Goodai-Base Update: 3 New AI Agent Skills Live

At Fountane, we build products fast. That pressure exposed a real problem with AI coding agents. They'd confidently write code for a codebase they barely understood. No warnings, no caveats — just wrong decisions that looked right until they broke something. So I built a fix: a skill you drop into Cursor, Claude Code, or any AI tool that reads markdown. Before your agent writes a single line, it scores itself: — How well does it understand your codebase? — What can it build autonomously right now? — What gaps exist, and what closes them? The real unlock wasn't better prompts. It was knowing the agent's confidence level before giving it work. A 60% understanding score means you're going to spend more time reviewing than building. A 90% score means you can actually delegate. We now run this before any major feature work. It's changed how we structure context, how we onboard agents to new repos, and how we catch blind spots early. Open source. Tool-agnostic. One command to install. If you enjoy thoughtful conversations with people building real products, this could be for you. Apply for an invite → https://lnkd.in/gZdbqS4J Link : https://lnkd.in/dB5Cb9Wp #ProductEngineering #AgenticAI #BuildingInPublic

1 month researching how to make engineers visible in the AI era. 5 days building SteeringLog. SteeringLog captures human judgment in AI-assisted coding. It runs silently in the background, logging moments when engineers steer, override, or refine AI output - turning them into clear, human-readable artifacts. It addresses gaps that are becoming clear as AI-assisted engineering becomes standard: - Engineers can't reflect on their own patterns. - Teams can't assess how their people actually work with AI. - The skill of steering AI effectively goes unmeasured and unrecognized. As a first step, I built a Claude Code plugin. It's free and open source: https://lnkd.in/eNsFiqf6 Next steps: - Refine what gets captured and how it's structured. - Support more AI coding tools. - Build a platform that analyzes steering logs, combines them with meaningful metrics, and generates reports for performance reviews, interviews, and team retrospectives. #IAmHuman #SteeringLog #OpenJudgement #OpenSource #AIEngineering #ClaudeCode

Built a small agent to stop burning tokens in Claude Code (and Cursor, Copilot, Codex). It's called lean-dev — one command sets up smart context management, auto-generates .claudeignore, tightens your CLAUDE.md, and switches models by task automatically. npx lean-dev init That's it. No config, no setup friction. Still early days — if you try it and find bugs, open a GitHub issue. PRs and ideas are very welcome too. https://lnkd.in/gQZwqVuz #ClaudeCode #AI #DeveloperTools #OpenSource

🚀 **The Future of AI Development is Here** We just published a deep dive into Claude Code's MCP development workflow — and it's a game-changer for how developers build software. Imagine coding where your AI assistant: ✅ Automatically lints and formats on every change ✅ Remembers your entire project architecture across sessions ✅ Controls browsers, databases, and GitHub directly from the terminal ✅ Executes specialized tasks via slash commands like `/tdd` and `/security-review` This isn't science fiction. It's the **Model Context Protocol (MCP)** in action. Our latest blog breaks down the four core layers: 🔹 **Hooks** — Event-driven automation 🔹 **Skills** — Reusable prompt templates 🔹 **MCP Servers** — Live integrations with external tools 🔹 **CLAUDE.md** — Persistent project memory The result? A full-stack coding environment that reduces context-switching by 98% and turns your terminal into an autonomous development powerhouse. Whether you're building AI agents, scaling production systems, or just tired of repetitive dev tasks — this workflow will transform how you code. 👉 Read the full guide: https://lnkd.in/gekY83-i #Anthropic #Claude #ClaudeCode #AIDevelopment #AI #MachineLearning #DeveloperTools #MCP #Automation #SoftwareEngineering #DevOps #Anablock #CodingWorkflow #TechInnovation #AIAgents

Deep Agents: Why Planning, Files, Todos, Sub‑Agents & Prompts Matter Building truly capable AI agents isn’t about a single clever prompt—it’s about architecture. Projects like Deep Agents from LangChain highlight five core building blocks that take agents from demos to production‑ready systems: 🧠 Planning Agents need explicit planning to break down complex goals, reason step‑by‑step, and adapt when things change—just like humans do. 📁 Files Persistent file access enables agents to store context, artifacts, logs, and intermediate outputs—critical for long‑running or multi‑step workflows. ✅ Todos Task tracking gives agents memory of what’s done and what’s next, improving reliability, resumability, and transparency. 🤖 Sub‑Agents Delegation is power. Specialized sub‑agents allow parallelism, separation of concerns, and cleaner reasoning—each agent focuses on what it does best. 📝 Prompts (as first‑class citizens) Well‑designed, reusable prompts define agent roles, boundaries, and decision‑making patterns—turning instructions into systems. Together, these components enable deep reasoning, autonomy, and scalability—exactly what’s needed to move from “chatbots” to real AI teammates. 🔗 Explore the project: https://lnkd.in/e_xFiyD6 If you’re building agentic systems, this repo is a must‑study. #AI #AgenticAI #LLM #LangChain #DeepAgents #SoftwareArchitecture #GenerativeAI

🚀 We just open-sourced Daena-Coder — because the community deserves a coder that doesn't get cut from the plan. When Claude Code removed its coder from the Pro plan, thousands of developers were left without a powerful AI coding partner. We built Daena-Coder to fill that gap — and gave it to the community for free. What makes Daena-Coder different: 🧠 Council of LLMs — Claude 4.7, GPT-5.5, and Gemini vote on every major code decision. No single model blind spot. 🏠 Runs fully local — Ollama, vLLM, llama.cpp via MCP. Your code never leaves your machine if you want it that way. ⚡ Hybrid mode — local speed for routine tasks, cloud power for complex reasoning. Automatic routing. 🔒 Governed by default — every action has an audit trail. You can see exactly what the AI did and why. Built on the same governed multi-agent architecture as Daena OS — but laser-focused on software development. If you've been looking for an AI coding partner that works WITH your stack, not around it — this is it. ⭐ Star us on GitHub and join the community building the future of AI-assisted development. 🔗 https://lnkd.in/efQ6-5zv #AI #OpenSource #DaenaCoder #LLM #Ollama #ClaudeCode #CodingAI #MASai #DeveloperTools #ArtificialIntelligence

Lately I almost never just sit down and solve something myself. First instinct: tool it. Spin up the AI, wire a progressive loop against a quality target, then let it run. Results get better every week to the point where they frequently surpass what I could only accomplish after at least three full drafts. That's been working. But a new tradeoff has crept in, and I'm confident I'm not alone. Sometimes I'm faster. The AI takes a few passes, orbits the problem, gets there eventually, and I already knew the answer, but it stuck the landing soundly. So now there's this constant background calculation running in my head: > Is this worth the tokens, or should I "just do it?" < The shift isn't "can I automate this?" because I have and will continue to do this. AI tooling routinely elevates my work product and allows others to contribute similarly across the team. Big equalizer! I've been building toward this for a while — this repo is where that thinking lives: https://lnkd.in/gfhgSGQ6. Frankly, though, the more interesting stuff is what happens when you layer real workflows on top of it. That's what we're working on at CallBox, and it's where the actual gains are showing up. We have some great internal adoption by Product and BizDev folks as well as Software Engineers. How are others thinking about this tradeoff? #AIFirst #AIAssistants #WorkflowAutomation #EngineeringLeadership #TechLeadership #DeveloperExperience #Productivity #FutureOfWork #SoftwareEngineering #BuildInPublic

Synthesising 700+ AI agents across 11 platforms into 10 specialised ultra-agents — here's what the architecture looks like. After deep-scanning 7,663 files across Claude Code, Cursor, Codex, GitHub Copilot, Google Antigravity, and OpenClaw, I built Kazi's Agents Army v2. Here are the key technical decisions: → ZEUS uses a Phase 0–6 project lifecycle with LOKI autonomous mode (RARV: Reason → Act → Reflect → Verify). Blind review uses 3 parallel reviewers + Devil's Advocate on unanimous verdicts to break sycophantic AI loops. Trust scores are penalty-based per agent. → ATLAS implements LSP semantic code intelligence (25k+ symbol indexing), enforces event sourcing/CQRS, and applies Kaizen with the Rule of Three before any abstraction is introduced. → SENTINEL uses Ed25519 cryptographic identity per agent, penalty-based trust scoring, delegation chain verification, and post-quantum readiness mapping. Zero-trust by default for all agentic systems. → NEXUS is mapped to the 2026 framework landscape: LangGraph 1.0 GA, CrewAI v1.10.1, Claude Agent SDK v0.1.48, Google ADK v1.26 — covering supervisor, swarm, and hierarchical multi-agent patterns with context compression and CDD workflows. → FORGE defines concrete scaling triggers: CPU >70%, Memory >85%, API p95 latency >200ms — before auto-scaling fires. Includes 5-pattern load testing and platform engineering. Total: 10 agents, 6 platform configs, 6,202 lines, deployable with a single copy command across Claude Code, Codex, Cursor, Copilot, Antigravity, and OpenClaw. 🔗 https://lnkd.in/dFcnAsBc #AIAgents #MultiAgent #LLM #AgentArchitecture #ClaudeCode #LangGraph #SoftwareEngineering #MachineLearning #AIEngineering

🫠 Built an autonomous AI system that analyzes GitHub repositories, detects bugs, and suggests fixes using LLMs (Qwen2.5-Coder). Designed a full ML pipeline with fine-tuning, training workflows, and a vector database for efficient code retrieval—reducing manual code review effort. https://lnkd.in/dV4xzRMB

If your team is still merging PRs without AI in the loop, you're paying for hours you don't need to spend. Manual reviews. Bugs caught in production instead of pre-merge. Test cases written by hand at 11pm before a release. Every one of those is solvable in 2026. Here's the AI-enhanced GitHub pipeline I now recommend to every team 👇 1️⃣ Code pushed → GitHub 2️⃣ CI/CD triggered → GitHub Actions 3️⃣ AI reviews the PR → GitHub Copilot / CodeRabbit 4️⃣ AI suggests improvements → Claude / OpenAI 5️⃣ Tests auto-generated → Playwright / Testgen 6️⃣ Deploy if approved → Docker + GitHub Actions + Render/AWS AI at every step. Quality at every commit. What teams are seeing after the switch: ✅ 50% faster deployments ✅ 70% fewer bugs in production ✅ 60% less manual effort ✅ Cleaner, more maintainable code The takeaway: you don't need a bigger team. You need a smarter pipeline. Let AI handle reviews, fixes, and tests — and let your engineers focus on what only they can build. Build smarter. Ship faster. That's the whole game now. 🔔 Follow Umesh Kalia for more on AI, GitHub, and modern dev workflows. Which step would you automate first? Drop it in the comments 👇 #AI #GitHub #DevOps #Automation #SoftwareEngineering #CICD #DeveloperProductivity #GitHubActions #AITools