speed-run

Speed-Run

Token-efficient code generation pipeline. Uses hosted LLM (Cerebras) for fast, cheap first-pass generation with Claude handling architecture and surgical fixes.

Skill	Description	Best For
speed-run:turbo	Direct hosted codegen	Single task, algorithmic code, boilerplate
speed-run:showdown	Same design, parallel runners compete	Medium-high complexity, want best implementation
speed-run:any-percent	Different approaches explored in parallel	Unsure of architecture, want to compare designs

Installation

/plugin marketplace add 2389-research/claude-plugins
/plugin install speed-run@2389-research

Prerequisites

Speed-run requires a Cerebras API key for hosted code generation. Free tier includes ~1M tokens/day.

1. Get a key at cloud.cerebras.ai
2. Add to ~/.claude/settings.json:

{
  "env": {
    "CEREBRAS_API_KEY": "your-key-here"
  }
}

3. Restart Claude Code

Flow

User: "speed-run" / "turbo build" / "fast build"
    ↓
Check: Cerebras API key
    ↓
┌─────────────────────────────────────────┐
│  Route Selection                        │
│                                         │
│  1. Turbo     - Direct codegen          │
│  2. Showdown  - Parallel competition    │
│  3. Any%      - Parallel exploration    │
└─────────────────────────────────────────┘

Quick Examples

Turbo (Direct Code Generation)

User: "Use speed-run to build a rate limiter"

Claude writes a contract prompt:
  - DATA CONTRACT (exact models, types)
  - API CONTRACT (exact routes, responses)
  - ALGORITHM (step-by-step logic)
  - RULES (framework, storage, error handling)

Cerebras generates code → written to disk (~0.5s)
Claude runs tests → surgical fixes if needed (1-4 lines)

The contract prompt pattern is like speccing a ticket for a junior dev — explicit inputs, outputs, types, and behavior. That specificity is what makes hosted LLMs reliable at 80-95% first-pass accuracy.

Showdown (Parallel Competition)

User: "Use showdown for the auth system"

Claude assesses complexity → spawns 3 runners
Each runner:
  1. Reads the shared design doc
  2. Creates their OWN implementation plan
  3. Generates code via Cerebras
  4. Runs tests, fixes failures

All runners dispatched in parallel.
Fresh-eyes review → judge scores all → winner selected.

Key insight: each runner creates their own plan from the design doc. No shared implementation plan means genuine variation emerges naturally.

Any% (Parallel Exploration)

User: "Not sure whether to use SQLite or Postgres, try both"

Claude generates 2-3 architectural approaches
Each variant:
  1. Gets its own worktree and branch
  2. Creates implementation plan for its approach
  3. Generates code via Cerebras
  4. Runs tests

Same scenario tests run against all variants.
Fresh-eyes review → judge scores all → winner selected.

When to use it

Scenario	Speed-run?
Algorithmic code, data transforms	Yes, turbo
Boilerplate, scaffolding	Yes, turbo
Comparing multiple implementations	Yes, showdown
Exploring different architectures	Yes, any-percent
Complex business logic that needs reasoning	No, use Claude directly
One-liner fixes	No, overkill

How It Compares to Test Kitchen

Speed-run mirrors test-kitchen's parallel patterns but shifts code generation to a hosted LLM:

	Test Kitchen	Speed-Run
Code generation	Claude writes everything	Cerebras generates, Claude fixes
Token cost	Standard	~60-70% savings
Generation speed	~10s per file	~0.5s per file
First-pass quality	~100%	80-95%
External dependency	None	Cerebras API key

The most direct comparison: test-kitchen's cookoff vs speed-run's showdown — same concept (multiple agents implement the same design), different execution strategy.

Available Models

Model	Speed	Notes
gpt-oss-120b	~3000 t/s	Default — best value, clean output
llama-3.3-70b	~2100 t/s	Reliable fallback
qwen-3-32b	~2600 t/s	Has verbose <think> tags
llama3.1-8b	~2200 t/s	Cheapest, may need more fixes

Dependencies

Speed-run orchestrates these skills (uses fallbacks if not installed):

• superpowers:dispatching-parallel-agents
• superpowers:using-git-worktrees
• superpowers:writing-plans
• superpowers:executing-plans
• superpowers:test-driven-development
• superpowers:verification-before-completion
• fresh-eyes-review:skills
• scenario-testing:skills
• superpowers:finishing-a-development-branch

Documentation