Generate point-in-time research snapshots for technical decisions as structured markdown artifacts.
npx skills add https://github.com/bpawlakj/ai-devkit --skill research-bpawlakj
Please help me install this Agent Skill. Skill: research Source: https://github.com/bpawlakj/ai-devkit/tree/main/claude/skills/research Command: npx skills add https://github.com/bpawlakj/ai-devkit --skill research-bpawlakj
This Skill turns open-ended technology questions into a durable, decision-ready research artifact so teams can compare options, document evidence, and reduce confirmation bias before making a significant technical choice.
Ask an AI runner to create a research snapshot for a specific decision by running: /research evaluate Auth0 vs Supabase vs Clerk.
High-intent search queries and answers about installing and using this skill.
Technology research for vendor selection is documented by generating a structured research snapshot artifact with a clear date, status, and decision framing to capture evidence and compare options before commitment.
Comparing libraries before adoption works best by classifying the research type, gathering web and code context via parallel subagents, and synthesizing findings with enforced citation discipline into a single durable markdown artifact.
Reduce confirmation bias during technology investigation by running an anti-bias cross-check that includes a devil's advocate and pre-mortem section to stress-test the leading conclusion before finalizing the research snapshot.
Yes, technical decision documentation reads existing reference material from docs/reference/ directories, including binary inputs via their sibling markdown indexes, and cites those sources directly within the research findings.
Yes, technology research supports an interview mode where you supply the facts and the system structures them, alongside an investigation mode that spawns subagents to gather web and code context automatically.
A point-in-time research snapshot is not suited for continuous monitoring or post-commitment tracking, as it targets specific technology-level questions like vendor selection and library comparisons before a decision is made.
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