition must answer: "Which truth requires this?"

Step 5: Validate

• Does the solution solve the original problem?
• What assumptions need verification?
• What's the simplest experiment to test this?

Artifact Rules

prd.md records requirements and acceptance:

• goal and user value
• confirmed facts
• requirements
• acceptance criteria
• out of scope
• open questions that still block planning

design.md records technical design for complex tasks:

• architecture and boundaries
• data flow and contracts
• compatibility and migration notes
• important trade-offs
• operational or rollback considerations

implement.md records execution planning for complex tasks:

• ordered implementation checklist
• validation commands
• risky files or rollback points
• follow-up checks before task.py start

Guru Team requires prd.md, design.md, and implement.md for every task before implementation. Some native Trellis workflows may allow a smaller planning set, but that is not the Guru Team start gate. After all three documents exist, perform the planning artifact ambiguity review required by .trellis/workflow.md, then display task-local links to all three, wait for explicit post-planning confirmation, record/check schema 1.2 planning-approval.json with passed ambiguity_review evidence, fixed-scope scanner hits[], empty unchecked_normative_hits[], and only then run task.py start.

Planning artifact normative language must be deterministic. For requirements, design contracts, gates, acceptance criteria, implementation steps, and validation steps, rewrite unconditional weak wording into explicit trigger conditions or deterministic requirements. Treat the controlled weak constraint terms in .trellis/workflow.md as review inputs, not as execution contract language. External quotes or history may keep their original wording only when the source is labeled and the quote is not treated as the task contract.

implement.md is not a replacement for implement.jsonl. On sub-agent-dispatch workflows, implement.jsonl and check.jsonl must each contain at least one real spec/research entry before task.py start; the seed _example row does not count. Inline workflows skip this JSONL gate because Phase 2 loads context through trellis-before-dev.

PRD Convergence Pass

Before declaring planning ready or running task.py start, rewrite prd.md once against the final structure described in the artifact rules above. This is not optional cleanup; it is the final planning gate.

The pass must be lossless:

• Collapse repeated facts into one authoritative section.
• Fold temporary brainstorm sections such as What I already know, Assumptions, and resolved Open Questions into Goal, Background, Requirements, Technical Notes, or Acceptance Criteria.
• Remove resolved open questions instead of leaving empty or already-answered sections.
• Merge parallel bug and requirement lists when they describe the same work; keep each defect's severity, evidence, and file:line anchors on the owning requirement.
• Preserve every file:line anchor, decision, constraint, requirement ID, and acceptance-criteria mapping.
• Keep only genuinely blocking open questions.

After the pass, read prd.md top to bottom and verify that no fact is repeated across sections unless the repetition adds new information.

Quality Bar

Before declaring planning ready:

• prd.md contains testable acceptance criteria.
• prd.md has passed the PRD convergence pass: no unresolved temporary brainstorm sections, no duplicate facts across sections, and no lost anchors, decisions, or acceptance mappings.
• Repository-answerable questions have already been answered through inspection.
• Remaining open questions are genuinely about user intent or scope.
• Guru Team tasks have design.md and implement.md before implementation.
• Planning artifact ambiguity review has passed before links are displayed for post-planning approval.
• Sub-agent-dispatch tasks have real curated entries in both implement.jsonl and check.jsonl; seed-only manifests are not ready.
• The user has reviewed the final prd.md, design.md, and implement.md links and explicitly confirmed post-planning approval.

Do not start implementation until the user approves or asks for implementation.