doc-impact

Assess repository health for agent-generated skills across targets.

95|4|Updated Feb 27, 2026
One-click install
npx skills add https://github.com/aspenkit/aspens --skill doc-impact
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: doc-impact
Source: https://github.com/aspenkit/aspens/tree/main/.agents/skills/doc-impact
Command: npx skills add https://github.com/aspenkit/aspens --skill doc-impact

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Context health analysis helps ensure generated agent context stays fresh, comprehensive, and aligned with the codebase by auditing per-target coverage, drift, and hub integrity.

Core Features & Use Cases

  • Per-target health scoring across the repository
  • Drift detection and hub coverage evaluation
  • LLM interpretation (optional) and auto-repair recommendations

Quick Start

Run doc-impact to generate per-target health reports and apply recommended repairs.

Frequently Asked Questions about doc-impact

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

FAQPage Schema
What is context health analysis for agent-generated skills?

Context health analysis audits per-target coverage, drift, and hub integrity to ensure generated agent context stays fresh, comprehensive, and aligned with the codebase. It assesses repository health by scoring freshness, domain coverage, and drift across targets.

How do I detect context drift in codebases with per-target agent skills?

You can detect context drift by running drift detection evaluation to audit per-target coverage and hub integrity. The analysis evaluates drift, health scores, and hub coverage across targets to identify where generated context has fallen out of alignment with the codebase.

How do I generate per-target health reports and apply recommended repairs?

Run the analysis to generate per-target health reports and apply recommended repairs. The process executes summarizeTarget(), summarizeOpportunities(), and buildApplyPlan() to assess freshness, identify actionable opportunities, and output an auto-repair plan.

Can I use LLM interpretation for auto-repair recommendations on agent context?

Yes, LLM interpretation is an optional feature for generating auto-repair recommendations. It works alongside drift detection and health scoring to provide actionable opportunities for repairing misaligned agent-generated context across repository targets.

What's the best way to evaluate hub coverage and domain coverage for agent skills?

The best way to evaluate hub and domain coverage is to run a comprehensive health analysis that scores per-target freshness and coverage. This approach evaluates hook health and save tokens health to ensure generated context remains comprehensive and aligned.

Does context health analysis require specific dependencies to run drift detection?

No specific dependencies are required to run drift detection and context health analysis. The tool natively assesses repository health context, domain coverage, and hub integrity for agent-generated skills without needing external component installations.