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Sales calls generate a lot of raw information. Notes, transcripts, rambling 45-minute recordings where the actual insights are buried somewhere in the middle. My client's team was supposed to be capturing competitive intel, objection patterns, and deal blockers from those calls — but realistically, nobody had time to do it consistently.
I built a pipeline in n8n that takes call transcripts (auto-generated via their existing recording tool), runs them through an LLM with a structured prompt, and writes the extracted data directly to a Google Sheet. Each row gets: deal stage, top objections raised, competitor mentions, next-step commitments, and a one-sentence summary.
The prompt engineering was the most iterative part. Generic "summarize this call" prompts give generic output. What worked was being extremely specific about output format and giving the model examples of what "a real objection" looks like versus filler. Once that was dialed in, the extractions were consistently useful.
The sheet now gets updated automatically after every call. The sales manager does a 10-minute review on Monday mornings instead of chasing reps for notes all week. Patterns that would've gone unnoticed — like a specific competitor coming up in 60% of deals in one region — became visible within the first month.
The automation didn't replace judgment. It just made sure the information existed in a usable form.
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I’ve been working on a small skill called Manual Orchestrator to make longer projects with AI agents easier to manage.
The problem started with a familiar situation: one chat would accumulate plans, implementation details, corrections, and test results. By the time I was ready for the next phase, I wanted a fresh conversation without losing the decisions we’d already made.
Manual Orchestrator gives that process some structure. One chat acts as the orchestrator: it breaks the work into stages and writes a prompt for the next one. I run that prompt in a new session, then bring the result back. The orchestrator reviews it against the stage’s criteria before preparing the next prompt. If something needs fixing, I go back to the same worker session.
The skill also keeps a record in the project with the current stage, decisions, evidence, and next steps. That makes it easier to pause, resume, or hand the work to another session.
The handoff is manual. I choose when to open a session and carry the result back. The orchestrator and worker sessions can also use different agent CLIs, with the prompt adapted to the capabilities available.
It’s open source and still a work in progress. I’m using it to learn what helps in practice and where the workflow needs to improve. Installation has been checked for Codex, Claude Code, and Gemini CLI, but the full workflow hasn’t been tested across every CLI or version.
If you try it, I’d be interested in hearing what works and where it gets in your way.
