basic BMAD setup
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# Research with deep recon
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Open this when the user asks how to get the most from `bmad-deep-recon` or which of its services to use.
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## Start from the decision
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Every run serves a decision: enter a market, pick a library, scope a product. Have the user state it first. A vague question gives vague research, so when the idea itself is still loose, recommend `bmad-forge-idea` first.
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## Which service
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| Service | Recommend when |
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| Draft: writes a prompt for the user's own deep-research tool | The user subscribes to ChatGPT, Gemini, Perplexity, or similar. It is the cheap option and often covers more public sources. |
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| Process: turns a finished report into a short cited summary | The user has any report, from a tool or an analyst. Draft then Process is the usual pairing. |
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| Run: researches here with parallel web searches | The user wants results in one sitting, or the research needs sources only this session can reach. It costs tokens and minutes and needs web access. |
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## What to tell the user
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- It can go quick or deep. Suggest a quick pass for a narrow question, and a deep pass with stronger verification when the decision is costly to reverse. The user just says so.
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- "Help me choose between A and B" is supported for any research type. It agrees the requirements first and ends with a pick, a runner-up, and the strongest argument against the pick.
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- Research types cover market, domain, technical, competitive, user voice, and academic literature. A team can add its own through `bmad-customize`.
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- Conclusions come only from sources retrieved during the run, with citations. The model's memory and the project's files only shape the questions. Thin evidence is reported as thin.
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- A report ages. An existing run can be refreshed, which re-checks only the claims most likely to be stale, or deepened in one area. When a run on the topic already exists, recommend resuming it.
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- The result is `research-<topic>.md`, a cited summary other skills can take as input without reprocessing.
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