Investigate undocumented behavior with source-referenced, confidence-graded findings.
npx skills add https://github.com/nsheaps/ai-mktpl --skill deep-research-nsheaps
Please help me install this Agent Skill. Skill: deep-research Source: https://github.com/nsheaps/ai-mktpl/tree/main/.claude/skills/deep-research Command: npx skills add https://github.com/nsheaps/ai-mktpl --skill deep-research-nsheaps
This Skill provides a repeatable, evidence-driven methodology for investigating undocumented behavior, internal mechanisms, and "how does X work?" questions when official documentation and community sources are incomplete or contradictory. It helps researchers move from uncertain observations to verifiable explanations with source references and confidence judgments.
Run a deep research session on "terminal title behavior" and produce a source-backed report with confidence levels and file:line references.
High-intent search queries and answers about installing and using this skill.
Investigate undocumented behavior by applying a multi-phase research workflow that combines parallel external searches with optional source-level tracing and decompilation. This methodology produces clear, source-referenced explanations with file:line evidence.
Trace unexpected CLI telemetry by combining GitHub issues, official documentation, and decompiled code to identify entry points and trace call chains. This produces a source-backed report with explicit confidence levels and file:line references.
Yes, you can obtain confidence-graded findings when documentation contradicts observed behavior by running a multi-phase research workflow. It synthesizes external searches and source analysis to deliver verifiable explanations with explicit confidence judgments.
Source analysis on minified or decompiled code works by locating entry points and tracing call chains to verify internal mechanisms. It provides definitive answers and annotates findings with file:line references where available.
Use reverse engineering and decompilation when official documentation is insufficient or contradictory and community sources lack answers. It enables source-level tracing to move from uncertain observations to verifiable, evidence-driven explanations.
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