The Only Two AI Skills That Actually Work: Brainstorming and TDD

Category: AI | Published: March 2026

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After experimenting with dozens of AI agent skills and automation workflows, most developers arrive at the same honest conclusion: the majority of skills are impressive in demos and underwhelming in practice. But occasionally, a tool genuinely changes how you work.

Developer @jarodise recently shared a candid take on this exact experience — after testing many OpenClaw-style skills, only two consistently delivered real value: Superpower's Brainstorming skill and Superpower's TDD (Test-Driven Development) skill. Both are deceptively simple in concept but remarkably effective in execution. This post breaks down why they work, how to use them, and what makes them different from the noise.

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The Core Problem: Why Most AI Skills Fall Short

The promise of AI skills is straightforward — plug them into your workflow, and your AI agent becomes a specialist. In reality, most skills fail at the same point: context quality.

When you prompt an AI with a vague or underspecified request, you get a vague or misaligned output. The model fills gaps with assumptions, and those assumptions compound quickly. You end up iterating not on your actual idea, but on the AI's interpretation of your idea.

Most AI skills don't solve this problem. They add formatting templates, persona prompts, or output styles — cosmetic changes that don't address the root cause. The two skills highlighted here are different because they both target context quality directly, each from a different angle.

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Skill #1: Superpower Brainstorming — Decompose Before You Create

What It Does

The Superpower Brainstorming skill intercepts your prompt before the AI starts generating. Instead of immediately producing output, it breaks your request into a structured set of sub-questions — the key decisions and ambiguities hiding inside your original prompt — and presents them to you for review.

You answer those questions. Then the AI generates.

Why This Matters

Consider a common developer scenario: you ask an AI to help you design a notification system.

Without brainstorming, the AI picks a direction:

You: Design a notification system for my app.

AI: Here's a notification system using a pub/sub pattern with Redis...

Maybe that's what you wanted. Maybe you needed WebSockets. Maybe you needed push notifications for mobile. The AI guessed, and now you're steering a ship that's already moving.

With the Brainstorming skill, the flow changes:

You: Design a notification system for my app.

AI (Brainstorming): Before I begin, let me clarify a few things:
  1. What delivery channels do you need? (email, push, in-app, SMS)
  2. Is this real-time or batch/digest delivery?
  3. Do you need user-level notification preferences?
  4. What's the expected volume — hundreds of users or millions?
  5. Are there any existing infrastructure constraints (Redis, Kafka, etc.)?

You: In-app only, real-time, yes to preferences, ~10k users, using Postgres.

AI: Got it. Here's a design using Postgres LISTEN/NOTIFY with a preference layer...

The output is now grounded in your actual constraints. You didn't write a longer prompt — you answered structured questions that surfaced what mattered.

Practical Use Cases

• Technical architecture planning — surface assumptions before committing to a design
• Content creation workflows — clarify tone, audience, format, and goals before drafting
• Product feature ideation — enumerate trade-offs and constraints before speccing
• Code refactoring — define scope, goals, and risk tolerance before touching the codebase

The key insight is that the quality of AI output is bounded by the quality of context. The Brainstorming skill systematically improves context without requiring you to become a better prompt engineer. It does the prompt engineering for you.

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Skill #2: Superpower TDD — Stop Being the AI's Manual Test Runner

The Problem It Solves

Any developer who has spent serious time with AI-generated code knows the pattern: the AI writes code confidently, you run it, something breaks, you report the error, the AI fixes it, something else breaks. You become a human test runner in a slow, error-prone feedback loop.

This is especially painful in complex codebases where bugs compound and context drifts across long conversations. The AI wrote the code, but you are doing the quality assurance work — manually.

The Superpower TDD skill flips this dynamic.

How It Works

Instead of writing implementation code first, the skill drives the AI to write tests before writing the implementation — mirroring real test-driven development methodology. The workflow looks like this:

1. You describe the desired behavior or function.
2. The AI writes failing tests that define that behavior.
3. You (or the AI) review and approve the tests.
4. The AI writes implementation code to pass those tests.
5. Tests run. If they fail, the AI debugs against the tests — not against your manual feedback.

This shifts the debugging target. Instead of you describing "it doesn't work" in natural language, the test output provides precise, machine-readable failure information. The AI can iterate against that feedback autonomously.

A Concrete Example

# Step 1: Behavior description
# "I need a function that parses a duration string like '2h30m' into total minutes."

# Step 2: AI writes tests first
def test_parse_duration():
    assert parse_duration("2h30m") == 150
    assert parse_duration("45m") == 45
    assert parse_duration("1h") == 60
    assert parse_duration("0m") == 0
    assert parse_duration("1h0m") == 60

# Step 3: AI writes implementation to pass tests
import re

def parse_duration(duration_str: str) -> int:
    hours = int(m.group(1)) if (m := re.search(r'(\d+)h', duration_str)) else 0
    minutes = int(m.group(1)) if (m := re.search(r'(\d+)m', duration_str)) else 0
    return hours * 60 + minutes

If parse_duration("1h0m") fails, the AI sees the exact assertion error and fixes the implementation — without you needing to trace the bug manually or explain what "wrong" means in plain English.

Why TDD Changes the AI Collaboration Dynamic

• Reduces your manual testing burden significantly
• Pins expected behavior so regressions are caught automatically
• Keeps the AI accountable to a verifiable spec, not a vague description
• Produces a test suite as a byproduct — documentation that stays accurate because it's executable
• Shortens debugging loops by giving the AI structured failure signals instead of natural language error reports

This is especially powerful in longer projects where AI context windows drift. The tests remain a stable, persistent source of truth that anchors the AI's work even as the conversation evolves.

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Why These Two Skills Complement Each Other

Used together, the Brainstorming and TDD skills address AI collaboration at two distinct stages:

| Stage | Problem | Skill | |-------|---------|-------| | Before generation | Vague context leads to misaligned output | Brainstorming | | During implementation | AI-generated code requires manual validation | TDD |

Brainstorming ensures you're building the right thing. TDD ensures the thing gets built correctly. Most AI workflow failures happen at one of these two points — ambiguous intent upstream, or unverified output downstream.

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Conclusion: Fewer Tools, Better Fundamentals

The takeaway from @jarodise's experience isn't that you need more skills — it's that you need the right skills targeted at the right problems. The AI skills ecosystem is crowded, but most entries add surface-level features rather than addressing fundamental workflow bottlenecks.

Superpower's Brainstorming skill attacks context quality before generation. Superpower's TDD skill attacks output quality during implementation. Together, they address the two most common failure modes in AI-assisted development workflows.

If you're evaluating AI skills for your own stack, the filtering question is simple: does this skill improve the quality of what goes in, or what comes out? If it doesn't do either, skip it.

Start with fundamentals. The developers who get the most out of AI tools aren't using the most skills — they're using the best ones.

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Based on insights shared by @jarodise. Explore more AI automation tools and OpenClaw skills at ClawList.io.