// Process sales call recordings into structured customer intelligence. Use when the user asks to process a call transcript, create or update a deal doc, add to the playbook or glossary, or do any work involving call transcripts, deal qualification, or sales reference material.
Detect repetitive AI defaults for a domain and generate a reusable avoidance profile. Use when asked to "unslop", "de-slop", "remove AI patterns", "make less generic", generate an anti-slop profile, or analyze AI output patterns for any domain (writing, landing pages, emails, code, etc.).
Audit and reconcile Thomas's todo list against all active data sources — calendar, email, customer deal docs, deal tracker spreadsheet, and any future sources. Use when Thomas asks to "review my todos", "what's outstanding", "todo audit", "anything I'm missing", "check my follow-ups", "what's stale", or as part of a Monday morning / Friday EOD review. Also use when he asks about overdue items, missed follow-ups, or wants a pipeline hygiene check.
Design things that AI agents can operate effectively — CLIs, tools, file formats, codebases, context layers, skills, visual assets, and any interface where agents are the primary or frequent consumer. Use when building a CLI for agents to use, structuring a codebase or repo for AI-assisted development, choosing file formats for agent-readable content, designing MCP tools or skills, creating agent-operable assets (HTML templates, markdown-driven workflows), setting up context infrastructure (AGENTS.md, SKILL.md, progressive disclosure), or making any existing system more agent-friendly. Also use when the user asks about agent DX, agent UX, or how to make something easy for agents to work with.
Extract session cookies from Chrome on macOS by decrypting Chrome's cookie database. Use when you need to authenticate API calls using the user's existing browser session — e.g., refreshing expired session cookies, accessing a service the user is logged into, or bootstrapping API access for a tool that uses cookie auth.
| name | customer-intel |
| description | Process sales call recordings into structured customer intelligence. Use when the user asks to process a call transcript, create or update a deal doc, add to the playbook or glossary, or do any work involving call transcripts, deal qualification, or sales reference material. |
Process sales calls into structured customer intelligence: cleaned transcripts, deal qualification docs, playbook entries, and glossary terms.
This is a starter template. Customise the repo structure, deal doc template, and workflow to match your team's sales process.
Create a private repo for your team (e.g., your-org/customers):
customers/
├── _templates/deal-doc.md # Your deal qualification template (MEDDPICC, BANT, etc.)
├── _reference/
│ ├── playbook.md # Sales patterns, objections & responses
│ └── glossary.md # Industry-specific terms
├── <customer>/
│ ├── <date>-<description>.md # Cleaned transcript(s)
│ ├── deal.md # Deal qualification doc
│ └── changelog.md # What changed and why
Get the transcript from your meeting recording tool (e.g., Granola, Gong, Otter.ai, Fireflies). Ask Pi: "process my call with [customer]" — it will search your connected meeting tools.
Using your deal qualification framework (MEDDPICC, BANT, SPICED, etc.):
If the call revealed:
Changes should be committed and pushed immediately so teammates have the latest intel.
To set this up for your team:
_templates/deal-doc.md_reference/playbook.md