Yeah most agents treat skills as static
AGENTS
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Hermes Agent: Self-Improving AI with Cross-Session Memory
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The self-improving AI agent from Nous Research! Hermes Agent is a self-improving AI agent that builds skills from your work, improves them over time, and remembers across sessions. Most AI agents reset every conversation. You teach them your codebase structure, they forget. You
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Analyzing Claude’s Deep and Distinctive Personality Traits
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Lisez bien. Je pense avoir cerné la personnalité profonde et très particulière de Claude.
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Corey Ganim’s Article on Claude’s Managed Agents
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He also wrote a great article about Claude's Managed Agents here: nitter.net/coreyganim/status/2042… Corey Ganim (@coreyganim) x.com/i/article/204224928226… — https://nitter.net/coreyganim/status/2042286607449874527#m
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Corey Ganim builds first managed agent with Claude Code
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this guy is @coreyganim. Full video (including the Google Doc you hand to Claude Code to build your first managed agent) here: piped.video/watch?v=nAOyErph…
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Claude Managed Agents: Complete 12-Minute Breakdown and Deployment Guide
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This guy literally gave the clearest breakdown of Claude Managed Agents you can find.
— Charly Wargnier (@DataChaz) 12 avril 2026
In 12 minutes he covers:
→ the real definition (Platform as a Service for AI)
→ who actually needs it and who doesn't (4 personas)
→ a raw look at the live console (sessions, analytics,… pic.twitter.com/FGqZREpioIThis guy literally gave the clearest breakdown of Claude Managed Agents you can find. In 12 minutes he covers: → the real definition (Platform as a Service for AI) → who actually needs it and who doesn't (4 personas) → a raw look at the live console (sessions, analytics, costs) → the crazy math ($2.58 to fulfill a $1k service) He even dropped a free Google Doc that deploys your first agent when you give it to Claude Code 🔥 Full video in 🧵↓
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MiniMax M2.7 Open Source Model Achieves Strong Performance in Agent Workflows
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MiniMax has open sourced M2.7, their open-source model designed "for agent-based workflows, complex reasoning, and real-world engineering tasks." It introduces self-evolution capabilities, where the model improves itself through iterative experimentation, achieving 30% performance gains and a 66.6% ML competition medal rate. Honestly, this is more impactful than expected. On the performance side, M2.7 delivers strong software engineering results (56.22% SWE-Pro), near top-tier benchmarks, and excels in multi-agent collaboration, tool use, and productivity tasks like document editing. With high ELO scores, fast incident recovery (<3 min), and 97% skill compliance, it positions itself as one of the most capable open-source AI systems right now.
→ View original post on X — @kimmonismus, 2026-04-12 08:48 UTC
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Agent Skills: AI Coding Agents with Engineering Best Practices
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If you found this useful, a like or RT goes a long way 🦾 Follow me → @datachaz for insights on LLMs, AI agents, and data science! Charly Wargnier (@DataChaz) 🚨 ICYMI @addyosmani from Google just dropped his new Agent Skills and it's incredible. It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯 AI coding agents are powerful, but left alone, they take shortcuts. They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that. Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize. The full lifecycle is covered: → Define – refine ideas, write specs before a single line of code → Plan – decompose into small, verifiable tasks → Build – incremental implementation, context engineering, clean API design → Verify – TDD, browser testing with DevTools, systematic debugging → Review – code quality, security hardening, performance optimization → Ship – git workflow, CI/CD, ADRs, pre-launch checklists Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle. It works with: ✦ Claude Code ✦ Cursor ✦ Antigravity ✦ … and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow! `npx skills add addyosmani/agent-skills` Free and open-source. Repo link in 🧵↓ — https://nitter.net/DataChaz/status/2043246635996807300#m
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Agent Skills Open Source Repository by Addy Osmani
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repo link: → github.com/addyosmani/agent-… Shoutout to @addyosmani for building this and making it open-source for the community! 🤗 Don't forget to drop a ⭐️ on to help boost visibility!
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Addy Osmani’s Agent Skills: 19 Competencies to Enhance AI Coders
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🚨 ICYMI @addyosmani from Google just dropped his new Agent Skills and it's incredible. It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯 AI coding agents are powerful, but left alone, they take shortcuts. They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that. Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize. The full lifecycle is covered: → Define – refine ideas, write specs before a single line of code
→ Plan – decompose into small, verifiable tasks
→ Build – incremental implementation, context engineering, clean API design
→ Verify – TDD, browser testing with DevTools, systematic debugging
→ Review – code quality, security hardening, performance optimization
→ Ship – git workflow, CI/CD, ADRs, pre-launch checklists Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle. It works with:
✦ Claude Code
✦ Cursor
✦ Antigravity
✦ … and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow! `npx skills add addyosmani/agent-skills` Free and open-source. Repo link in 🧵↓ [Translated from EN to English]