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assess-findings

Render codebase assessment reports from deterministic run-context data and layer-specific scorecards.

30|5|Updated Jan 13, 2026
Search Tags:#automation#codebase#reporting#assessment#readiness#technical-debt
One-click install
npx skills add https://github.com/bjcoombs/ai-native-toolkit --skill assess-findings
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: assess-findings
Source: https://github.com/bjcoombs/ai-native-toolkit/tree/main/skills/assess-findings
Command: npx skills add https://github.com/bjcoombs/ai-native-toolkit --skill assess-findings

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the inconsistency of AI-generated reports by separating deterministic data gathering from prose generation, ensuring that codebase assessments are reproducible, objective, and actionable.

Core Features & Use Cases

  • Deterministic Reporting: Assembles complex data from the assess-engine into a standardized, high-integrity report format.
  • Cross-Layer Analysis: Surfaces critical findings like hidden coupling, lying maps, and complexity hotspots that are invisible to single-axis scans.
  • Use Case: Use this after running the assess-engine to generate a professional-grade readiness report that highlights the top three technical debt priorities for your engineering team.

Quick Start

Trigger the assess-findings skill to assemble the final report once the deterministic orchestrator has completed the data collection phase.

Frequently Asked Questions about assess-findings

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

FAQPage Schema
How do I generate reproducible codebase assessment reports?▼

Reproducible codebase assessment reports are generated by synthesizing deterministic run-context data and layer-specific scorecards into a structured Markdown format, separating objective data gathering from prose generation to ensure consistency.

What is deterministic technical debt reporting?▼

Deterministic technical debt reporting assembles complex data into a high-integrity format, ensuring codebase assessments remain objective and reproducible by preventing AI-generated prose from altering the underlying factual findings.

How do I identify hidden coupling and complexity hotspots in a codebase?▼

Hidden coupling and complexity hotspots are identified through cross-layer analysis that surfaces critical findings invisible to single-axis scans, synthesizing the results into layer-specific scorecards within a comprehensive readiness report.

Can I track architectural readiness across software development lifecycles?▼

Architectural readiness tracking across software development lifecycles is supported by rendering comprehensive assessment reports that balance high-level executive summaries with granular technical findings.

Do I need an orchestrator to produce standardized codebase readiness reports?▼

A deterministic orchestrator must complete the data collection phase first, after which the assessment findings are assembled into a standardized, professional-grade report highlighting the top three technical debt priorities.

What is the best way to format technical debt findings for an engineering team?▼

The best way to format technical debt findings is using a structured, fold-based Markdown output that balances high-level executive summaries with granular technical findings, directly highlighting priority action items for engineering teams.

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