agentsclimarketplace

Etl retry backoff simulator

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

Production-Ready Agent Skills : product analytics, growth experiments, CRM, research synthesis, postmortems, data contracts, SaaS spend, compliance, architecture maps, and LLM eval and many more.

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.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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

SKILL.md

1.5 KB, 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

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.