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skills/parallelization/SKILL.md

parallelization

Run independent LLM subtasks at same time, then combine results. Use when work can split into isolated parts or multiple judges can score in parallel.

npx skills add https://github.com/flpbalada/fb-skills --skill parallelization
GitHub

Skill Docs

Split independent work. Run branches at same time. Merge results.

When to Use

  • Subtasks are independent
  • Latency matters
  • Work splits by item, source, section, or test
  • Need multiple judges or rankings
  • Merge step is simple

Goal

Reduce wall-clock time or improve judgment quality without adding merge risk.

Rules

  • Only parallelize independent work.
  • Give each branch stable input.
  • Use same output schema for branches.
  • Set timeout and retry rules.
  • Allow partial-failure policy.
  • Merge with deterministic code when possible.
  • Cap branch count and cost.

Patterns

  • Sectioning: each branch handles different slice.
  • Voting: branches judge same item independently.
  • Retrieval: branches query different sources.
  • Testing: branches run separate checks.

Flow

  1. Identify independent units.
  2. Define branch input and schema.
  3. Run branches concurrently.
  4. Collect outputs.
  5. Normalize results.
  6. Merge, vote, or rank.
  7. Report branch failures.

Avoid

  • Hidden dependencies between branches.
  • Branch output needed by another branch.
  • Vague merge logic.
  • Duplicate work.
  • Ignored partial failures.
  • Branch count larger than value.

Output

## Parallel Plan

Task: [goal]
Split: [sectioning / voting / retrieval / testing]
Branches:
- [branch]: [input], output [schema]

Merge:
- Method: [merge / vote / rank]
- Failure policy: [retry / skip / stop]
- Cost cap: [limit]

Decision Rule

Use parallelization when independence is real and merge is cheap. If steps depend on each other, use prompt chaining.