Personal Project
A context-engineered AI copilot that actually knows my company, my stakeholders, and my work.
Overview
The problem
Most PMs use AI the same way they use Google: one-off questions, zero context. The output is generic, the loop never compounds, and every new document starts from scratch.
The thesis
Context engineering over prompt engineering. A good prompt is table stakes. What produces senior-PM-quality work is a persistent layer of company context, writing style, stakeholder preferences, past decisions, and live analytics — all readable by the model on every call.
The system
Three layers on top of Claude Code: (1) a Context Library of company info, writing styles, stakeholder profiles, PRDs, strategy, research, decisions, meetings, and metrics — referenced automatically on every interaction; (2) 41 Skills covering the full PM loop — strategy, research, PRDs, metrics, meetings, launches, retros — each auto-routing to related context and chaining into the next; (3) 7 Sub-Agents (engineer, designer, executive, legal, UXR, skeptic, customer voice) that critique work in parallel before it ships. Optional MCP connectors for Amplitude, Linear, Dovetail, and other tools route natural-language questions to live data, not stale exports.
What it produces
PRDs drafted against my real strategy. Meeting notes with timeline conflict detection against committed launch dates. Stakeholder comms that match each exec's documented preferences. Post-launch retros that feed calibration data back into the next impact-sizing cycle. A self-updating learning log that captures writing-style corrections, stakeholder patterns, and estimate-vs-actual accuracy.
Outcomes
PRDs drafted in a fraction of the time, against real strategy. Meeting notes same-day instead of batched at end-of-week. Stakeholder updates that arrive earlier and read like they came from someone who knows the room. Decisions captured with rationale as they happen.
Architecture
Three layers. One orchestrator. A compounding loop.
Claude Code reads across all three layers on every invocation. The Context Library grounds output in real work; Skills route to relevant context automatically; Sub-Agents critique in parallel when depth is needed. Finalized work writes back to the Context Library — so the system tomorrow knows what happened today.
Claude Code
Reads across all layers on every invocation
Context Library
41 Skills
7 Sub-Agents
A Day in Practice
Each skill feeds the next.
The system at 6pm knows more than at 9am. Every output becomes context for what follows. Tomorrow starts where today ended.
9 am
/daily-plan
Start of day
Pulls calendar, active PRDs, open action items, and live analytics → prioritized plan for the day
10 am
/meeting-notes
Customer interview
Structured notes, decisions, action items, and verbatim quotes — written back to the Context Library
customer quote now available to all future skills
11 am
/prd-draft
Feature scoping
The morning's customer quote appears automatically as supporting evidence — the skill reads the meeting notes without being told to
1 pm
/meeting-notes
Stakeholder review
Flags a timeline conflict: stakeholder said "three weeks" — system finds a committed launch date in a PRD that contradicts it
conflict surfaced before it becomes a problem
2 pm
/impact-sizing
New request arrives
References past estimate-vs-actual accuracy from the learning log — confidence intervals calibrated to my actual track record
3 pm
/prd-review-panel
Draft review
Fires 7 sub-agents simultaneously — engineer, designer, executive, legal, UXR, skeptic, customer voice
all seven run in parallel, not in sequence
5 pm
/status-update · /slack-message
End of day
Status update pulls from the full day's context — decisions, blockers, progress. Slack drafts tone-matched per stakeholder from their documented preferences.
all outputs written back → tomorrow starts here
Why it matters for a Senior PM role
This is what daily PM work looks like when you stop treating AI as a chatbot and start treating it as a system you design. Ships faster, aligns stakeholders earlier, compounds learning across quarters.
It's also evidence of how I'd approach building internal tooling for a product team: context first, workflow second, model last.