Everything you build around the model — the instructions, the tools, the error handling, the pipeline — developers call this the harness. Mitchell Hashimoto, who built Terraform, gave it a simple rule: every time your agent makes a mistake, engineer a fix so it never makes that mistake again.

All of this research comes from the engineering world. But I applied the same principles to my trendwatching agent — and it brought me 85K impressions on LinkedIn in 3 days by finding the hottest.

The Context

(skip if you want to apply harness asap)

When there’s no marketing budget, you ARE the marketing. For me that meant LinkedIn — and LinkedIn meant knowing what’s trending before everyone else. That was 3 hours of my morning, every morning. I tried to automate it. Ten times.

The first six versions were a slow education in what doesn’t work. I started by dumping everything into one Claude conversation — worked until the context window filled up and the model forgot what it had already read. Then I vibe-coded Python scripts with a scoring system, but spent more time calibrating scores than the system was saving me. Then I added 9+ sources across languages and the volume drowned the model in noise — hallucinations, missed items, everything still crammed into one agent’s context.

Version 7 broke me. I tried giving every single content piece its own dedicated agent. Three hours of compute that never resolved. Then the opposite — blind parallel pipelines with no monitoring. 45 minutes, garbage output. Both extremes failed for the same reason: I was engineering around the wrong problem.

Version 9-10 was a full rebuild. Monitored background agents instead of blind subprocesses. Evals at every phase with fallback instructions for when things break. An orchestrator that takes over when subagents fail. The specifics are the seven principles below.

Runtime went from 45 minutes to 15, reliability went from “maybe works” to something I’d actually stake my LinkedIn on — and it’s still the same Claude.

Here are 6 harness principles I applied:

1. Give the AI less, not more

Version 7 ran for 3 hours and never finished because the agent was doing everything — fetching data, routing APIs, handling files, analyzing, scoring, formatting output. I kept throwing more at Claude because that’s what felt natural — more tools, more responsibility, more context — and three hours later I had nothing.

I split the work — Python fetches, Claude analyzes. The scoring agent sees only the scoring rubric and the raw items, no pipeline logic, no historical context, and scores improved immediately.

Vercel learned this too. Their text-to-SQL agent had 17 tools. They deleted 15, gave the model bash and file access. 100% accuracy. 3.5x faster. 37% fewer tokens. The model wasn’t confused by the task — it was confused by the choices.

Birgitta Bockeler from Thoughtworks: “Increasing trust and reliability requires constraining the solution space rather than expanding it.”

2. Evals and Plan B

My clusterer received hundreds of scored items, silently dropped most of them, and delivered a report with a handful — acting like everything was fine. I only caught it because the output looked suspiciously thin.

The fix: after every phase, run a check. Did the output contain as many items as the input? Is the JSON valid? Did the scores fall within expected ranges? If any check fails, don’t retry the same agent — have the main system take over and do the work itself.

Three lines of eval logic after each step caught more failures than weeks of prompt tweaking ever did.

Here’s what my evals actually check:

• After scoring: item count in = item count out. No drops.

• After clustering: every scored item appears in exactly one cluster.

• After writing: every item is in the report.

If any check fails, the orchestrator does that step directly. No retries, no second chances for the agent.

Stripe’s coding agents enforce the same pattern: can’t fix it in 2 rounds? Stop. Hand to human. Devin had no such limit — independent testing: 15% success rate.

3. One agent is usually better than many

I use multiple agents — but only for genuinely parallel work. When I need to score hundreds of items, I split them into batches and run scoring agents in parallel. Each batch is independent — item #47 doesn’t depend on item #248 — so parallelism works.

But the overall pipeline is strictly sequential because each phase depends on the previous one, and when I tried parallelizing phases that shouldn’t be parallel, it broke immediately — the clusterer was reading scores that hadn’t been written yet.

Princeton found single agents beat agent teams 64% of the time. Google tested 180 configurations — multi-agent degrades sequential tasks by up to 70%.

If your work is genuinely independent — split it across agents. If it’s sequential — keep it in one.

4. Build a filing system, not a memory dump

My early versions crammed everything into one conversation. By version 6, the context window was so full that Claude started forgetting earlier parts of the run — mixing up sources, duplicating scores, losing track of what it had already processed.

Each scoring agent now gets exactly one batch to read and one scored file to write — the orchestrator holds the big picture, but individual agents never see the whole pipeline, and keeping each agent’s context small and focused is what finally made your scoring reliable.

MEMORY LAYERS

Always loaded:  CLAUDE.md (the constitution)
On demand:      Batch files (one per agent)
Never loaded:   Full run history (searched, not read)

The system has a CLAUDE.md file — audience calibration, engagement normalization rules, some secret sauce — loaded at the start of every session. Not the full history — just the constitution. Hashimoto calls this AGENTS.md: every time an agent makes a mistake, the fix becomes a permanent rule in the file. My CLAUDE.md has grown the same way — each line traces back to a bad run.

This scales. 11 out of 12 models drop below 50% accuracy at 32,000 tokens. A well-curated 30K-token context beats an uncurated 120K-token context every time.

5. Kill before build

My biggest deletion: the entire subprocess architecture from version 7. Spawn, wait, hope — no monitoring, no intervention, no visibility. Replaced it with background agents the main system monitors in real time. If an agent fails, the system sees it in seconds and takes over.

Another one: I tried a cheaper model for scoring to save costs. It produced broken JSON that crashed everything downstream. Switched back to the expensive model for all agents, zero exceptions. Sometimes the “optimization” is the problem.

Manus deleted their todo tracker — it ate 30% of tokens doing nothing. Rentier Digital deleted their memory subsystem— 2.3-second latency improvement.

Audit your system regularly. If a component isn’t earning its cost — kill it.

6. Every step must earn its place

The compound reliability math:

I started with more phases than I have now — separate validation steps, pre-processing, post-processing, reformatting stages — and each one added marginal quality but cost reliability, so the net was negative and I cut back to the minimum where each phase does real work.

Same inside each phase. My scoring prompt started as a long document with examples, edge cases, scoring histories, anti-patterns. I trimmed everything around the core: the dimensions, the weights, the normalization table, and one strict rule — score EVERY item, drop NOTHING. Shorter prompt, better scores.

What I’d do differently on day 1

The counterintuitive lesson from 10 rebuilds: more is less. If you’re reaching for more agents, more tools, more context — it will probably make your system worse, not better.

If I started over tomorrow, I’d build the eval framework before writing a single agent, and I’d recommend you do the same. Every version that failed taught me the same thing: I didn’t know it was failing until it was too late. Evals first, agents second, optimization never.

Phil Schmid said it best: “The harness is the dataset. Competitive advantage is the trajectories your harness captures.” Every failure my system logs makes the next run better. That’s the real compounding — not a better model, but a smarter harness.

The 6-point harness audit

Before you rebuild anything, run this on what you have now. One “no” = the highest-impact fix you can make today.

• Less, not more. Is your agent doing tasks that deterministic code could handle? (Fetching, routing, formatting — move to scripts. And check what the model actually sees — if the input is mangled, no prompt will save you.)

• Evals and Plan B. What happens when a step fails silently? Do you have an assertion checking output count/quality after each phase?

• Single agent default. Are you using multiple agents for work that’s actually sequential? (If step 2 depends on step 1’s output — that’s one agent, not two.)

• Scoped context. Does each agent see only what it needs? Or does it get the full conversation history?

• Recent deletions. When did you last remove a component that wasn’t earning its cost?

• Step tax. How many steps in your pipeline? Multiply: 0.95^N = your theoretical reliability ceiling. Is every step worth the tax?

What’s the thing you kept trying to fix with a better model before you realized the model wasn’t the problem?

Thank you to my founding members — Kostas Nasis, Artem Krivonos, and Kristina Hananeina. Your early support made this newsletter real.

A year ago I opened a terminal for the first time since high school — dark screen, blinking cursor, zero buttons.

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Hey! I’m Alena, former AI startup CEO ($2M raised), Yandex and Acronis Sr. PM with 10k LinkedIn followers, 57% of my followers are senior leaders from big tech, good company ;)

There are tasks that should be delegated to AI.

And then there are tasks where you need to build a custom project – one that works for you, and only you.

(I’ve included the full free template for AI personal assistant at the end of this post. I spent over 50 hours debugging the prompts so you can set this up in 5 minutes.)

There are about 100 popular AI-assisted to-do lists popping up right now.

Teresa Torres, author of Continuous Discovery Habits and an internationally acclaimed coach, explains this proliferation simply:

“How we manage our tasks is so idiosyncratic that this is exactly the type of thing that you should build for yourself because it can work exactly the way you want it to work.”

I couldn’t agree more.

I’ve been setting personal and professional goals for the last 10 years. I rarely achieve even half of them. Usually, things lose relevance, or I simply forget they exist. This year, I decided to outsource the executive function to AI.

Imagine having a personal assistant with the skills of an executive coach, ready to help you 24/7 for $0 a month. Top CEOs have Chiefs of Staff. Professional athletes have performance coaches. Why do we expect ourselves to manage our complex lives alone?

The assistant that doesn’t just manage your calendar. It manages you. It knows when you’re lying to yourself about “just checking email.” It knows when you’re catastrophizing. And it knows exactly how to get you back on track without making you feel guilty.

But first, I did some research to find the most scientifically validated techniques for goal setting and tracking.

The science

Part 1: How to set and track goals

It turns out, simply “set SMART goals” isn’t enough. Here are the four evidence-backed frameworks I’m using:

1. Implementation Intentions (Gollwitzer, 1999)

The structure is simple: “If [Situation], then I will [Action].”

• Example: “If it is 9:00 AM on Monday and I open my laptop, then I will write for 25 minutes before checking email.”

2. WOOP (Oettingen, 2014)

Wish → Outcome → Obstacle → Plan

• Wish: What do you want?

• Outcome: What will it feel like?

• Obstacle: What internal barrier stops you?

• Plan: If [Obstacle], then [Action].

Fun Fact: Positive visualization without visualizing the obstacles actually reduces the likelihood of achieving the goal. You need to prepare for the resistance.

3. Learning vs. performance goals (Locke & Latham, 2002)

I personally love this concept.

• Performance Goals: Good for familiar tasks (e.g., “Increase conversion rate by 2% in Q1”).

• Learning Goals: Essential for new or complex territory.

  • Bad: “Hit 10k subscribers.” (You can’t fully control this).

  • Good: “Run 5 experiments to understand what drives subscription growth.” (I will learn/understand X).

Fun Fact: The SMART methodology lacks a strong evidence base for complex, ambiguous goals. It often forces false certainty.

4. Written Goals + Accountability (Matthews, 2015)

The formula: Write the goal → Share with 1 person → Schedule weekly check-ins.

Part 2: How to track goals & maintain motivation

The Weekly Check-in (Harkin et al., 2016)

Action: Every week, ask yourself: “Am I closer to the goal than I was a week ago? If not, why?”

If you lose motivation → Goal Gradient (Kivetz et al., 2006)

Action: Break the goal into 5-10 stages. Show progress as “X% done” (not “Y% remaining”). Make the first 10-20% easy wins to build momentum.

If you forget to act → Implementation Intentions

Action: Write “If [Situation], then [Action]” and place a reminder at the physical trigger point.

If you have too many goals → Sequential Focus

Action: Maximum 3 goals per quarter. 1 MIT (Most Important Task) per day. Finish one before adding another.

If you’re stuck and don’t know why → WOOP Diagnostic

Action: Ask yourself: “What INSIDE ME is stopping this? What feeling am I avoiding?”

Part 3: How I picked goals

To start, I wrote down 100 wishes. It was hard. My imagination ran dry after the first 20, but that’s when the interesting stuff started coming out (e.g., “start taking golf lessons” or “take a course at the Art Institute”).

For the final list, I divided all goals into 3 groups:

1. One-off Projects: Finite tasks with a clear end.

2. Pillars: Ongoing areas requiring constant engagement (Startup, Consultancy, Personal Brand).

3. Side Quests: Everything else – from learning to make pizza to visiting my 41st country.

Important: I intentionally did not separate “Work” and “Life.” Work-life balance is one of my goals this year, and I plan to achieve it by freeing up more time for rest using AI.

The build

Here is how I actually built it:

1. Created a new project. I use Cursor, but you can use Obsidian, Antigraviti, Claude Projects, ChatGPT Projects or just a folder on your desktop. The magic is that it’s just text files – no vendor lock-in.

2. Uploaded my goals. I literally took a photo of my notebook and had Claude transcribe it.

3. Created a Context File. This defines work, family, time constraints, and cognitive distortions.

4. Uploaded Frameworks. I fed the AI the scientific frameworks for goal setting and tracking (WOOP, etc.).

5. Tasked Claude Code. I asked it to organize the project structure based on all inputs.

6. Tasked Gemini 3 Pro. I asked it to review the structure.

7. ...Several iterations of edits...

8. ...2 weeks of debugging... (I added a simple database for task tracking and a calendar database).

Profit.

The final structure

goals-template-2026/
├── Vision_2026.md          # The main map (generic categories)
├── Memory.md               # AI Instructions (Persona & Rules)
├── README.md               # Setup Guide
├── Pillars/
│   └── pillar-template.md  # Template for recurring systems
├── One-off projects/
│   └── project-template.md # Template for finite projects
├── Side quests/
│   └── quest-template.md   # Template for hobbies/fun
└── frameworks/
    └── project_template.md # The master template to copy

The context

I added two critical layers of context to my system:

1. Cognitive Distortions: The psychological patterns I’m currently working on.

2. Life Constraints: My reality – like having a small child, which means plans often change unexpectedly.

I also applied the concept of Progressive Disclosure when designing my AI coach.

The result

I now have a Coach/PA that helps me:

• Set goals in a way that makes them achievable.

• Track those goals without the manual headache.

• Identify blockers (both external and internal).

• Overcome demotivation.

• Work through my cognitive distortions in real-time.

• Keep all related notes and information related to each project.

• Balance work and life.

• Work through my cognitive distortions in real-time.

• My most favorite feature so far: organize meeting transcripts into tasks and allocate to appropriate projects.

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This week I went deep on AI usage research — and the data is surprising. Turns out 52% of open-source AI usage is... roleplay. Gemini has better retention than ChatGPT. And “act as an expert” prompting? Wharton proved it doesn’t actually work. Microsoft dropped a similar usage report. It’s benchmark panic season.

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Interviews are forgiving — you adjust questions on the fly, you follow up, you rephrase when someone looks confused.

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You’ve surely heard about MCP over the last year and about Skills for the past 2 weeks, both concepts by Anthropic, both can be used to increase your AI capabilities. MCP was still a pretty “dev required” thing—you need an API key at least. Turned out that Anthropic realized that MCP doesn’t really fulfill user’s needs and came up with the Skills concept. And like MCP, it can be used with any AI; I tested it in Cursor. So you don’t really need to lock into Claude—think about it as an instructions template that is highly consumable for AIs.

In this issue I want to show you what Skills are at their core, best practices and limitations to consider, and how to find, build, customize and manage skills.

What’s Inside

• The Matrix moment — Why Skills changes everything you know about AI automation

• Token economics — How to cut costs 80% while fixing hallucinations

• Skills vs MCP — Why Anthropic quietly abandoned their own protocol

• 30-minute build — Your first Skill that actually works (Meeting Notes → Action Items)

• Security blindspots — What malicious Skills can do + protection protocol

• The Cursor edge — Why some teams get 3x better results

• Early adopter notes — Current limitations & workarounds that work

What are skills from the first principles

Remember how Trinity in The Matrix downloaded a helicopter piloting Skill? That’s pretty much it. If your Agent (can be a general agent like ChatGPT or specialized like Claude Code) has a skill, whenever you set a task that can be solved with that skill it will find, download and use it on demand.

If you have repetitive tasks or prompts that you store somewhere and paste every time you need to perform a specific task, Skills is the solution. Instead of explaining your process every single time you need something done, you create a Skill once – and Claude remembers it forever. Need a weekly status report? There’s a Skill for that. Want consistent brand colors in every presentation? Create a Skill. Have a specific way you analyze customer feedback? Package it as a Skill.

A Skill is a folder containing:

1. A SKILL.md file - This is your instruction manual. It tells Claude what this Skill does, when to use it, and exactly how to do it. You write this in plain text using simple formatting.

2. Optional extras - You can add template files, reference documents, example outputs, or even small scripts if you’re comfortable with that.

At its core, it has the idea of progressive disclosure (like in The Matrix) meaning that at every single moment it uploads only the necessary amount of information (=tokens), which saves you from two main problems:

→ Hallucinations and mistakes because it doesn’t contain huge unrelated context

→ High costs because it doesn’t upload all context, only what’s necessary

Here’s how it works in practice. When you start a conversation with Claude, it sees a short description of each available Skill (like “Creates weekly team reports with wins, blockers, and priorities”). These descriptions are tiny – they barely use any of Claude’s “memory” for the conversation.

When you ask for something that matches a Skill description, Claude thinks “Oh, this person needs a weekly team report!” and then – and only then – it opens up that Skill folder and reads the full instructions. It’s like having a filing cabinet where Claude can instantly grab the right procedure when needed.

What makes Skills different from other features:

• Custom instructions - Those brief, always-active settings apply to everything. Skills are detailed procedures that activate only when relevant.

• Projects - These are workspace-specific and accumulate context over time. Skills are portable recipes you can use anywhere.

• MCP (Model Context Protocol) - This connects Claude to external data sources like your CRM or Google Drive. The main problem with MCP (Anthropic just admited it in this article is token consuptions – it was the main reason to shift paradigm from MCP or projects (also called Agents in Claude Code) to Skills.

Use Cases

You can use it for whatever you use AI for now. It’s better to use it for simple, separable, repetitive tasks. Think about baby skills: they learn how to hold a spoon and then they can hold it – it’s a skill. Eating dinner is a complex project that requires many skills. For the best results, use skills for small repetitive tasks, not for projects.

Here’s around 100 use cases for the reference:

Examples you can use today

Anthropic includes four Skills with every paid account:

• pptx - Creates and edits PowerPoint presentations

• xlsx - Works with Excel spreadsheets

• docx - Handles Word documents with tracked changes

• pdf - Manages PDFs (extracting text, merging, splitting)

The community has built hundreds more. Here are some favorites:

From Anthropic’s official collection:

• canvas-design - Creates visual designs in PNG and PDF

• brand-guidelines - Applies Anthropic’s brand colors and typography (you can adapt this for your company)

• theme-factory - Generates consistent themes for documents and presentations

For more check out the Anthropic Skills repository or Awesome Skills collection on GitHub.

Limits to know

• Maximum 8 Skills per conversation

• Each Skill package can’t exceed 8MB

• Skill names: 64 characters maximum

• Skill descriptions: 1,024 characters maximum

Best Practices from official sources

Security: Please Read This Part

Skills run code in a secure sandbox, but you should still be careful:

Only install Skills from sources you trust. Just like you wouldn’t install random software on your computer, don’t install random Skills.

What could go wrong with malicious Skills:

• Tricking Claude into doing things you didn’t intend

• Leaking your private information

• Misusing Claude’s capabilities

Safety practices:

• If a Skill includes scripts (Python or JavaScript files), have someone review them before installing

• Never put passwords or API keys directly in Skills

• When installing community Skills, check the creator’s reputation

• Review what the Skill does before uploading it

Think of Skills like browser extensions: incredibly useful, but install thoughtfully.

Let’s build something wonderful!

Let’s build a Meeting Notes Summary Skill together. This will take about 30 minutes and give you something immediately useful.

In the next sections, I’ll break down how to create your very first skill, what didn’t work out, share download link for my working Meeting Notes Analyzer and 8 usefull resources. There’s a special offer – a 60% discount and the LinkedIn Optimization Playbook for free. The methodology has already helped 3,000 professionals increase recruiter visibility up to 8x and land their dream jobs, from new grads to CEOs.

Feel free to unsubscribe anytime, no hard feelings!

Short preparation – I promise.

Before touching your computer, answer these questions:

01 What this skill will do?

Transform messy meeting notes into structured summaries with:

- Key decisions made
- Action items (with owners)
- Important discussion points
- Find inconsistencies and open questions
- Follow-up needed

For future: create JIRA tickets

02 What’s the input?

Raw meeting notes – could be bullet points, could be a transcript, could be scattered thoughts. It’s better to find last 5 meeting notes to give LLM an example how it could look like.

03 What makes a good [your output acceptance criteria]?

This defines your quality standards.

- A clean, scannable summary formatted consistently every time
- Decisions are clearly stated
- Action items show who’s responsible
- Discussion points capture the key themes, not every detail
- Inconsistency, missing items and open questions are highlighted
- Under 2 pages no matter how long the meeting

04 When should Agent use this?

When someone says “summarize these meeting notes” or “turn these notes into a summary” or shares meeting notes and asks for help organizing them.

Creating skill with Claude

Step 1: Turn on skill-creator

1. Open Claude.ai in your browser

2. Click the settings icon (bottom left corner)

3. Click “Capabilities”

4. Find “skill-creator” in the list

5. Toggle it ON (the switch turns blue)

Step 2: Talk to Claude

Say what you want and answer questions. I suggest use Opus 4.1 for creating.

Step 3: Get your skill (1 minute)

1. Claude will generate your complete skill and give you a ZIP file to download.

2. Click the download link and save the file somewhere you’ll remember (like your Desktop or Downloads folder).

3. Go back to Settings > Capabilities in Claude

4. Scroll down to the “Skills” section

5. Click “Upload skill”

6. Find and select the ZIP file Claude gave you

7. Wait for the upload to finish

Important: Toggle your new skill ON (make sure the switch is blue)

Step 4: Test

Here are downloadable skill files and testing log.

Claude v0.1 - failed to wake the skill when I asked: “analyze meeting notes:”, had to add “using skill”

Step 5: Iterate

→ With Claude: go to the original chat with the skill creation conversation and list suggestions, get a new file

→ Manually: open SKILL.md and fix it

Creating and using Skill in Cursor

I created the same Meeting Notes Analyzer Skill using the same prompt and sent the link to Claude Code Skills Documentation.

The similarity: Skills don’t auto-activate reliably yet. In both Claude and Cursor, I had to explicitly mention “using skill” to wake it up.

The difference: Cursor created a much better structure and instructions and as a result better outcome. You can compare it in Notion log or here.

To fix calling problem I updated the skill description to be more trigger-friendly.

before

---
name: meeting-notes-analyzer
description: Transforms raw meeting notes (bullet points, transcripts, scattered thoughts) into structured, scannable summaries with clear sections for decisions, action items with owners and due dates, discussion points, inconsistencies, and follow-ups. Use when someone shares meeting notes asking for help organizing them, says “summarize these meeting notes”, “analyze this meeting”, “extract action items”, or “turn these notes into a summary”.
---

after

---
name: meeting-notes-analyzer
description: Transforms raw meeting notes (bullet points, transcripts, scattered thoughts) into structured, scannable summaries with clear sections for decisions, action items with owners and due dates, discussion points, inconsistencies, and follow-ups. Use when someone shares meeting notes asking for help organizing them, says “summarize these notes”, “analyze this meeting”, “extract action items”, or “turn these notes into a summary” or phrases similar to it
---

It didn’t work out – I think it’s still early stage, so you’d better explicitly mention that the skill should be used.

To fix Claude’s output quality I took skill.md from Cursor and replaced the Claude file and then repacked it as a skill for Claude. The result is closer to Cursor; still, Cursor feels better overall.

Key Takeaways

It’s still early stage, so it doesn’t work perfectly and you need to ask it to use the skill every time.

Skills are simpler than they sound. You’re just writing instructions in a text file and putting it in a folder. If you can write documentation, you can create a Skill.

Start small. Don’t try to automate your entire workflow at once. Pick one repetitive task that annoys you. Make a Skill for that. Then build from there.

You don’t need to be technical. The easiest path is letting Claude build the Skill for you using skill-creator. Just describe what you want.

Skills save time on repetitive work. Anything you do the same way repeatedly—that’s a candidate for a Skill.

Quality comes from good instructions. The better you explain your process in SKILL.md, the better the results. Include examples. Be specific about format and tone.

Security matters. Only install Skills from sources you trust. Review any scripts before uploading.

Useful Resources

Agent Skills Engineering Post — Technical deep dive into how Skills extend agent capabilities with modular, composable folders

Claude Code Skills Documentation — Complete guide to creating and managing Skills in Claude Code

Skills Support Article — How to teach Claude your preferred work approaches and methodologies

Skills Blog Announcement — Official introduction to Agent Skills across Claude apps and Developer Platform

Official Skills Repository — Anthropic’s collection of ready-to-use Skills

Skills Cookbooks — Code examples and recipes for building Skills

Awesome Claude Skills — Community-curated collection of Skills

Simon Willison on Claude Skills — Independent analysis comparing Skills to MCP, arguing Skills may be more impactful

How’s it going?

Last week I tested Claude Skills - more handy and transferable than projects. Projects are still more manageable for complex work. The interesting part: these skills work as portable prompts you can adapt for other models. Also discovered a benchmark showing AI agents fail 97.5% of real freelance work. Reality check deliv…

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Following up Lenny’s LinkedIn poll on how people use Claude Code for non-coding tasks. I think it was brilliant conversation and we can learn so much from that thread. Check the original post here.

Teresa Torres (bestselling author) wrote this:

I now write all of my content with Claude Code in VS Code. We iterate on an outline, it helps me improve the hook, it conducts research for me and adds citations to my outline, and it reviews and gives feedback on each section as I write.

Teresa doesn’t code. She writes books. Using a “coding tool” for everything.

So I scraped 159 comments from professionals using Claude Code. Categorized every single one. Let’s take a look which story data tells us:

What’s Claude Code and how to get started

Claude Code is Anthropic’s AI coding assistant that lives in your terminal or VS Code. But here’s the twist - it’s not just for coding. It reads, writes, and organizes any files on your computer. Think of it as having a smart assistant who can see your entire file system and help you work with it.

Getting started takes 3 minutes:

1. Open your terminal and install Claude.

2. Open it anywhere

• In your regular terminal: Just type claude

• In Cursor: Open the terminal tab (inside Cursor) and type claude

• In VS Code: Same thing - open terminal, type claude

3. Log in (first time only)

4. Talk to it

Just describe what you want:

• “what does this code do?”

• “fix this bug”

• “add a login button”

Done! Claude edits your files right there.

Everyone is talking about context engineering, I believe it’s the easiest way to start use it as your tool.

Who’s using it

🥇 Product Managers: 38 people (24%)

🥈 Founders/Co-founders: 30 people (19%)

🥉 Engineers for non-coding tasks: 24 people (15%)

What they’re building

1. Content Creation, 31 mentions

2. Knowledge Management (aka Second Brain), 28

3. Product Management, 24

4. File Organization, 18

5. Research & Analysis, 17

6. Data Processing, 15

7. Personal Productivity, 14

8. Business Operations, 12

9. Workflow Automation, 11

10. Documentation, 10

Let me know your thoughts below and I’ll feature best comments in the next issue on this topic.

Subscribe now

Now let’s dive into exactly how people use each category - with real examples you can implement today.

5 most popular use cases

01 Content Creation & Marketing (31 Users, Highest Engagement)

The Problem

Writing takes forever. Research is scattered. Quality is inconsistent.

Teresa Torres isn’t alone. 38 people shared similar workflows.

12 agents working sequentially from newsletter research to publish-ready article with gated PDF/Notion template...Repurposing all content (published/research) for Twitter, Substack notes, Medium, Reddit through specific agents

Ayush Poddar

Framework based on comments.

Stage 1: Capture → Everything becomes markdown

Stage 2: Process → Specialized agents extract value

Stage 3: Create → Multi-iteration refinement

Stage 4: Multiply → One source → many formats

Stage 5: Compound → Everything feeds back → Next content uses accumulated knowledge

Edgar’s Quality Gate System

• Draft from docs → Rate 1-10 → If <9, loop back with fixes

• Bans overused AI words (”seamless”, “leverage”)

• Publishes directly to Notion with proper formatting

File structure example:

content-system/
├── 00-inbox/              # Raw captures
├── 01-research/           # Sources, transcripts, notes
├── 02-ideas/              # Brainstorms, outlines
├── 03-drafts/             # Work in progress
├── 04-published/          # Final pieces
├── 05-templates/          # Proven frameworks
├── agents/                # Agent instructions
└── analytics/             # Performance data

Your agent team

Core Agents

1. Librarian Agent: Organizes files, creates cross-links

2. Research Agent: Web search, competitor analysis, pattern extraction

3. Writer Agent: Knows your voice, past content, frameworks

4. Editor Agent: Enforces quality rules, scores output

Specialized Agents (task-specific)

• Hook Optimizer: Tests 5-10 opening variations

• SEO Agent: Keyword research, optimization

• Visual Agent: Quote cards, diagrams, thumbnails

• Distribution Agent: Formats and posts to platforms

• Analytics Agent: Tracks performance, identifies patterns

Results

• 4-6 hours → 45 minutes per piece

• Teresa Torres: Iterates on outlines, gets research with citations, receives real-time feedback on each section

• Ayush Poddar: 12-agent pipeline from research to multi-platform distribution

• Edgar: Quality gates ensure consistent 9/10+ output before publishing

But the most valuable source from the discussion is a GitHub repo containing a fully functional LinkedIn content creation machine that you can copy:

https://github.com/mslavov/linkedin

In the next sections, I’ll break down the remaining 4 popular use cases. There’s a special offer – a 60% discount and the LinkedIn Optimization Playbook for free. The methodology has already helped 3,000 professionals increase recruiter visibility up to 8x and land their dream jobs, from new grads to CEOs.

Feel free to unsubscribe anytime, no hard feelings!

02 Your Second Brain: Knowledge Management (28 Users)

The Problem

Information scattered across tools. Can’t find what you need when you need it. No connections between related ideas.

“I built my own ‘Knowledge Management System’ inside of it. The biggest unlock is creating CLAUDE.md and slash commands that allow your knowledge to be ‘distilled and compounded’ back into the system itself.”

Matt Stockton, Delivering Practical Data (5 likes, 6 replies)

Framework based on comments.

Stage 1: Structure → Everything has a place

Most popular storage solutions: Local markdown files (fastest, most control), Notion (team collaboration), Obsidian (advanced linking), or hybrid approach (markdown locally, sync to Notion for sharing).

Stage 2: Capture→ Friction-free input

• Voice → Wispr Flow → Markdown

• Meetings → Granola → Auto-filed

• Ideas → Quick capture → Daily note

• Research → Web clips → Topic folders

• Conversations → Transcripts → Searchable

Stage 3: Connect → Automatic linking

• Automatic backlinking

• Theme clustering

• People → Projects mapping

• Timeline connections

• Contradiction detection

Stage 4: Synthesize → Insights emerge

• Reviews all week’s notes

• Identifies emerging themes

• Surfaces forgotten insights

• Suggests connections

• Creates summary documents

• Gap analysis (intended vs actual)

Stage 5: Evolve → System improves itself

• Workflow optimization suggestions

• Template extraction from successes

• Automatic reorganization

File structure example:

second-brain/
├── areas/              # Life areas
│   ├── work/          # Projects, meetings
│   ├── learning/      # Courses, books
│   └── personal/      # Ideas, goals
├── resources/         # Reference materials
│   ├── templates/     # Reusable formats
│   ├── people/        # Contact notes
│   └── tools/         # How-tos, configs
├── archives/          # Completed/old
├── daily/             # Journal entries
└── agents/
    ├── librarian.md   # Organizes, tags
    ├── connector.md   # Finds links
    └── coach.md       # Challenges thinking

Your agent team

Core Agents:

1. Librarian Agent: Organizes, tags, maintains structure

2. Connector Agent: Finds relationships, creates links

3. Synthesis Agent: Weekly reviews, pattern finding

4. Coach Agent: Challenges assumptions, prompts reflection

Specialized Agents:

• Research Agent: Gathers context, finds sources

• Template Agent: Extracts patterns, creates reusables

• Archive Agent: Moves old content, maintains relevance

• Daily Agent: Creates journal entries, tracks habits

Results

• Matt Stockton: Knowledge “distilled and compounded”

• Fernanda: Entire company OS in markdown

• Jason Cyr: “2,000+ notes organized in 10 minutes”

• Tyler: Complete life tracking system

• Gang Rui: Self-improving feedback loop

03 Product Management Workflows (18 Users)

The Problem

PRDs get outdated instantly. User feedback lives in 20 different tools. Roadmap decisions lack data backing.

“Markdown files are my all-encompassing data store. PRD, PRFAQ, requirements, QA testing, user feedback, roadmap decisions. Everything lives in markdown. Then I create subagents in Claude Code specialized for different PM functions. PRD Agent knows the full requirements doc. User Research Agent has all customer interviews and feedback. QA Agent owns testing strategy. All in seconds, not days of digging through docs!”

Gavin McNamara, Founder @ Why Not Us Labs (43 likes)

Framework based on comments.

Stage 1: Capture → Every input becomes structured data

• All running initiatives and core features documented

• Team info including dev strengths, knowledge matrices

• Daily Granola meeting notes auto-sorted and tagged

• Cross-functional todos tracked and chased

Daily update workflow:

• Morning: Pull calendar, review priorities

• During meetings: Granola captures everything

• Post-meeting: Auto-sort into feature folders

• Evening: 3-round consistency check

Stage 2: Synthesize → Patterns emerge from chaos

• Read all week’s meeting notes

• Extract feature requests and pain points

• Identify contradictions in requirements

• Surface hidden dependencies

• Generate insight reports

Stage 3: Decide → Data-driven prioritization

Derek DeHart’s Evidence System:

• Evidence scoring from customer calls

• Cross-reference with analytics (via MCP)

• Weight by strategic alignment

• Output: Prioritized backlog with reasoning

Stage 4: Document → Living specs that update themselves

• Latest customer quotes

• Current implementation status

• Test results and edge cases

• Acceptance criteria validation

Stage 5: Communicate → Stakeholder updates on autopilot

File structure example:

product-brain/
├── initiatives/          # Active projects
│   ├── feature-x/       # PRDs, specs, research
│   └── experiment-y/    # Hypotheses, results
├── meetings/            # Granola captures
│   ├── 2024-01/        # By date
│   └── _processed/     # Sorted by feature
├── evidence/           # Customer insights
│   ├── calls/          # Transcripts
│   ├── feedback/       # Support tickets
│   └── analytics/      # Data pulls
├── team/               # People context
│   ├── strengths.md    # Who knows what
│   └── capacity.md     # Current load
└── agents/
    ├── prd-agent.md    # Spec maintenance
    ├── research.md     # Pattern finding
    └── standup.md      # Daily updates

Your agent team

Core Agents:

1. PRD Agent: Maintains living requirements docs, ensures consistency

2. Research Agent: Analyzes customer feedback, identifies patterns

3. QA Agent: Owns testing strategy, validates acceptance criteria

4. Standup Agent: Generates daily updates, tracks cross-functional todos

Specialized Agents:

• Evidence Scorer: Prioritizes features based on customer data

• Dependency Mapper: Flags hidden dependencies across initiatives

• Consistency Checker: Runs 3-round validation on requirements

• Stakeholder Agent: Generates tailored updates for different audiences

Results

• Gavin: “All in seconds, not days of digging”

• Inna: 3-round consistency check catches contradictions

• JD: Kanban dashboard with dozen integrations

• Megan: PRDs write themselves from conversations

• Manav: “It pushes back on vague criteria”

04 File Organization & Data Management (22 Users)

The Problem

Digital hoarding. Downloads folder chaos. Can’t find anything. Duplicates everywhere.

“You can give Claude Code access to a local folder and it will iteratively automate the sorting of the files in that folder. Not just by date or name... but by the content of the files.”

Justin Parnell (9 likes)

Framework based on comments.

Stage 1: Audit → Know what you have

Initial Scan:

• File type distribution

• Size analysis

• Duplicate detection

• Naming pattern identification

• Date range mapping

Stage 2: Categorize → Smart filing rules

• Screenshot_2024* → Projects/Screenshots/[Date]/

• Invoice_* → Financial/[Year]/[Vendor]/

• Download(*).pdf → _Inbox/ToReview/

Stage 3: Clean → Intelligent deduplication

• Keep newest in primary location

• Archive older versions with timestamps

• Delete true duplicates

• Log everything for recovery

Stage 4: Organize → Everything finds its home

• Active vs Archive separation

• Date-based hierarchies

• Project-based clustering

• Content-aware placement

Stage 5: Maintain → Ongoing sanity

• Process Downloads folder

• Archive completed projects

• Update file index

• Clean desktop

• Compress old logs

File structure example:

life-organized/
├── Active/              # Current work
│   ├── Projects/       # In progress
│   └── Daily/          # Today’s files
├── Archive/            # Completed
│   ├── 2024/          # By year
│   └── _Old/          # Legacy items
├── Admin/              # Life stuff
│   ├── Financial/      # Invoices, receipts
│   ├── Contracts/      # Agreements
│   └── Taxes/         # By year
├── Resources/          # References
└── _Inbox/            # To be processed

Your agent team

Core Agents:

1. Auditor Agent: Scans folders, identifies patterns, flags duplicates

2. Organizer Agent: Applies filing rules, creates folder structure

3. Cleaner Agent: Handles deduplication, archiving, compression

4. Maintenance Agent: Daily processing of inbox, desktop cleanup

Specialized Agents:

• Financial Agent: Sorts invoices, receipts by vendor and date

• Screenshot Agent: Organizes by project and timestamp

• Archive Agent: Moves completed projects, maintains structure

• Index Agent: Maintains searchable file catalog

Results

• Martin: Tax invoices organized perfectly

• Justin: “Cognitive load” eliminated

• Preet: Gmail receipts → organized expenses

• Taimoor: Large CSVs split, PDFs merged

• Everyone: “Game changer for reducing mental clutter”

05 Research & Analysis Deep Dives (17 Users)

The Problem

Information overload. Can’t connect dots across sources. Insights buried in noise.

“I had it pull in interview transcripts, survey responses, and web research, then draft a PRD, ICP, product roadmap, and feature prioritization, all backed by actual evidence. It connects dots across sources, flags contradictions, and builds coherent artifacts you can actually use.”

Michael Ulin, 3x AI Founder (2 likes)

Framework based on comments.

Stage 1: Collect → Gather all sources

• Customer call transcripts (Fireflies)

• Support tickets (Linear)

• Product docs (Notion)

• Survey responses

• Competitor analysis

Stage 2: Extract → Surface key information

• Pattern recognition across transcripts

• Quote mining with attribution

• Statistical extraction

• Contradiction flagging

Stage 3: Synthesize → Connect the dots

• Theme clusters

• Evidence chains

• Gap analysis

• Insight documents

• Research questions

Stage 4: Analyze → Deep pattern recognition

• Cross-source validation

• Pattern identification

• Outlier detection

• Trend analysis

• Hypothesis testing

Stage 5: Deliver → Actionable outputs

• Executive summaries

• Product strategies

• Research briefs

• Competitive reports

• Feature prioritization

File structure example:

research-ops/
├── sources/            # Raw inputs
│   ├── interviews/     # Transcripts
│   ├── surveys/        # Responses
│   ├── competitors/    # Intel gathering
│   └── literature/     # Papers, articles
├── analysis/          # Processing
│   ├── themes/        # Clustered insights
│   ├── patterns/      # Cross-source
│   ├── evidence/      # Supporting data
│   └── contradictions/# Conflicts
├── outputs/           # Deliverables
│   ├── strategies/    # Product docs
│   ├── briefs/        # Research summaries
│   └── reports/       # Deep dives
└── agents/
    ├── collector.md   # Source gathering
    ├── extractor.md   # Information mining
    └── synthesizer.md # Pattern finding

Your agent team

Core Agents:

1. Collector Agent: Gathers sources from multiple platforms

2. Extractor Agent: Mines insights, quotes, and data points

3. Synthesizer Agent: Connects themes across sources

4. Analyst Agent: Validates patterns, tests hypotheses

Specialized Agents:

• Contradiction Detector: Flags conflicting information

• Evidence Chain Builder: Links insights to supporting data

• Competitive Intelligence Agent: Tracks competitor moves

• Hypothesis Tester: Validates assumptions against data

Results

• Michael Ulin: “Catches patterns I would’ve missed”

• Derek DeHart: “Hub for ongoing product research”

• Sumant: Automated competitive intelligence

• Gang Rui: “Information compounds into competitive advantages”

• 80% faster than manual analysis

Best practices across the board

1. Everything becomes markdown - The universal format

2. Agents specialize by function - Not by tool

3. Knowledge compounds - Today’s output feeds tomorrow’s context

4. Automation handles repetition - Humans handle creativity

5. Local-first wins - Your data, your control

Quick Start Guide

Pick your use case and start small:

Drowning in content creation? Start with a simple content-system folder. Create one agent that helps you outline. Add research capabilities next week. Build your content engine one piece at a time.

Drowning in meetings? Start with Product Management workflow. Let Granola capture one meeting. Have Claude sort the notes. Add consistency checking when you’re ready.

Can’t find anything? Start with File Organization. Pick one messy folder (Downloads, Screenshots, Desktop). Let Claude audit it first. Watch it organize by content, not just filename.

Research takes forever? Start with the Analysis system. Dump your last 5 customer calls in a folder. Ask Claude to find patterns. Be amazed at what you missed.

The key: Start with today’s urgent problem, create your first simple building block. Let it prove value. Then expand systematically. Your system grows with you.

Comment with your experience or your thoughts if you want to be featured in the next analysis - your insights make everyone better.

Before you go…

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With gratitude for your curiosity,

Alena

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