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 Β· Directory Structure Β· Context Setup Β· Core Workflow Β· Tasks Β· Projects Β· Best Practices
What is This?
Sams Product OS is an AI-powered personal operating system to organize my PM workspace
- Three-Bucket Workflow - Backlog β Active β Archive keeps you focused
- Backlog Processing - Brain dump β 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 β required to use this system
- Node.js β 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 β stay in
tasks/BACKLOG.md(organized under topic headers) - Opportunities β
knowledge/opportunities/(observed problems and ideas to explore) - References β
knowledge/references/(useful context)
Plan your week:
Move items from tasks/BACKLOG.md into tasks/ACTIVE.md:
- In Progress β working on now
- Up Next β committed this week
- Waiting On β blocked on someone else
Start here, build as you go.
GOALS.mdandtasks/BACKLOG.mdare the core β fill those in first. Add a few files toknowledge/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 β add them when you feel the friction of not having them.
Directory Structure
sams-product-os/
βββ tasks/ # Simple backlog β active β archive flow
βββ knowledge/ # Persistent reference material & agent-learned context
βββ projects/ # Discrete work with its own context, research, and outputs
βββ meetings/ # Meeting notes and transcripts
βββ templates/ # Document structures for consistent outputs
βββ _temp/ # Scratch work and files in transit
βββ tools/ # API integrations and custom tooling
βββ .claude/skills/ # Slash commands and agent capabilities
βββ GOALS.md # Quarterly goals and ownership areas
βββ AGENTS.md # Agent instructions
βββ 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 β one file per significant decision |
opportunities/ | Observed problems and ideas to explore β groomed feature requests, market signals, patterns |
people/ | (Optional) One file per person β direct reports, stakeholders, key peers. Useful at manager/director/VP level. |
AI reads files in priority order: about-me/ β product-strategy/ β company-context/ β 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 β 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.mdand subdirectoryAGENTS.md+CLAUDE.mdfiles (agent instructions for each folder)
Gitignored (your data):
GOALS.md,VOICE-GUIDE.md- Content in
tasks/,knowledge/,projects/,meetings/,_temp/ - Note:
AGENTS.mdandCLAUDE.mdinside any folder are always tracked
Core Workflow
tasks/BACKLOG.md β /process-backlog β Tasks (stay in backlog) / Opportunities / References
β weekly planning β tasks/ACTIVE.md β tasks/_archived/YYYY-MM.md
- Brain dump to
tasks/BACKLOG.mdthroughout the day - Process with
/process-backlogβ AI classifies items, creates opportunity and reference files - Plan β Move items into
tasks/ACTIVE.mdfor the week: In Progress, Up Next, Waiting On - Archive β Log completed work to
tasks/_archived/YYYY-MM.mdduring weekly review
Tasks
Three-Bucket System
Tasks live in three files.
tasks/BACKLOG.md β 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 β This week's focus. Three sections:
# Active β Week of Apr 7β11
**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 β Monthly retrospective. Logged at week-end.
## Week of Apr 7β11
### Shipped
- Pricing experiment launched to 10% of users
### Completed
- PRD draft reviewed and approved
Managing Tasks
Daily:
/daily-pulseβ morning briefing with calendar + active tasks- "What am I working on?" β agent reads
tasks/ACTIVE.md - "Show my backlog" β agent reads
tasks/BACKLOG.md - Brain dump into
tasks/BACKLOG.md
Weekly:
/process-backlogβ classify and clean the backlog/weekly-reviewβ review progress, plan next week, log to archive
Projects
A project is committed discrete work β a clear objective, connected to a goal, with real outputs. One folder per project in projects/.
projects/
βββ checkout-redesign/
βββ brief.md # From templates/project-brief-template.md
βββ research.md
βββ 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 β 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.mdinto organized tasks, opportunities, references - Deduplication and goal-alignment checks
Daily Pulse (/daily-pulse):
- Morning briefing β 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/
βββ voice-samples/
β βββ email-to-colleague.md
β βββ email-to-exec.md
β βββ slack-messages.md
β βββ linkedin-post.md
β βββ blog-post-excerpt.md
β βββ 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
Daily:
- Brain dump to
tasks/BACKLOG.mdthroughout the day - Ask "what am I working on?" to check active tasks
Weekly:
/process-backlogto classify and clean/weekly-reviewto reflect, plan, and archive- Update
tasks/ACTIVE.mdat the start of each week
Context:
- Start small β add context as you go
- Update voice samples quarterly
Tips:
- Use @ mentions:
@knowledge/product-strategy/ - Process 3-5 backlog items at a time, not 50
- Keep ACTIVE.md focused β if you can't finish it this week, it belongs in the backlog
- Install additional skills from the plugin marketplace
Troubleshooting:
- Generic responses? Add more to
knowledge/ - AI not using context? Use @ mentions explicitly
- Overwhelmed by backlog?
/process-backlogto declutter
License
MIT β Copyright (c) 2026 Sam Kawsarani
