Parallel Execution Optimizer
SkillFiles & storageThe parallel-execution-optimizer is a skill that speeds up a task by turning it into a dependency graph of parallel lanes. It batches independent reads and checks, isolates write surfaces by file, worktree, branch, or service, and finishes with a verification table. Use it when a task can be done much faster through parallel work.
Use Parallel Execution Optimizer in Claude, ChatGPT or Ahel Desktop
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Have a task that can be split into independent pieces of work.
What your AI can do with it
- Map a task into a dependency graph of parallel lanes
- Mark which lanes can run concurrently and which need sequencing
- Batch independent reads and checks into fewer steps
- Isolate write surfaces by file, worktree, branch, or service
- Produce a final verification table proving results
Getting started
- Have a task that can be split into independent pieces of work.
- Add the skill to your agent's available skills.
- Give the agent the task and ask it to plan parallel lanes.
- Review the lane matrix and write surfaces before work starts.
- Check the final verification table when the task finishes.
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.
- Define the objective and done signal.
- Split work into lanes.
- Mark each lane as parallel, sequential, or gated.
- Run independent reads/checks together.
- Keep writes isolated by file, worktree, branch, service, or dataset.
- Merge only after evidence shows the lanes are compatible.
- 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
- 270k
- Forks
- 40k
- Last commit
- Sep 2026
Questions
- Can Claude code run in parallel?
- The skill plans parallel lanes and isolates writes by file, worktree, branch, or service so concurrent work does not conflict. It does not itself run or schedule agents.
- How to optimize Claude skills?
- This skill optimizes a task, not other skills. It maps the task into parallel lanes, batches reads and checks, isolates writes, and ends with a verification table.
- What kinds of tasks is this skill for?
- Tasks that can be split into independent pieces, especially ones with many reads or checks and writes that can be isolated by file, worktree, branch, or service.
- Does it guarantee the task will be faster?
- No. It produces a verification table proving results rather than claiming speed. Actual speed depends on the task and how well the lanes can run concurrently.
Advanced
- Item type
- skill
- Key
parallel-execution-optimizer- Source
- github.com/affaan-m/ecc