pipeline-input-review

Identify the earliest pipeline stage causing downstream failures via backward analysis.

Updated Apr 20, 2026
One-click install
npx skills add https://github.com/thistleknot/skills --skill pipeline-input-review
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: pipeline-input-review
Source: https://github.com/thistleknot/skills/tree/main/pipeline-input-review
Command: npx skills add https://github.com/thistleknot/skills --skill pipeline-input-review

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents “laundering errors” in multi-stage pipelines by forcing an agent to pinpoint the earliest stage where inputs become wrong, then verify the failing stage with materialized inputs before attempting any fix.

Core Features & Use Cases

  • Pipeline partitioning before harness dispatch: isolates the smallest coherent failing unit so the harness can fix only the root stage.
  • Boundary input verification: materializes the exact inputs that enter the failing stage (files, visuals, and parsed structures) to avoid guessing.
  • Hyper-focused problem statements: translates the symptom into a strict, single-stage fix request with explicit constraints.

Quick Start

Use pipeline-input-review when a harness produces wrong, blurry, or misaligned output and you suspect the root cause is an upstream input or stage-boundary parsing issue.

Frequently Asked Questions about pipeline-input-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find the root cause of blurry or mis-cropped image processing output in a multi-stage pipeline?

To find the root cause of blurry or mis-cropped image processing output, perform a backward stage analysis to identify the earliest pipeline stage where incorrect inputs cause a visible downstream failure, then materialize and verify those exact inputs before fixing.

Why does patching output artifacts in agentic workflows fail to fix the underlying issue?

Patching output artifacts fails because it launders errors from upstream stages. You must isolate the smallest coherent failing unit by verifying boundary inputs, preventing the error from propagating through subsequent multi-stage harness executions.

How do I isolate a failing stage in a multi-stage agent harness for debugging?

To isolate a failing stage in a multi-stage agent harness, partition the pipeline before dispatch, materialize the exact files and parsed structures entering the suspected stage, and issue a single-stage fix request with explicit scope constraints.

What is the best way to debug misaligned grid parsing across multiple pipeline stages?

The best way to debug misaligned grid parsing is executing a backward stage analysis to pinpoint where exact inputs become wrong, materializing the boundary inputs of that specific stage, and avoiding downstream output patches.

Does pipeline debugging work without materializing the exact boundary inputs of the failing stage?

Pipeline debugging does not work effectively without materializing boundary inputs. Materializing exact files, visuals, and parsed structures entering the failing stage prevents guessing and ensures the root cause is targeted accurately.

When should I avoid patching outputs and use stage isolation for pipeline debugging instead?

You should avoid patching outputs and use stage isolation when you suspect an upstream input or stage-boundary parsing issue is causing symptoms like blur, mis-cropping, or offset artifacts across stages in your agentic workflow.