SuperAPI AI Directory

any-percent

Coding & development

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Updated 10-15 09:29

About this skill

Any%

Explore different approaches in parallel - no restrictions, fastest path to comparing real implementations. Each variant uses hosted LLM for code generation.

Announce: "I'm using speed-run:any% for parallel exploration via hosted LLM."

Core principle: When unsure of the best architecture, implement multiple approaches fast via hosted LLM and let real code + tests determine the winner.

When to Use

  • Unsure which architectural approach is best
  • Want to compare 2-5 different designs quickly
  • "Try both approaches", "explore both", "not sure which way"
  • Want token-efficient parallel exploration

Workflow Overview

Phase Description
1. Context Quick gathering (1-2 questions max)
2. Approaches Generate 2-5 architectural approaches
3. Plan Create implementation plan per variant
4. Implement Dispatch ALL agents in SINGLE message, each uses hosted LLM
5. Evaluate Scenario tests -> fresh-eyes -> judge survivors
6. Complete Finish winner, cleanup losers

Directory Structure

docs/plans/<feature>/
  design.md                  # Shared context
  speed-run/
    any-percent/
      variant-<slug>/
        plan.md              # Implementation plan for this variant
      result.md              # Final report

.worktrees/
  speed-run-variant-<slug>/  # Variant worktree

Phase 1: Quick Context

Gather just enough to generate approaches (1-2 questions max):

  • What are you building?
  • Any hard constraints? (language, framework, infrastructure)

Do NOT brainstorm extensively. The point of any% is to explore fast, not deliberate slowly.

Phase 2: Generate Approaches

Identify the primary architectural axis (biggest impact decision):

  • Storage engine (SQL vs NoSQL vs file-based)
  • Framework (Express vs Fastify vs Hono)
  • Architecture pattern (monolith vs microservices vs serverless)
  • State management approach
  • Auth strategy

Generate 2-5 approaches along that axis:

Based on the requirements, here are 3 approaches to explore:

1. variant-sqlite: SQLite storage with query builder
   → Pros: Simple, embedded, zero config
   → Cons: Single writer, no replication

2. variant-postgres: PostgreSQL with ORM
   → Pros: Scalable, ACID, rich queries
   → Cons: External dependency, more setup

3. variant-redis: Redis with persistence
   → Pros: Fast, built-in pub/sub
   → Cons: Memory-bound, limited queries

All will be implemented via hosted LLM and tested.
Spawning 3 parallel variants now.

Variant limits: Max 5-6. Don't do full combinatorial explosion:

  1. Identify the primary axis
  2. Create variants along that axis
  3. Fill secondary slots with natural pairings

Phase 3: Plan + Implement (Parallel)

Setup worktrees:

.worktrees/speed-run-variant-sqlite/
.worktrees/speed-run-variant-postgres/
.worktrees/speed-run-variant-redis/

Branches:
<feature>/speed-run/variant-sqlite
<feature>/speed-run/variant-postgres
<feature>/speed-run/variant-redis

CRITICAL: Dispatch ALL variants in a SINGLE message

<single message>
  Task(variant-sqlite, run_in_background: true)
  Task(variant-postgres, run_in_background: true)
  Task(variant-redis, run_in_background: true)
</single message>

Variant agent prompt: see references/variant-agent-prompt.md

Phase 4: Evaluate

Step 1: Gate check - All tests pass

Step 2: Run same scenario tests against all variants

Use scenario-testing skill. Same scenarios, different implementations.

Step 3: Fresh-eyes on survivors

Fresh-eyes review of variant-sqlite (N files)...
Fresh-eyes review of variant-postgres (N files)...

Step 4: Invoke Judge Skill

CRITICAL: Invoke speed-run:judge now. See references/judge-invocation.md for full context and any%-specific scoring notes.

Phase 5: Completion

Winner: Use finish-branch

Losers: Cleanup

git worktree remove .worktrees/speed-run-variant-sqlite
git worktree remove .worktrees/speed-run-variant-redis
git branch -D <feature>/speed-run/variant-sqlite
git branch -D <feature>/speed-run/variant-redis

Write result.md:

# Any% Results: <feature>

## Approaches Explored
| Variant | Approach | Tests | Scenarios | Fresh-Eyes | LLM Calls | Result |
|---------|----------|-------|-----------|------------|-----------|--------|
| variant-sqlite | Embedded SQL | 18/18 | 5/5 | 0 issues | 3 | WINNER |
| variant-postgres | External DB + ORM | 20/20 | 5/5 | 1 minor | 4 | eliminated |
| variant-redis | In-memory + persist | 16/16 | 4/5 | 2 issues | 5 | eliminated |

## Winner Selection
Reason: Simplest architecture, zero external dependencies, all scenarios pass

## Token Savings
Estimated savings vs Claude direct: ~60% on code generation

Save to: docs/plans/<feature>/speed-run/any-percent/result.md

Skill Dependencies

Dependency Usage
superpowers:writing-plans Generate implementation plan per variant
superpowers:using-git-worktrees Create isolated worktree per variant
superpowers:dispatching-parallel-agents Dispatch all variant agents in parallel
scenario-testing:scenario-testing Run same scenarios against all variants
fresh-eyes-review:fresh-eyes-review Quality review on survivors
speed-run:judge Scoring framework (bundled)
superpowers:finishing-a-development-branch Handle winner, cleanup losers

Common Mistakes

Too many variants

  • Problem: Combinatorial explosion, wasted compute
  • Fix: Cap at 5-6, pick primary axis only

Extensive brainstorming before exploring

  • Problem: Defeats the purpose of any% (fast exploration)
  • Fix: 1-2 questions max, then generate and go

Using Claude direct for all code generation

  • Problem: No token savings, defeats speed-run purpose
  • Fix: Variants MUST use hosted LLM for first-pass generation

Dispatching variants in separate messages

  • Problem: Serial instead of parallel
  • Fix: ALL Task tools in a SINGLE message

Skipping scenario tests

  • Problem: Can't compare variants fairly
  • Fix: Same scenarios against all variants

Forgetting cleanup

  • Problem: Orphaned worktrees
  • Fix: Always cleanup losers, write result.md