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affaan-m/parallel-execution-optimizer

affaan-m

parallel-execution-optimizer

Use when the user wants a task done much faster through parallel work, concurrent agents, batched tool calls, isolated worktrees, or many independent verification lanes without losing correctness.

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v1.0Saved May 25, 2026

Parallel Execution Optimizer

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.
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Overall Score

83/100

Grade

B

Good

Safety

88

Quality

82

Clarity

85

Completeness

78

Summary

This skill guides concurrent task execution by decomposing work into parallel lanes with explicit dependency analysis. It teaches agents to batch independent operations, isolate writes, and verify compatibility before merging results—enabling significant speedups without sacrificing correctness.

Detected Capabilities

file readbash executiongrep searchfile glob operationsedit operationsprocess polling

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

parallel task executionconcurrent build lanesbatch independent workspeed up with parallelismmulti-lane verification

Use Cases

  • Run multiple code inspections simultaneously without conflicts
  • Execute independent build/test lanes in parallel
  • Perform concurrent API and browser verification checks
  • Deploy readbacks after isolated implementation passes
  • Batch file reads and metadata queries for faster repo analysis

Quality Notes

  • Well-structured skill with clear patterns and decision rules
  • Explicit guardrails against dangerous parallelization (destructive commands, live deploys, shared writes)
  • Practical Lane Matrix concept for visualizing concurrency boundaries
  • Strong failure modes section identifies real anti-patterns and traps
  • Clear execution rules prevent common parallelism errors (write collisions, unpolled processes, skipped verification)
  • Good guidance on isolation strategies (worktrees, branches, datasets)
  • Emphasizes correctness-first approach with verification tables rather than vague speed claims
  • Could benefit from concrete code examples or pseudo-code for lane construction
Model: claude-haiku-4-5-20251001Analyzed: May 25, 2026

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