Parallel Execution Optimizer

SkillFiles & storage

parallel-execution-optimizer is a skill that speeds up a task by turning it into a dependency graph of parallel lanes. It marks which lanes can run concurrently, batches independent reads and checks, isolates writes by file, worktree, branch, or service, and ends with a verification table proving results rather than claiming spee

Available today. Use it from your connected AI after setup.

Have an agent environment with Read, Write, Edit, Bash, Grep, and Glob tools available.

Then ask your AI: use the Parallel Execution Optimizer skill

What your AI can do with it

  • Maps a task into a dependency graph of lanes marked parallel, sequential, or gated
  • Writes a lane matrix showing write surfaces, risk, and verification per lane
  • Batches independent file reads, searches, status checks, and metadata queries
  • Isolates writes by file, worktree, branch, service, or dataset to avoid conflicts
  • Starts long-running tests, builds, and deploys in separate sessions and polls them
  • Produces a verification table with lanes run, blocked lanes, and checks passed

Getting started

  1. Have an agent environment with Read, Write, Edit, Bash, Grep, and Glob tools available.
  2. Add the parallel-execution-optimizer skill to your agent's skill set.
  3. Ask the agent to speed up a task through parallel work; it will build a lane matrix first.
  4. Review the matrix so lanes only run in parallel when their write surfaces do not collide.
  5. Check the final verification table rather than accepting a vague speed claim.

What this skill tells your AI

The instructions your AI receives, as published by affaan-m/ecc in skills/parallel-execution-optimizer/SKILL.md and read by ahel’s review.

Use this skill when speed comes from doing independent work at the same time: repo inspection, file reads, API checks, browser checks, build/test lanes, deploy readbacks, or multi-worktree implementation passes.

Core Pattern

Turn urgency into a dependency graph before acting.

  1. Define the objective and done signal.
  2. Split work into lanes.
  3. Mark each lane as parallel, sequential, or gated.
  4. Run independent reads/checks together.
  5. Keep writes isolated by file, worktree, branch, service, or dataset.
  6. Merge only after evidence shows the lanes are compatible.
  7. End with a verification table, not a vague speed claim.

Lane Matrix

Before a large push, write a compact matrix:

Lane | Can run in parallel? | Write surface | Risk | Verification
Repo scan | yes | none | low | rg/git status outputs
Backend patch | maybe | src/api | medium | unit tests
Frontend patch | maybe | app/components | medium | browser screenshot
Deploy readback | after build | remote service | high | live URL + logs

Only run lanes in parallel when their write surfaces do not collide.

Execution Rules

  • Batch file reads, searches, status checks, and metadata queries.
  • Use isolated worktrees for large unrelated implementation lanes.
  • Start long-running tests, builds, backfills, and deploys in separate sessions, then poll them deliberately.
  • If a lane discovers a blocker that changes the plan, pause dependent lanes and update the matrix.
  • Never let a background process outlive the turn unless the user explicitly asked for a continuing service.
  • Do not parallelize destructive commands, migrations, writes to the same table, or live customer-impacting deploys without an explicit gate.

Output Shape

Use this when reporting:

Parallel execution result:
- Lanes run: 5
- Lanes completed: 4
- Blocked lane: deploy readback, waiting on DNS propagation
- Fast path found: batched repo scan + focused tests
- Verification: lint pass, unit pass, live smoke pass

Failure Modes

  • More concurrency that creates conflicting edits.
  • Benchmarking the tool instead of the task.
  • Treating "fast" as done before correctness is proven.
  • Forgetting to poll running sessions.
  • Hiding skipped checks behind a success summary.

Signals

GitHub stars
268k
Forks
40k
Last commit
Sep 2026

Questions

Can Claude code run in parallel?
This skill lets an agent run independent lanes concurrently: batched reads and checks, isolated worktrees for implementation lanes, and long-running sessions polled deliberately. Lanes only run in parallel when their write surfaces do not collide.
What is the Claude Code Prompt Optimizer skill?
This item is the parallel-execution-optimizer skill, not a prompt optimizer. It turns a task into a dependency graph of parallel lanes with batched reads, isolated write surfaces, and a final verification table.
Advanced
Item type
skill
Key
parallel-execution-optimizer
Source
github.com/affaan-m/ecc