Evaluate a single product dimension and return prioritized recommendations.
npx skills add https://github.com/n0rvyn/indie-toolkit --skill teardown-n0rvyn
Please help me install this Agent Skill. Skill: teardown Source: https://github.com/n0rvyn/indie-toolkit/tree/main/product-lens/skills/teardown Command: npx skills add https://github.com/n0rvyn/indie-toolkit --skill teardown-n0rvyn
Provides a structured, deep examination of a single product evaluation dimension when a focused, evidence-driven analysis is required beyond a standard multi-dimension evaluation. It resolves calibration and dimension reference files, merges universal and platform-specific sub-questions, and orchestrates market scanning and evaluator agents to produce per-question analysis, scoring anchors, evidence summaries, and prioritized recommendations.
Teardown the moat for this product and return a deep-dive report with per-question analysis, scores, evidence, and prioritized recommendations.
High-intent search queries and answers about installing and using this skill.
Use this tool to conduct a deep-dive product evaluation by targeting a single dimension like demand, journey, or moat. It resolves calibration files, merges sub-questions, and dispatches evaluator agents to return structured scores, evidence summaries, and prioritized recommendations.
Yes, you can analyze a single dimension like moat or demand instead of a full evaluation. The tool accepts English and Chinese dimension names or aliases, running a focused assessment that merges platform-specific sub-questions and extracts scoring anchors for a comprehensive report.
To get evidence and scoring anchors for product dimension analysis, the tool dispatches a dedicated dimension-evaluator in deep mode. It returns per-sub-question analysis with sub-scores, an evidence summary table, and a dimension score matched to specific anchors.
Yes, the product evaluation tool supports analyzing local project directories. It accepts a local path, name, or URL for target resolution and platform detection, automatically defaulting to the current working directory if no specific target is provided.
The best way to generate prioritized recommendations for an execution dimension is to run a focused deep-dive evaluation. It orchestrates market scanning for sensitive dimensions and dispatches evaluators to produce actionable output, including follow-up suggestions based on evidence.
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