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tinkermonkey
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pipeline-recommendations

Analyze and filter AI-generated pipeline recommendations by priority, target, and run time.

Updated Sep 22, 2025
Search Tags:#pipeline#filters#orchestrator#recommendations#analyze#project
One-click install
npx skills add https://github.com/tinkermonkey/switchyard --skill pipeline-recommendations
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pipeline-recommendations
Source: https://github.com/tinkermonkey/switchyard/tree/main/.claude/skills/pipeline-recommendations
Command: npx skills add https://github.com/tinkermonkey/switchyard --skill pipeline-recommendations

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze and filter AI-generated pipeline recommendations from completed runs to surface actionable fixes.

Core Features & Use Cases

  • Filter recommendations by priority (high/medium/low) and by target (orchestrator or project).
  • Distinguish between orchestrator and project recommendations and view per-run metadata.
  • Retrieve and aggregate fields like id, project, ended_at, issue_number, issue_title to facilitate triage.
  • Provide cross-project summaries or per-project views for strategic decision making.

Quick Start

Provide a prioritized summary of recommendations for a specific pipeline run, project, or time window using the available filters.

Frequently Asked Questions about pipeline-recommendations

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

FAQPage Schema
How do I filter pipeline run recommendations by priority and target?▼

You can filter pipeline run recommendations by priority (high, medium, low) and target (orchestrator or project) using command-line flags like --priority and --target to surface actionable fixes.

What is the best way to analyze AI-generated pipeline recommendations across multiple projects?▼

Analyzing pipeline recommendations across multiple projects is best achieved by using project-scoped views and cross-project summaries, aggregating fields like id, project, ended_at, issue_number, and issue_title for strategic decision making.

Can I retrieve pipeline recommendations for a specific time window?▼

Yes, you can retrieve pipeline recommendations for a specific time window by applying the --recent flag to filter completed runs based on their ended_at metadata.

How do I distinguish between orchestrator and project recommendations during triage?▼

You distinguish between orchestrator and project recommendations by applying the --target filter, which separates the respective run metadata to facilitate accurate triage and per-run analysis.

Do I need specific schema documentation to analyze pipeline-runs indices?▼

Yes, analyzing pipeline-runs indices requires documented schemas to correctly parse arguments and aggregate metadata fields like id, project, ended_at, issue_number, and issue_title for filtering.

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