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himanshu231204
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agent-sort

Classify ECC components into DAILY and LIBRARY buckets using repo evidence.

2|Updated May 11, 2026
Search Tags:#agent configuration#ecc#component classification#repo analysis#context trimming#install plan#tech stack alignment
One-click install
npx skills add https://github.com/himanshu231204/AI_Research_agent --skill agent-sort-himanshu231204
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-sort
Source: https://github.com/himanshu231204/AI_Research_agent/tree/main/.opencode/skills/agent-sort
Command: npx skills add https://github.com/himanshu231204/AI_Research_agent --skill agent-sort-himanshu231204

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the guesswork and manual effort of curating ECC (AI agent context component) installations for software projects, solving the common problem of bloated, irrelevant default installs that add unnecessary context overhead and don't align with a repo's actual tech stack.

Core Features & Use Cases

  • Evidence-Based Classification: Sorts all ECC components (skills, commands, rules, hooks, scripts, and extras) into DAILY (always loaded per session) and LIBRARY (searchable, not default-loaded) buckets using concrete evidence from the repo's code, configs, and dependencies, not generic preferences.
  • Structured Review Workflow: Runs parallel or sequential review passes for different component types to speed up classification, with clear guardrails that require every DAILY classification to cite specific repo evidence.
  • Actionable Outputs: Generates a complete install plan, optional searchable skill library router, and verification report to ensure the final ECC setup exactly matches the repo's stack, with no stale or incompatible components.
  • Use Case: For a Python-based AI research repo using FastAPI, LangGraph, and Celery, this Skill would promote Python linting rules and LangGraph workflow skills to DAILY, and demote off-stack components like React or Swift rules to LIBRARY.

Quick Start

Use the agent-sort skill to analyze the current repository's tech stack and generate a tailored ECC install plan that only includes components relevant to the project.

Frequently Asked Questions about agent-sort

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

FAQPage Schema
How do I trim AI agent context components to match my repo's tech stack?▼

To trim AI agent context components, you classify ECC elements into DAILY or LIBRARY buckets using concrete evidence from repo code, configs, and dependencies. This eliminates bloated default installs, ensuring only relevant surfaces load by default for your specific project.

What is evidence-based ECC classification for software repositories?▼

Evidence-based ECC classification is the process of sorting AI agent context components into DAILY and LIBRARY buckets by analyzing a repository's actual code, configs, and dependencies. It removes stale off-stack components, reducing unnecessary context overhead during agent workflows.

How do I generate an ECC install plan for a specific tech stack?▼

You generate an ECC install plan by running structured review passes on your repository's components. The process cites specific repo evidence to promote relevant skills and rules to DAILY status while demoting off-stack items to a searchable LIBRARY.

Can I use repo analysis to remove irrelevant rules and scripts from my AI agent context?▼

Yes, you can use repo analysis to remove irrelevant rules and scripts from your AI agent context. By evaluating concrete repo evidence, the classification process demotes off-stack components to a searchable LIBRARY bucket, preventing them from loading by default.

Does this context trimming approach work for any software repository?▼

Yes, this context trimming approach applies to any software repository needing a project-specific ECC surface. It analyzes the repo's code, configs, and dependencies to classify skills, commands, rules, hooks, scripts, and extras into default-loaded or searchable buckets.

Why should I classify ECC components into DAILY and LIBRARY buckets?▼

You should classify ECC components into DAILY and LIBRARY buckets to eliminate bloated, irrelevant context installations that add overhead to AI agent workflows. This evidence-based classification ensures only tech-stack-aligned components load by default, optimizing agent performance.

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