AI Coding Skill That May Make Your Coding Sessions +44% More Productive
Vlad Mysla
SubscribeSign in
AI Coding Skill That May Make Your Coding Sessions +44% More Productive
Vlad Mysla<br>May 26, 2026
Share
This is a quick story about how I developed an AI coding skill for myself and realized it unintentionally competes with startups that do something very different, raise millions, and make money by saving time and cutting costs for software developers.<br>I am a practicing AI product builder. After working for three months across ten different projects, I discovered that this AI coding skill also does the following:
I do a lot of rapid prototyping, proof-of-concept exploration, and MVP validation with real customers. That means my clients and I sometimes reimagine and redesign the same idea multiple times a day. We pivot often, so reusing previously developed code does not always make sense. From an engineering perspective, it can be easier to start from scratch. But from a product perspective, that means similar requirements have to be given to AI over and over again. PRD documents help and are always a good start, but as always, the devil is in the details. During a coding session, more nuances bubble up, and product requirements change, evolve, and drift from the original document. The problem with starting over from the original PRD is that valuable information gets lost. It is painful. I had to type or dictate everything from memory (btw, check out LocalFlow, my free, private, open-source alternative to paid voice dictation tools). That was the user pain that started all of this.<br>Product Traceability Skill
This skill started as an AI coding skill that keeps the product requirements document up to date across coding sessions. It worked like a charm and let me switch between implementations quickly. The next thing I added was a change log, mostly so I could stop inventing clever Git commit messages every time. Fast forward: at some point, I realized that even within the same codebase, I still had to repeat myself, steer-correct, and hard-override things that had already been discussed but were lost across separate sessions. That is how the Product Traceability skill started tracking pivotal decisions. Finally, Claude Code itself suggested adding a traceability matrix to make it easier to trace how requirements map to code, tests, and decisions. Together with a few Git hooks, producing and maintaining these four files is essentially the Product Traceability skill:
The skill is open source: https://github.com/vmysla/agent-skill-product-traceability<br>MIT licensed. One-command install. After installation, Claude Code gets a standing rule: “After changing code, config, or structure, update the trace files.” That means every future session can start by reading the repo’s own memory instead of reverse-engineering the project from scratch.<br>Without realizing it, I had given my AI coding assistant agentic memory. Once I saw that, I wanted to know whether it had a material impact on my day-to-day workflow.<br>The Impact
To find evidence, I ran a retroactive analysis using historical Claude Code session logs. Luckily, I had ten projects of different complexity: AI agent skills, websites, complex web apps, and mobile applications.<br>Disclaimer : the numbers below come from a pre/post analysis and would require a larger A/B comparison for statistical significance. Please treat every data point as directional unless stated otherwise.
The first discovery was that passing these four files to AI, along with the context skill, improves caching. Specifically, +35 percentage points in estimated prompt-cache hit rate, improving it from 5% to 40% .<br>According to my analysis, this translates into 30-45% savings on input-token costs . The agent reads four compact files near the start of the session. Those files are high-signal and relatively stable, which makes them friendly to prompt caching. Instead of paying full price to rediscover project context every turn, more of that context can be reused.
Cache saves both time and money. It adds up quickly. For a small startup with two agile teams, or roughly ten developers, using Claude Opus, this can mean $10K/year that could be invested into marketing or a proper team-building event.<br>Another powerful productivity component is that AI agents typically have to rediscover your codebase at the beginning of each session. Without traceability, a fresh session in a real codebase begins with a familiar ritual: ls, find, and grep for entry points. In a complex repo, the AI often gets it wrong on the first pass and re-greps with a different query. In practice, this discovery work takes 1 to 5 minutes . Without enough context, it can be triggered often and become a taxing recurring fee that wastes hands-on engineering time. When the AI guesses the architecture from a grep result, it is often wrong on the first pass and has to backtrack. When those mistakes are missed, you get so-called AI...