Draft

Build a deep-research prompt the user runs themselves — in conversation, fast, not a project. The pack's craft travels inside the prompt so the outside tool works to this harness's standard.

1. Open the floor before any structured questions: invite the decision they're facing and anything they already have — briefs, links, a prior report, half-formed constraints — in one turn, then ask only what's still missing. Nail the decision, topic, and type; load the pack. Ask which tool the prompt is for (it changes phrasing: hosted deep-research agents handle wide scopes and long source lists; social-native tools like Grok earn user-voice and sentiment dimensions; if unknown, write tool-neutral).
2. Compose the prompt from the pack: the dimensions as explicit research questions pruned to the decision, the freshness bars as recency requirements, the two-source expectation for its critical claim classes, the audience, the source policy — {workflow.preferred_sources} named as sources to prefer, {workflow.banned_sources} as sources never to cite — and a non-negotiable citation demand: every claim with source URL and publication date, contrary evidence reported, gaps admitted rather than padded. Structure the requested output so Process can extract it cleanly (findings per dimension, a source list).
3. Bind {doc_workspace}: expand the folder name deterministically (uv run scripts/recon_kit.py slug "<topic>" --type <type> --pattern "{workflow.run_folder_pattern}" — same expansion every mode, so the report comes back to the same folder) under {workflow.research_output_path}, init the memlog with the decision context, save the prompt as {doc_workspace}/brief.md, and present it paste-ready in chat.
4. Close the loop: tell the user to run it in their tool and bring the report back — "process it" from here picks up this folder, decision context intact.