# Agent Skills Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/agent-skills
> Markdown URL: https://aitinkerers.org/technologies/agent-skills.md
> Technology record last updated: 2026-07-27T08:11:06Z
> Generated: 2026-09-22T20:43:50Z

Agent Skills are an open standard for packaging modular, reusable AI agent capabilities as self-contained directories, enabling agents to perform specialized, context-aware tasks on demand.

Agent Skills define modular, reusable AI agent capabilities. Developed by Anthropic and published as an open standard in December 2025, a skill is essentially a folder containing a `SKILL.md` file with metadata and instructions. These skills equip AI agents, like Claude Code or Google Gemini CLI, with domain-specific expertise and repeatable workflows. Instead of relying on bloated prompts, agents dynamically load skill instructions and resources (scripts, templates, reference materials) only when a task demands them, optimizing performance and consistency across various AI platforms. This approach transforms general-purpose agents into specialists, allowing for the capture and sharing of procedural knowledge across teams without requiring code changes.

- Official technology site: https://www.agentskills.org
- Public AI Tinkerers demos and talks: 4
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Agents Building Agents: Reflective Optimization Loops](https://toronto.aitinkerers.org/talks/rsvp_4gS7qartFFk)

I'll show how an AI agent can build and optimize another AI agent, using reflective optimization to find issues, optimize evals, iterate on architecture, find the optimal prompt/model, and more. We've built a system with multiple levels of reflective optimization for agent development. - GEPA: reflective prompt optimization - Synthetic eval generation: going from a 1-off bug to an proper eval you can use in reflective optimization - Expanding reflective optimization beyond prompts: model selection, tool use, subagents -- reflective optimization can drive all levels of agent optimization.

- Event context: AI Tinkerers Toronto - April 2026 - hosted by Shopify — 2026-04-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_4gS7qartFFk

### [Building a Daily AI Sales Agent in Claude Code — 38x Cold Outreach Response Rate](https://seattle.aitinkerers.org/talks/rsvp_05m3TX8PeEs)

A daily AI sales agent built entirely in Claude Code that sources, scores, and delivers qualified leads to prospects via LinkedIn — replacing templated cold outreach with curated artifacts. Live demo: I'll build a value play from scratch on stage — Exa Websets discovers 25 recently funded startups, Claude scores them against an ICP definition, filters to the top 10, creates a Google spreadsheet, and delivers the formatted sheet as a LinkedIn first-touch.

- Event context: AI Tinkerers Seattle: GTM Engineering — April Meetup — 2026-04-23 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_05m3TX8PeEs

### [From ServiceNow Ticket to Terraform Pull Request using Skills, MCP with Coding Agents](https://nashville.aitinkerers.org/talks/rsvp_3hn_1rgR6k0)

Most coding agents are really good at generating infrastructure code from scratch. But how to steer them to follow existing Terraform conventions in the form of base modules: encoding security policies, tagging standards for cost allocation, naming conventions, and compliance guardrails into reusable building blocks. These modules are how we enforce consistency across environments, track cloud spend back to business units, and pass regulatory audits. In this talk, I'll show you how to use skills to define a better infrastructure provisioning workflow. One that steers coding agents to first discover your existing conventions, understand the risks, implement changes within those guardrails, and provide full visibility into every decision made along the way. This gives cloud operations teams the ability to orchestrate infrastructure requests with human-in-the-loop validation at every stage, see huge productivity gains The system uses coding agents designed to work directly on a filesystem—reading and modifying code, running bash commands and interacting with real repositories. Built using Agent Skills and the Claude Agents SDK, this talk walks through how the workflow is structured, what works today. I'll also cover how this approach is agnostic to agent runtimes and model providers - built on open standards (MCP + Skills), so you're not locked into any single vendor and how the same pattern extends to other tasks across the enterprise beyond infrastructure provisioning.

- Event context: AI Tinkerers Nashville – Software Development in the Age of AI — 2026-03-25 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_3hn_1rgR6k0

### [AI-powered Inbound Lead Qualification](https://seattle.aitinkerers.org/talks/rsvp_QxPNK282fH4)

Vercel had 10 people doing inbound lead qualification. They automated it — and moved 9 out of 10 to outbound. Savings: $900K/year. The process they followed:  1. Shadow your best SDR for a few weeks  2. Document every decision they make  3. Ask "why" for each scoring choice  4. Turn it into an AI workflow I built an open-source skill for Claude Code that does this: /design-scoring — builds a scoring model for your business. Asks about your ICP, analyzes closed deals from your CRM, researches your website and competitors.  /qualify-lead — takes any inbound request (email, form, whatever format), finds the person on LinkedIn, scrapes the company website, applies your scoring model, and outputs: Hot / Warm / Cold / Disqualified + a draft response.

- Event context: AI Tinkerers Seattle: GTM Engineering Kickoff — Building AI for Growth — 2026-02-26 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_QxPNK282fH4

## Related Technologies

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- [Anthropic API](https://aitinkerers.org/technologies/anthropic-api) ([Markdown](https://aitinkerers.org/technologies/anthropic-api.md)) — 66 public demos
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- [Claude Agents SDK](https://aitinkerers.org/technologies/claude-agents-sdk) ([Markdown](https://aitinkerers.org/technologies/claude-agents-sdk.md)) — 4 public demos
- [Exa Websets](https://aitinkerers.org/technologies/exa-websets) ([Markdown](https://aitinkerers.org/technologies/exa-websets.md)) — 1 public demo
- [FastAPI](https://aitinkerers.org/technologies/fastapi) ([Markdown](https://aitinkerers.org/technologies/fastapi.md)) — 181 public demos
- [Firecrawl](https://aitinkerers.org/technologies/firecrawl) ([Markdown](https://aitinkerers.org/technologies/firecrawl.md)) — 3 public demos
- [GEPA](https://aitinkerers.org/technologies/gepa) ([Markdown](https://aitinkerers.org/technologies/gepa.md)) — 3 public demos
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- [Opus](https://aitinkerers.org/technologies/opus) ([Markdown](https://aitinkerers.org/technologies/opus.md)) — 7 public demos
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