Agent skill

Brainstorm

by jlifeng in jlifeng/JobPilot

Brainstorm - Requirements Discovery (AI Coding Enhanced). An agent skill from jlifeng/JobPilot.

Apache-2.0Auto-check passedAgent Workflows

Install Brainstorm

skills CLI
$ npx skills add jlifeng/JobPilot --skill brainstorm -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jlifeng/JobPilot brainstorm --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jlifeng/JobPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/brainstorm .claude/skills/brainstorm && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
brainstorm
GitHub stars
140
Token cost
~3.1k tokens
SKILL.md length
1,019 words
Files
1
Skills in repo
5
Repo updated
First seen
Licence
Apache-2.0

At a glance

Brainstorm - Requirements Discovery (AI Coding Enhanced). An agent skill from jlifeng/JobPilot.

  • Works in 9 steps: Ensure Task Exists (ALWAYS) → Auto-Context (DO THIS BEFORE ASKING… → Classify Complexity (still useful, not… → …
  • Tasks that involve Brainstorming
  • SKILL.md covers When to Use, Core Principles (Non-negotiable), Step 0: Ensure Task Exists… and Step 1: Auto-Context (DO THIS…, plus 10 more sections
  • Calls python3

What it does

Brainstorm is an agent skill from jlifeng/JobPilot. Brainstorm - Requirements Discovery (AI Coding Enhanced)

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Brainstorming. The repository describes itself as: JobPilot — Zero-Deployment AI Resume Builder, JD Matcher & Mock Interview Assistant JobPilot — 开箱即用的桌面端 AI 求职助手,支持简历优化、岗位匹配与模拟面试. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Brainstorming

Example prompts

  • “/brainstorm”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Ensure Task Exists (ALWAYS)
  2. Auto-Context (DO THIS BEFORE ASKING QUESTIONS)
  3. Classify Complexity (still useful, not gating task creation)
  4. Question Gate (Ask ONLY high-value questions)
  5. Research-first Mode (Mandatory for technical choices)
  6. Expansion Sweep (DIVERGE) — Required after initial understanding
  7. Q&A Loop (CONVERGE)
  8. Propose Approaches + Record Decisions (Complex tasks)
  9. Final Confirmation + Implementation Plan

What it can do on your machine

Read from SKILL.md and the folder at commit 7f33542. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Brainstorm loads about 3.1k tokens when it runs. Until then it costs about 17 tokens; SKILL.md has 1,019 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~17
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jlifeng/JobPilot at commit 7f33542, republished under its Apache-2.0 licence (© jlifeng). 1,019 words, ~3,101 tokens.

Download SKILL.mdSave it as .claude/skills/brainstorm/SKILL.md (or your agent's skills folder).
name
brainstorm
description
Brainstorm - Requirements Discovery (AI Coding Enhanced)

Brainstorm - Requirements Discovery (AI Coding Enhanced)

Guide AI through collaborative requirements discovery before implementation, optimized for AI coding workflows:

  • Task-first (capture ideas immediately)
  • Action-before-asking (reduce low-value questions)
  • Research-first for technical choices (avoid asking users to invent options)
  • Diverge → Converge (expand thinking, then lock MVP)

When to Use

Triggered from $start when the user describes a development task, especially when:

  • requirements are unclear or evolving
  • there are multiple valid implementation paths
  • trade-offs matter (UX, reliability, maintainability, cost, performance)
  • the user might not know the best options up front

Core Principles (Non-negotiable)

  1. Task-first (capture early) Always ensure a task exists at the start so the user's ideas are recorded immediately.

  2. Action before asking If you can derive the answer from repo code, docs, configs, conventions, or quick research — do that first.

  3. One question per message Never overwhelm the user with a list of questions. Ask one, update PRD, repeat.

  4. Prefer concrete options For preference/decision questions, present 2–3 feasible, specific approaches with trade-offs.

  5. Research-first for technical choices If the decision depends on industry conventions / similar tools / established patterns, do research first, then propose options.

  6. 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.

  7. 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.


Step 0: Ensure Task Exists (ALWAYS)

Before any Q&A, ensure a task exists. If none exists, create one immediately.

  • Use a temporary working title derived from the user's message.
  • It's OK if the title is imperfect — refine later in PRD.
bash
TASK_DIR=$(python3 ./.trellis/scripts/task.py create "brainstorm: <short goal>" --slug <auto>)

Create/seed prd.md immediately with what you know:

markdown
# 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>

Step 1: Auto-Context (DO THIS BEFORE ASKING QUESTIONS)

Before asking questions like "what does the code look like?", gather context yourself:

Repo inspection checklist
  • Identify likely modules/files impacted
  • Locate existing patterns (similar features, conventions, error handling style)
  • Check configs, scripts, existing command definitions
  • Note any constraints (runtime, dependency policy, build tooling)
Documentation checklist
  • Look for existing PRDs/specs/templates
  • Look for command usage examples, README, ADRs if any

Write findings into PRD:

  • Add to What I already know
  • Add constraints/links to Technical Notes

Step 2: Classify Complexity (still useful, not gating task creation)

ComplexityCriteriaAction
TrivialSingle-line fix, typo, obvious changeSkip brainstorm, implement directly
SimpleClear goal, 1–2 files, scope well-definedAsk 1 confirm question, then implement
ModerateMultiple files, some ambiguityLight brainstorm (2–3 high-value questions)
ComplexVague goal, architectural choices, multiple approachesFull brainstorm

Note: Task already exists from Step 0. Classification only affects depth of brainstorming.


Step 3: Question Gate (Ask ONLY high-value questions)

Before asking ANY question, run the following gate:

Gate A — Can I derive this without the user?

If answer is available via:

  • repo inspection (code/config)
  • docs/specs/conventions
  • quick market/OSS research

→ Do not ask. Fetch it, summarize, update PRD.

Gate B — Is this a meta/lazy question?

Examples:

  • "Should I search?"
  • "Can you paste the code so I can proceed?"
  • "What does the code look like?" (when repo is available)

→ Do not ask. Take action.

Gate C — What type of question is it?
  • Blocking: cannot proceed without user input
  • Preference: multiple valid choices, depends on product/UX/risk preference
  • Derivable: should be answered by inspection/research

→ Only ask Blocking or Preference.


Step 4: Research-first Mode (Mandatory for technical choices)

Trigger conditions (any → research-first)
  • The task involves selecting an approach, library, protocol, framework, template system, plugin mechanism, or CLI UX convention
  • The user asks for "best practice", "how others do it", "recommendation"
  • The user can't reasonably enumerate options
Research steps
  1. Identify 2–4 comparable tools/patterns
  2. Summarize common conventions and why they exist
  3. Map conventions onto our repo constraints
  4. Produce 2–3 feasible approaches for our project
Show full SKILL.md (393 more words)Show less
Research output format (PRD)

Add a section in PRD (either within Technical Notes or as its own):

markdown
## 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:

  • "Which approach do you prefer: A / B / C (or other)?"

Step 5: Expansion Sweep (DIVERGE) — Required after initial understanding

After you can summarize the goal, proactively broaden thinking before converging.

Expansion categories (keep to 1–2 bullets each)
  1. Future evolution

    • What might this feature become in 1–3 months?
    • What extension points are worth preserving now?
  2. Related scenarios

    • What adjacent commands/flows should remain consistent with this?
    • Are there parity expectations (create vs update, import vs export, etc.)?
  3. Failure & edge cases

    • Conflicts, offline/network failure, retries, idempotency, compatibility, rollback
    • Input validation, security boundaries, permission checks
Expansion message template (to user)
markdown
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:

  • What's in MVP → Requirements
  • What's excluded → Out of Scope

Step 6: Q&A Loop (CONVERGE)

Rules
  • One question per message

  • Prefer multiple-choice when possible

  • After each user answer:

    • Update PRD immediately
    • Move answered items from Open Questions → Requirements
    • Update Acceptance Criteria with testable checkboxes
    • Clarify Out of Scope
  1. MVP scope boundary (what is included/excluded)
  2. Preference decisions (after presenting concrete options)
  3. Failure/edge behavior (only for MVP-critical paths)
  4. Success metrics & Acceptance Criteria (what proves it works)
Preferred question format (multiple choice)
markdown
For <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

Step 7: Propose Approaches + Record Decisions (Complex tasks)

After requirements are clear enough, propose 2–3 approaches (if not already done via research-first):

markdown
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:

markdown
## Decision (ADR-lite)

**Context**: Why this decision was needed
**Decision**: Which approach was chosen
**Consequences**: Trade-offs, risks, potential future improvements

Step 8: Final Confirmation + Implementation Plan

When open questions are resolved, confirm complete requirements with a structured summary:

Final confirmation format
markdown
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.
Subtask Decomposition (Complex Tasks)

For complex tasks with multiple independent work items, create subtasks:

bash
# 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 Target Structure (final)

prd.md should converge to:

markdown
# <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>

Anti-Patterns (Hard Avoid)

  • Asking user for code/context that can be derived from repo
  • Asking user to choose an approach before presenting concrete options
  • Meta questions about whether to research
  • Staying narrowly on the initial request without considering evolution/edges
  • Letting brainstorming drift without updating PRD

Integration with Start Workflow

After brainstorm completes (Step 8 confirmation approved), the flow continues to the Task Workflow's Phase 2: Prepare for Implementation:

text
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.


CommandWhen to Use
$startEntry point that triggers brainstorm
$finish-workAfter implementation is complete
$update-specIf new patterns emerge during work

© jlifeng, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/brainstorm of jlifeng/JobPilot.

Open the folder on GitHubat commit 7f33542

Compare with similar skills

Brainstorm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Brainstorm compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Brainstorm this skilljlifeng/JobPilot140—~3.1kAutomated safety check: PassApache-2.0
Idea Refinementaddyosmani/agent-skills103k6 repos~2kAutomated safety check: PassMIT
CE BrainstormEveryInc/compound-engineering-plugin25k—~1.9kAutomated safety check: PassMIT
AI Team Orchestrationboshi-xixixi/TraeSkill275—~1.4kAutomated safety check: PassMIT
Feature BrainstormVeryGoodOpenSource/vgv-wingspan109—~1.8kAutomated safety check: PassMIT
Nw DivergenWave-ai/nWave617—~2.2kAutomated safety check: PassMIT

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  • CE Brainstorm

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Questions about Brainstorm

What does Brainstorm do?

Brainstorm - Requirements Discovery (AI Coding Enhanced). An agent skill from jlifeng/JobPilot. Brainstorm is an agent skill from jlifeng/JobPilot.

When should I use Brainstorm?

Brainstorm fits situations like: tasks that involve Brainstorming.

How do I install Brainstorm in Claude Code?

Run `npx skills add jlifeng/JobPilot --skill brainstorm -a claude-code`. Or copy the skill folder (.agents/skills/brainstorm in jlifeng/JobPilot) into .claude/skills/brainstorm in your project. Claude Code loads it when a task matches its description.

How do I install Brainstorm in Codex?

Run `npx skills add jlifeng/JobPilot --skill brainstorm -a codex`. Or copy the skill folder (.agents/skills/brainstorm in jlifeng/JobPilot) into .agents/skills/brainstorm in your project. Codex loads it when a task matches its description.

Can I use Brainstorm in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jlifeng/JobPilot --skill brainstorm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/brainstorm, .gemini/skills/brainstorm, .github/skills/brainstorm and .opencode/skills/brainstorm in your project.

What does Brainstorm need to run?

Going by SKILL.md and its folder, Brainstorm needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Brainstorm access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Brainstorm safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Brainstorm use?

Brainstorm is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Brainstorm use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Brainstorm?

Skills that share tags, products or a category with Brainstorm: Idea Refinement (addyosmani/agent-skills, 103k stars), CE Brainstorm (EveryInc/compound-engineering-plugin, 25k stars), AI Team Orchestration (boshi-xixixi/TraeSkill, 275 stars) and Feature Brainstorm (VeryGoodOpenSource/vgv-wingspan, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Brainstorm?

jlifeng (a GitHub user) maintains it in jlifeng/JobPilot, which has 140 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 8, 2026.

Source: jlifeng/JobPilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.