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Etl retry backoff simulator

Skill sisodiabhumca/agent-skills/skills/etl-retry-backoff-simulator

Simulate retry and exponential backoff strategies against a failure-rate model to estimate expected runtime and cost (vendor-neutral).From its SKILL.md

Install
npx -y skills add sisodiabhumca/agent-skills --skill etl-retry-backoff-simulator

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

1.5 KB, 324 tokens by cl100k_base, as published. Nobody here has run it

When to invoke

  • You are tuning retries/backoff for ETL jobs, API ingestion, or batch pipelines.
  • You need to compare strategies (fixed delay vs exponential + jitter).
  • You want a quick estimate of expected runtime, attempts, and wasted work under failure.

Inputs needed

  • A JSON config with:
    • attempts_max
    • base_delay_seconds
    • strategy: fixed, exponential, or exponential_jitter
    • failure_probability per attempt (0..1)
    • work_seconds_per_attempt (time spent before a failure/success)
    • trials for Monte Carlo simulation

Workflow

  1. Validate config.
  2. Run Monte Carlo simulation across trials:
    • For each trial, attempt the job up to attempts_max.
    • Each attempt succeeds with probability \(1-p\).
    • Add work time each attempt; add delay between failed attempts per strategy.
  3. Compute summary statistics:
    • success rate
    • expected attempts
    • p50/p90 total duration
    • expected backoff time

Output format

JSON to stdout:

  • success_rate
  • expected_attempts
  • duration_seconds: p50, p90, mean
  • expected_backoff_seconds

Guardrails

  • Vendor-neutral: does not assume a specific orchestrator or cloud.
  • Model is simplified; use for comparative tuning, not precise capacity planning.

Reference code

  • etl_retry_backoff_simulator.py

What ships with it: 2 files

4.3 KB alongside SKILL.md, 1 of them executable

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