--- title: "Use Cases and Examples" source: https://fieldbw.com/docs/sr-7/use-cases --- # Use Cases and Examples ## Voice capture to searchable archive The simplest and most powerful use case for [Signal Recorder SR-7](/sr-7): record freely, let the software handle the rest. ### Daily voice journal Record a quick debrief at the end of each day. Don't worry about structure — just talk. SR-7 transcribes it, generates a title and summary, and adds it to your library. Over time, you build a searchable archive of what you were thinking, doing, and planning. ``` [Record 3 minutes about your day] SR-7 generates: Title: "Sprint retrospective thoughts and hiring update" Summary: "Reflected on the sprint — delivery was solid but velocity dropped due to onboarding. Noted two candidates to follow up with after the panel interview on Thursday." ``` Search your journal weeks later for "hiring" or "sprint" and find exactly what you said. ### Meeting capture Record meetings directly in SR-7, or import recordings from a hardware device afterward. The transcript becomes a searchable record, and the AI summary gives you the key points without re-listening. ### Research and interviews Record interviews, user research sessions, or field notes. Group them by project. Search across all recordings in a project to find patterns — every word is indexed once transcribed. ## Working with AI agents via MCP SR-7's MCP server exposes your entire recording archive to AI tools. This turns voice into a data source that agents can read, search, and act on. ### Search your recordings from an agent ``` You: "What did I say about the API redesign last week?" Agent: [calls search with query "API redesign"] Agent: "On March 28, you recorded a note titled 'API redesign direction'. Summary: You decided to go with a REST-first approach, defer GraphQL, and keep the existing auth layer. You mentioned wanting to discuss rate limiting with Dan." ``` ### Extract action items from recordings ``` You: "Go through today's recordings and pull out anything actionable" Agent: [calls list_recordings — filters to today] Agent: [calls get_recording for each — reads transcripts] Agent: "I found 3 actionable items: 1. Send the revised proposal to the client (from your 2pm note) 2. Book a dentist appointment (from your morning memo) 3. Review the Q2 budget spreadsheet Lisa sent (from your post-meeting debrief)" ``` ### Summarise a project ``` You: "Give me an overview of everything in the 'Product Launch' project" Agent: [calls list_recordings with projectId] Agent: [reads transcripts and summaries for all recordings] Agent: "The project has 14 recordings over 3 weeks. Key themes: - Messaging pivot: you moved from 'productivity' to 'clarity' positioning - Launch date: targeting April 21, pending final QA - Open question: pricing tier for teams — you keep going back and forth" ``` ### Generate a written draft from voice notes ``` You: "I recorded some thoughts about our approach to on-device AI. Turn those into a blog post draft." Agent: [calls search with query "on-device AI"] Agent: [reads transcripts from matching recordings] Agent: [writes a draft based on your words and ideas] ``` Your voice notes become first drafts. The agent preserves your thinking and phrasing while adding structure. ## SR-7 + TR-1: voice to action When you connect both SR-7 and TR-1 to the same AI agent, your voice recordings become a direct input to your task management system. Record your thoughts, and the agent turns them into structured, filed tasks. ### Morning brain dump to task list Record a stream-of-consciousness brain dump about everything on your mind. Then let the agent process it into your GTD system. ``` You: [Record 5 minutes in SR-7] "I need to call the accountant about the Q1 filing... also the design review is Thursday, I should prep slides for that... oh and we're out of coffee filters... Maria asked about the API docs, I should unblock her today... sometime this month I want to look into that new database thing Dan mentioned..." You: "Process my latest recording into TR-1" Agent: [calls SR-7 search — finds the recording, reads transcript] Agent: [calls TR-1 capture — "Call accountant about Q1 filing"] Agent: [calls TR-1 create_task — "Prep slides for Thursday design review" — dueDate: Wednesday, project: "Design Review"] Agent: [calls TR-1 capture — "Buy coffee filters" — energy: low] Agent: [calls TR-1 create_task — "Send Maria the API docs link" — status: next, today: true] Agent: [calls TR-1 create_task — "Look into new database Dan mentioned" — status: someday] Agent: "Created 5 tasks from your recording. 'Send Maria the API docs' is flagged for today. 'Look into new database' is in Someday/Maybe." ``` The agent applies GTD judgement — urgent items go to Today, vague ideas go to Someday/Maybe, and everything gets a clear title. You recorded a messy brain dump; you got a clean task list. ### Post-meeting action extraction ``` You: [Record meeting in SR-7] You: "Process the meeting I just recorded. Create tasks for anything I committed to, and put follow-ups in Waiting For." Agent: [calls SR-7 list_recordings — finds latest recording] Agent: [calls SR-7 get_recording — reads transcript] Agent: [calls TR-1 create_task — "Draft partnership proposal for Acme" — project: "Partnerships", dueDate: next Friday] Agent: [calls TR-1 create_task — "Waiting for: Jake to send pricing sheet" — status: waiting] Agent: [calls TR-1 create_task — "Schedule follow-up call with Acme (after they review proposal)" — status: waiting] Agent: "Created 1 action and 2 waiting-for items from the meeting." ``` ### Weekly review with voice context During your weekly review in TR-1, the agent can pull in context from your recordings to help you make decisions about stale tasks. ``` You: "Let's do the weekly review" Agent: [calls TR-1 start_weekly_review] Agent: [flags a stale task: "Research competitor pricing"] Agent: "This task has been in Next Actions for 2 weeks. Want to keep it, defer it, or drop it?" You: "I think I talked about this — check my recordings" Agent: [calls SR-7 search with query "competitor pricing"] Agent: "You mentioned this on March 25: 'Competitor pricing isn't urgent anymore — we decided to go with value-based pricing instead. Maybe revisit in Q3.' Want me to move it to Someday/Maybe?" You: "Yeah, defer it to July" Agent: [calls TR-1 update_task — deferDate: July 1] ``` Your recordings become the memory that makes reviews faster and decisions better-informed. ## Markdown export for knowledge systems SR-7 exports every recording as a Markdown file with YAML front matter — title, date, duration, transcript, and summary. These files integrate directly with Obsidian, Logseq, or any Markdown-based knowledge system. ### Build a voice-first knowledge base Enable auto-export in SR-7 settings. Every recording automatically becomes a Markdown file in your vault. Your voice notes sit alongside your written notes, fully linked and searchable. ### Feed recordings into other tools The exported Markdown files work as input for any tool that reads text — static site generators, search indexes, or custom scripts. The front matter makes them easy to filter and process programmatically. ## Tips - **Record short and often.** Five one-minute recordings are more useful than one rambling ten-minute recording. Shorter recordings get better titles and sharper summaries. - **Use projects to group context.** An agent summarising a project with 20 recordings can give you a far better overview than one summarising your entire library. - **Star recordings you reference often.** Stars give you a quick-access layer across projects — use them for key decisions, important ideas, or things you keep coming back to. - **Let transcription run automatically.** Enable auto-transcribe on import so every recording is searchable the moment it lands in your library. - **Pair with TR-1 for a complete capture system.** Voice in SR-7, tasks in TR-1, one agent connecting them. Nothing falls through the cracks.