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Sams Product Os

AI powered personal operating system - how I organize my PM workspace

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SAMS PRODUCT OS

Turn your AI assistant into a product management partner. Process ideas, generate specs, prioritize strategically.

Quick links: Quick Start · Directory Structure · Context Setup · Core Workflow · Tasks · Projects · Best Practices

What is This?

Sams Product OS is an AI-powered personal operating system to organize my PM workspace

Three-Bucket Workflow - Backlog → Active → Archive keeps you focused
Backlog Processing - Brain dump → Organized tasks/opportunities/references
Document Generation - Specs, briefs, PRDs from conversation
Research Synthesis - Transform interviews into insights
Voice Training - Match your writing style

Quick Start

1. Clone the Repo

git clone https://github.com/samkawsarani/sams-product-os.git
cd sams-product-os

2. Prerequisites

Claude Code — required to use this system
Node.js — optional, needed for QMD semantic search. The agent falls back to file search without it.

3. Run Setup

./setup.sh

Creates your workspace, sets up knowledge base directories, seeds starter files, and optionally installs QMD and plugins.

4. Start Using It

Brain dump to tasks/BACKLOG.md:

## Product
- Follow up with Sarah about Q4 goals

## Strategy
- Mobile Performance Issues
  - Source: Support tickets (15 this week)
  - Context: Android app slow on startup

Process your backlog:

/process-backlog

AI categorizes into:

Tasks → stay in tasks/BACKLOG.md (organized under topic headers)
Opportunities → knowledge/opportunities/ (observed problems and ideas to explore)
References → knowledge/references/ (useful context)

Plan your week:

Move items from tasks/BACKLOG.md into tasks/ACTIVE.md:

In Progress — working on now
Up Next — committed this week
Waiting On — blocked on someone else

Start here, build as you go. GOALS.md and tasks/BACKLOG.md are the core — fill those in first. Add a few files to knowledge/ about your role, company, and strategy; the agent gets meaningfully smarter with even basic context, and you can add more over time. Voice training and plugins are optional — add them when you feel the friction of not having them.

Directory Structure

sams-product-os/
├── tasks/          # Simple backlog → active → archive flow
├── knowledge/      # Persistent reference material & agent-learned context
├── projects/       # Discrete work with its own context, research, and outputs
├── meetings/       # Meeting notes and transcripts
├── templates/      # Document structures for consistent outputs
├── _temp/          # Scratch work and files in transit
├── tools/          # API integrations and custom tooling
├── .claude/skills/ # Slash commands and agent capabilities
├── GOALS.md        # Quarterly goals and ownership areas
├── AGENTS.md       # Agent instructions
└── setup.sh        # Interactive setup

Context Setup

The knowledge/ folder is your AI's long-term memory. It has two types of content:

Reference Context (set up by you)

Files you create once and update as things change. The AI reads these to understand who you are, how your team works, and what your strategy is.

Folder	What to put there
`about-me/`	Role, background, working style, strengths
`company-context/`	Mission, products, team, org structure
`product-strategy/`	Vision, strategic pillars, roadmap, OKRs
`processes/`	How your team works, sprint cadence, decision-making
`references/`	Competitive research, articles, open requests
`voice-samples/`	Writing samples for style matching (see Voice Training)
`decisions/`	Decision log — one file per significant decision
`opportunities/`	Observed problems and ideas to explore — groomed feature requests, market signals, patterns
`people/`	(Optional) One file per person — direct reports, stakeholders, key peers. Useful at manager/director/VP level.

AI reads files in priority order: about-me/ → product-strategy/ → company-context/ → task-relevant folders.

Reference files explicitly with @knowledge/product-strategy/current-strategy.md.

Domain Learning (maintained by your agent)

As you work, your agent builds up learned knowledge in domain-specific folders (e.g., knowledge/payments/, knowledge/checkout-flow/). Each domain folder holds three files: knowledge.md (facts), hypotheses.md (patterns under observation), and rules.md (confirmed — applied by default). The agent creates these automatically; you never pre-populate them. See knowledge/AGENTS.md for the full knowledge architecture and maintenance rules.

See knowledge/INDEX.md for a directory of what's in your knowledge folder.

What Gets Committed vs. Gitignored

Committed (shared structure):

Directory structure, templates, .claude/skills/
AGENTS.md and subdirectory AGENTS.md + CLAUDE.md files (agent instructions for each folder)

Gitignored (your data):

GOALS.md, VOICE-GUIDE.md
Content in tasks/, knowledge/, projects/, meetings/, _temp/
Note: AGENTS.md and CLAUDE.md inside any folder are always tracked

Core Workflow

tasks/BACKLOG.md → /process-backlog → Tasks (stay in backlog) / Opportunities / References
                 → weekly planning → tasks/ACTIVE.md → tasks/_archived/YYYY-MM.md

Brain dump to tasks/BACKLOG.md throughout the day
Process with /process-backlog — AI classifies items, creates opportunity and reference files
Plan — Move items into tasks/ACTIVE.md for the week: In Progress, Up Next, Waiting On
Archive — Log completed work to tasks/_archived/YYYY-MM.md during weekly review

Tasks

Three-Bucket System

Tasks live in three files.

tasks/BACKLOG.md — Brain dump inbox. Bullets organized by topic header. Not committed work yet.

## Product
- Follow up with Sarah about Q4 goals

## Strategy
- Research competitive pricing changes

tasks/ACTIVE.md — This week's focus. Three sections:

# Active — Week of Apr 7–11
**Focus:** Ship the pricing experiment

## In Progress
- [ ] Review PRD draft with eng lead

## Up Next
- [ ] Schedule merchant feedback call

## Waiting On
| Who | What | Since | Next step |
|-----|------|-------|-----------|
| Legal | Contract review | Apr 8 | Follow up if no word by Apr 10 |

tasks/_archived/YYYY-MM.md — Monthly retrospective. Logged at week-end.

## Week of Apr 7–11

### Shipped
- Pricing experiment launched to 10% of users

### Completed
- PRD draft reviewed and approved

Managing Tasks

Daily:

/daily-pulse — morning briefing with calendar + active tasks
"What am I working on?" — agent reads tasks/ACTIVE.md
"Show my backlog" — agent reads tasks/BACKLOG.md
Brain dump into tasks/BACKLOG.md

Weekly:

/process-backlog — classify and clean the backlog
/weekly-review — review progress, plan next week, log to archive

Projects

A project is committed discrete work — a clear objective, connected to a goal, with real outputs. One folder per project in projects/.

projects/
└── checkout-redesign/
    ├── brief.md        # From templates/project-brief-template.md
    ├── research.md
    └── outputs/

Each project brief has:

# Checkout Redesign

**Goal:** [Which goal from GOALS.md does this serve?]
**Status:** Active | On Hold | Complete
**Started:** YYYY-MM-DD

## Objective
## Target Customer
## Success
## What I Believe
## What I Need to Research
## Solution Directions
## Risks to Validate
## Updates

Active projects generate tasks — reference the project folder when adding related items to tasks/ACTIVE.md or tasks/BACKLOG.md.

Skills

This is the base project with core skills built in. Install additional skills from the plugin marketplace to extend your capabilities.

Built-in Skills

Process Backlog (/process-backlog):

Process tasks/BACKLOG.md into organized tasks, opportunities, references
Deduplication and goal-alignment checks

Daily Pulse (/daily-pulse):

Morning briefing — calendar + active task priorities
/daily-pulse tomorrow: tomorrow look-ahead
/daily-pulse week: week overview

Weekly Review (/weekly-review):

Reflect on past week, plan next week, log to archive
/weekly-review quick: condensed version

Weekly Update (/weekly-update):

Draft a stakeholder update email
Uses Linear projects and initiatives if MCP is connected, falls back to tasks/ACTIVE.md
Reads knowledge/people/ for stakeholder preferences

Plugin Marketplace

Browse and install additional skills (analytics, grooming, research, writing, and more) from the Sams Product Plugins marketplace.

Add the marketplace:

/plugin marketplace add samkawsarani/sams-product-plugins

Install a plugin:

/plugin install {PLUGIN-NAME}@sams-product-plugins

Voice Training

Give AI examples of your actual writing, then have it extract patterns and apply them.

Step 1: Collect Your Writing Samples

Gather 5-10 examples of your real writing. Mix of formats works best:

knowledge/
├── voice-samples/
│   ├── email-to-colleague.md
│   ├── email-to-exec.md
│   ├── slack-messages.md
│   ├── linkedin-post.md
│   ├── blog-post-excerpt.md
│   └── product-spec-intro.md

What makes good samples:

Emails you actually sent (not templates)
Slack messages that got good responses
Posts that felt authentically "you"
Writing you're proud of

What to avoid:

Heavily edited/formal documents
Writing you copied from somewhere
Samples that don't represent your natural style

Step 2: Run Voice Analysis

Ask your agent to analyze your samples:

Read all files in knowledge/voice-samples/ and analyze my writing style.
Extract specific patterns for:
1. Sentence structure and length
2. How I open and close messages
3. Words/phrases I use often
4. What I never say
5. Tone and formality level
6. How I structure arguments

Create a voice guide.

Step 3: Review the Voice Guide

Your agent will produce something like:

## Your Writing Voice

### Sentence Style
- Short sentences. Punchy. You rarely go over 15 words.
- Questions used to transition: "So what does this mean?"
- Em dashes avoided - you use commas or periods instead

### Openers
- Emails: Jump straight to the point, no "Hope you're well"
- Posts: Lead with a surprising fact or bold claim
- Specs: Start with the user problem, not the solution

### Signature Phrases
- "Here's the thing..."
- "Let's be real"
- "The short version:"
- Ends with clear next step or question

### What You Avoid
- "I hope this email finds you well"
- "Please don't hesitate to reach out"
- Bullet point lists in emails (prefer paragraphs)
- Emojis in professional context
- "Key insights" or "learnings"

### Tone Calibration
- To peers: Casual, direct, occasional humor
- To execs: Concise, data-first, clear ask
- Public posts: Confident but not salesy

Step 4: Save It (Optional)

Option A: Save to VOICE-GUIDE.md (faster, more consistent)

Save this to VOICE-GUIDE.md in the project root.

This file is gitignored. The agent automatically checks for it when drafting content.

Option B: Skip saving (simpler, always fresh) Let the agent read from knowledge/voice-samples/ each time you ask it to match your voice.

Step 5: Test and Refine

Draft an email to my VP about pushing the launch date back one week.
Match my voice from VOICE-GUIDE.md (or knowledge/voice-samples/).

Compare the output to how you'd actually write it. Give feedback:

Good start, but I wouldn't say "I wanted to reach out" - I'd just say
"Quick update on launch timing." Also too many bullet points, I usually
write in short paragraphs. Try again.

Maintenance: Add new samples monthly as your voice evolves.

Read my recent writing in [location] and update my voice guide.
What patterns have changed? What's new?

Best Practices