I've been using it for three weeks and really like it - it's already made some great additions to our app's skills and improved my coding agents' workflow!
Nikhil here, CTO @ BuildBetter, YC founder (W19). We’re building ZeroShot, an agent session monitoring tool that builds and shares self-improving skills with your team so you can work with AI agents the way your most productive engineers do.
Download the Mac Desktop App at tryzeroshot.com/download or run curl -fsSL tryzeroshot.com | sh to give it a spin!
What ZeroShot does: it captures your coding-agent sessions locally (Claude, Codex, Cursor, Pi, OMP, more on request), then runs a classification pipeline on them in the background, looking for signals that the agent could improve. When it finds a big enough cluster of signals related to the same frictions, we submit them to your local agent and get it to generate a skill. It's different from something that you can bootstrap off of your own agent sessions locally in the following ways:
1. Cross-session skill drafting: agents aren't good at telling you when they encounter the same friction again in a separate session, even when pointing an agent at session logs retroactively.
2. Team-level enrichment (paid tier): We look across your team's sessions/GitHub PR comments and contribution history, and weight skills toward the people who are experts in a given codebase or area. Our current development focus is on surfacing the strategies that your most productive teammates use to work with AI; early days for agentic engineering in general. If you have suggestions on how to track that productivity, I'm all ears.
3. Your agentic documentation becomes self-maintaining: we're trying to offload the cognitive burden of managing harness engineering so that you can focus on delivering business value.
The problem we kept hitting: Between September and November of last year, I found myself getting more upset with the vibe-slop that made its way into main from engineers who knew better (and some who didn’t). So we started harness engineering. At first we just wanted to make sure that the models wouldn't make the same dumb mistakes that we kept on repeating in PR comments and to our own agents. We realized that specific engineers were able to “hold” the models better and were more productive because of it. ZeroShot is born from that internal experimentation that led us to being 5x more productive as a team.
We were inspired to build the app by our own internal self-improving-skill system, which itself was inspired by systems that the Codex team has espoused on X.
Privacy: the app runs entirely on your machine for free. Your agent's session logs only leave the computer when you opt in to a paid team plan and otherwise stay entirely local.
A non-exhaustive list of limitations:
1. Our classifiers work best when there's a repeated friction pattern across sessions. If you're doing highly varied work, we may not be able to pick up on patterns that are worth incorporating into the harness.
2. The numbers on our website are from teams with heavy repeated work. I don't want to oversell the outcomes you'll get.
3. Works with Claude Code, Codex, Cursor, Pi, and OMP today, we're adding more on request.
4. Mac only for now, Windows in the works, Linux CLI.
Pricing: local app is free, paid plans are for team features.
Happy to answer any questions or hear any feedback!
Posting on behalf of Nikhil here, CTO @ BuildBetter, YC founder (W19). We're building ZeroShot, an agent session monitoring tool that builds and shares self-improving skills with your team so you can work with AI agents the way your most productive engineers do.
Download the Mac Desktop App at tryzeroshot.com/download or run curl -fsSL tryzeroshot.com | sh to give it a spin!
What ZeroShot does: it captures your coding-agent sessions locally (Claude, Codex, Cursor, Pi, OMP, more on request), then runs a classification pipeline over them in the background looking for friction such as retry loops, course corrections, and missing context. When the same friction shows up across multiple sessions or people, it clusters those and drafts a reusable skill. What's unique?
- Cross-session skill drafting. Agents can't see that they've walked the same failure trajectory five times across five sessions. Even agent-driven retrospectives miss what we catch because the same underlying friction looks different in each transcript, so anything analyzing raw transcripts comes up empty. Instead, we cluster at the pattern level.
- Team-level enrichment (paid tier). We look across your team's sessions, GitHub PR comments and contribution history, and weight skills toward the people who are experts in a given codebase or area. We're also starting to surface why some teammates get better mileage from their LLM use and share that across the team. This part is still in progress and I'd love feedback on which productivity metrics are most useful.
- The system maintains itself so you don't have to. Hand-written .md and rules files decay quickly. ZeroShot validates and regenerates skills as your sessions evolve, so the maintenance burden sits with us, not your team.
Knowledge sharing across the org. Skills live in ZeroShot-managed skillsets shared across the team, and the same accumulated skills follow you whether agents run locally or remote.
The problem we kept hitting: Between September and November of last year, I found myself getting more upset with the vibe-slop that made its way into main from engineers who knew better (and some who didn't). So we started harness engineering. At first we just wanted to make sure that the models wouldn't make the same dumb mistakes that we kept on repeating in PR comments and to our own agents. We realized that specific engineers were able to "hold" the models better and were more productive because of it. ZeroShot is born from that internal experimentation that led us to being 5x more productive as a team.
We were inspired to build the app by our own internal self-improving-skill system, which itself was inspired by systems that the Codex team has espoused on X.
Privacy: the app runs entirely on your machine for free. Session data doesn't leave your repo unless you explicitly opt into the team features. We use your local agents to parse through clusters.
Limitations:
- Skill extraction works best when there's repeated friction across multiple user sessions. If you're a solo dev doing highly varied work, the clustering has less signal to work with.
- The efficiency numbers on our site are from teams with heavy repeat work; your mileage will genuinely vary and I don't want to oversell it.
- Works with Claude Code, Codex, Cursor, Pi (including OMP) today, and we're adding support for more environments weekly. It's early days and we're constantly working on our pipelines; please give us feedback as you start using it!
Mac only for now, Windows in the works, Linux CLI.
Pricing: the local app is free, no account needed. Paid plans are for team features (shared skills, observability, cost attribution).
Happy to answer anything, especially skeptical questions about the token-savings claims. I'd rather explain the methodology than have you dismiss them outright. We're actively looking for design partners, so please hmu if you want a demo or run into any issues!
I've been using it for three weeks and really like it - it's already made some great additions to our app's skills and improved my coding agents' workflow!
thanks @kplawver! As you know it's early days and we're roughing things out but really excited that you're getting value from it :)
Nikhil here, CTO @ BuildBetter, YC founder (W19). We’re building ZeroShot, an agent session monitoring tool that builds and shares self-improving skills with your team so you can work with AI agents the way your most productive engineers do.
Download the Mac Desktop App at tryzeroshot.com/download or run curl -fsSL tryzeroshot.com | sh to give it a spin!
What ZeroShot does: it captures your coding-agent sessions locally (Claude, Codex, Cursor, Pi, OMP, more on request), then runs a classification pipeline on them in the background, looking for signals that the agent could improve. When it finds a big enough cluster of signals related to the same frictions, we submit them to your local agent and get it to generate a skill. It's different from something that you can bootstrap off of your own agent sessions locally in the following ways:
1. Cross-session skill drafting: agents aren't good at telling you when they encounter the same friction again in a separate session, even when pointing an agent at session logs retroactively.
2. Team-level enrichment (paid tier): We look across your team's sessions/GitHub PR comments and contribution history, and weight skills toward the people who are experts in a given codebase or area. Our current development focus is on surfacing the strategies that your most productive teammates use to work with AI; early days for agentic engineering in general. If you have suggestions on how to track that productivity, I'm all ears. 3. Your agentic documentation becomes self-maintaining: we're trying to offload the cognitive burden of managing harness engineering so that you can focus on delivering business value.
The problem we kept hitting: Between September and November of last year, I found myself getting more upset with the vibe-slop that made its way into main from engineers who knew better (and some who didn’t). So we started harness engineering. At first we just wanted to make sure that the models wouldn't make the same dumb mistakes that we kept on repeating in PR comments and to our own agents. We realized that specific engineers were able to “hold” the models better and were more productive because of it. ZeroShot is born from that internal experimentation that led us to being 5x more productive as a team.
We were inspired to build the app by our own internal self-improving-skill system, which itself was inspired by systems that the Codex team has espoused on X.
Privacy: the app runs entirely on your machine for free. Your agent's session logs only leave the computer when you opt in to a paid team plan and otherwise stay entirely local.
A non-exhaustive list of limitations:
1. Our classifiers work best when there's a repeated friction pattern across sessions. If you're doing highly varied work, we may not be able to pick up on patterns that are worth incorporating into the harness.
2. The numbers on our website are from teams with heavy repeated work. I don't want to oversell the outcomes you'll get.
3. Works with Claude Code, Codex, Cursor, Pi, and OMP today, we're adding more on request.
4. Mac only for now, Windows in the works, Linux CLI.
Pricing: local app is free, paid plans are for team features.
Happy to answer any questions or hear any feedback!
Posting on behalf of Nikhil here, CTO @ BuildBetter, YC founder (W19). We're building ZeroShot, an agent session monitoring tool that builds and shares self-improving skills with your team so you can work with AI agents the way your most productive engineers do.
Download the Mac Desktop App at tryzeroshot.com/download or run curl -fsSL tryzeroshot.com | sh to give it a spin!
What ZeroShot does: it captures your coding-agent sessions locally (Claude, Codex, Cursor, Pi, OMP, more on request), then runs a classification pipeline over them in the background looking for friction such as retry loops, course corrections, and missing context. When the same friction shows up across multiple sessions or people, it clusters those and drafts a reusable skill. What's unique?
- Cross-session skill drafting. Agents can't see that they've walked the same failure trajectory five times across five sessions. Even agent-driven retrospectives miss what we catch because the same underlying friction looks different in each transcript, so anything analyzing raw transcripts comes up empty. Instead, we cluster at the pattern level.
- Team-level enrichment (paid tier). We look across your team's sessions, GitHub PR comments and contribution history, and weight skills toward the people who are experts in a given codebase or area. We're also starting to surface why some teammates get better mileage from their LLM use and share that across the team. This part is still in progress and I'd love feedback on which productivity metrics are most useful.
- The system maintains itself so you don't have to. Hand-written .md and rules files decay quickly. ZeroShot validates and regenerates skills as your sessions evolve, so the maintenance burden sits with us, not your team.
Knowledge sharing across the org. Skills live in ZeroShot-managed skillsets shared across the team, and the same accumulated skills follow you whether agents run locally or remote.
The problem we kept hitting: Between September and November of last year, I found myself getting more upset with the vibe-slop that made its way into main from engineers who knew better (and some who didn't). So we started harness engineering. At first we just wanted to make sure that the models wouldn't make the same dumb mistakes that we kept on repeating in PR comments and to our own agents. We realized that specific engineers were able to "hold" the models better and were more productive because of it. ZeroShot is born from that internal experimentation that led us to being 5x more productive as a team.
We were inspired to build the app by our own internal self-improving-skill system, which itself was inspired by systems that the Codex team has espoused on X.
Privacy: the app runs entirely on your machine for free. Session data doesn't leave your repo unless you explicitly opt into the team features. We use your local agents to parse through clusters.
Limitations:
- Skill extraction works best when there's repeated friction across multiple user sessions. If you're a solo dev doing highly varied work, the clustering has less signal to work with.
- The efficiency numbers on our site are from teams with heavy repeat work; your mileage will genuinely vary and I don't want to oversell it.
- Works with Claude Code, Codex, Cursor, Pi (including OMP) today, and we're adding support for more environments weekly. It's early days and we're constantly working on our pipelines; please give us feedback as you start using it!
Mac only for now, Windows in the works, Linux CLI.
Pricing: the local app is free, no account needed. Paid plans are for team features (shared skills, observability, cost attribution).
Happy to answer anything, especially skeptical questions about the token-savings claims. I'd rather explain the methodology than have you dismiss them outright. We're actively looking for design partners, so please hmu if you want a demo or run into any issues!