[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-079d6f57e5fb787a-claude-managed-agents-replace-n8n-for-ai-automatio-summary":3,"summaries-facets-categories":80,"summary-related-079d6f57e5fb787a-claude-managed-agents-replace-n8n-for-ai-automatio-summary":3650},{"id":4,"title":5,"ai":6,"body":13,"categories":55,"created_at":56,"date_modified":56,"description":57,"extension":58,"faq":56,"featured":59,"kicker_label":56,"meta":60,"navigation":61,"path":62,"published_at":63,"question":56,"scraped_at":64,"seo":65,"sitemap":66,"source_id":67,"source_name":68,"source_type":69,"source_url":70,"stem":71,"tags":72,"thumbnail_url":56,"tldr":77,"tweet":56,"unknown_tags":78,"__hash__":79},"summaries\u002Fsummaries\u002F079d6f57e5fb787a-claude-managed-agents-replace-n8n-for-ai-automatio-summary.md","Claude Managed Agents Replace n8n for AI Automations",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8654,1278,13052,0.00234715,{"type":14,"value":15,"toc":48},"minimark",[16,21,25,28,32,35,38,42,45],[17,18,20],"h2",{"id":19},"prompt-driven-agent-building-handles-real-workflows","Prompt-Driven Agent Building Handles Real Workflows",[22,23,24],"p",{},"Describe your automation goal in natural language, like \"Parse sales call transcripts into ClickUp tasks,\" and Claude generates the agent spec, including schema for inputs (e.g., transcript) and outputs (e.g., structured tasks). Anthropic hosts the agent on their backend with a reusable endpoint, limiting networking for safety. Add credentials via secure vaults—no manual API keys: connect ClickUp directly in-browser, acknowledge sharing, and test end-to-end. In a demo, pasting a standup transcript (\"Alice sets up staging by Friday, Bob reviews API doc\") extracted 5 parallel tasks like \"Review API design doc\" into ClickUp's \"example builds\u002Fcrm\" list, visible immediately with creation timestamps.",[22,26,27],{},"Refine iteratively: after testing, prompt changes like \"Add default ClickUp space 'example builds\u002Fcrm' and assignee mappings,\" updating the system prompt automatically. This conversational setup builds production-ready agents faster than n8n's node wiring, as humans struggle with text-based flows but excel at verbal specs.",[17,29,31],{"id":30},"debug-and-observability-beat-black-box-no-code","Debug and Observability Beat Black-Box No-Code",[22,33,34],{},"Every run logs full transcripts, debug views (code-like process states, API events), and timelines showing agent thinking (e.g., 27k output tokens), model starts\u002Fstops, idle times, and cache hits. Filter logs by agent messages or thinking segments; visual timelines cluster events (e.g., message → thinking → API call). Environments detail permissions (e.g., limited to mcp.clickup.com), token usage (2.3M input\u002F20k output in testing), wall-clock time, and costs ($2.40 for Sonnet 4o, some Opus). Access logs track all requests across workspaces.",[22,36,37],{},"Manage via dashboard: list\u002Farchive agents (not environments—delete separately to save resources), view sessions (conversations\u002Fruns), and vaults for shared credentials. Analytics aggregate usage (e.g., $24 last month on Opus), rate limits, and model breakdowns, enabling cost optimization before pricing scales.",[17,39,41],{"id":40},"frontend-integration-deploys-apps-in-seconds","Frontend Integration Deploys Apps in Seconds",[22,43,44],{},"After agent creation, prompt Claude for integration code: \"Build a frontend chat passing to this agent.\" It generates Netlify-ready prompts for tools like Antigravity, deploying a chat UI in 30 seconds (fast mode). Test transcript (\"Write 1-pager on pricing tiers\") triggers tasks in ClickUp without local env setup—Anthropic handles credentials\u002Fserver. Push to production for team sharing, creating apps like action-item generators linked to transcripts\u002Fproposals.",[22,46,47],{},"Trade-off: Locked to Sonnet 4o (no fast mode), text-based (no visual nodes yet), but unprecedented ease—full stack (backend agent + frontend) without infra. Anthropic will add visual editors, making it superior to n8n\u002FMake\u002FZapier, as pictures reveal flows faster than 1,000 words of prompts.",{"title":49,"searchDepth":50,"depth":50,"links":51},"",2,[52,53,54],{"id":19,"depth":50,"text":20},{"id":30,"depth":50,"text":31},{"id":40,"depth":50,"text":41},[],null,"💼 Work with my AI consulting team: https:\u002F\u002Fdub.sh\u002Fwork-with-me-pkg\n📚 Watch my NEW 2026 Claude Code course: https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QoQBzR1NIqI\n🎙️ Listen to my silly podcast: www.youtube.com\u002F@stackedpod\n\n📚 Free multi-hour courses\n→ Claude Code (4hr full course): https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QoQBzR1NIqI\n→ Vibe Coding w\u002F Antigravity (6hr full course): https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gcuR_-rzlDw\n→ Agentic Workflows (6hr full course): https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MxyRjL7NG18\n→ N8N (6hr full course, 890K+ views): https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2GZ2SNXWK-c\n\nSummary ⤵️\nThings are moving pretty quickly now that Anthropic has access to Mythos. They just dropped Claude Managed Agents, which is meant to replace no-code builders for automation.\n\nMy software, tools, & deals (some give me kickbacks—thank you!)\n🚀 Instantly: https:\u002F\u002Flink.nicksaraev.com\u002Finstantly-short\n📧 Anymailfinder: https:\u002F\u002Flink.nicksaraev.com\u002Famf-short\n🤖 Apify: https:\u002F\u002Fconsole.apify.com\u002Fsign-up (30% off with code 30NICKSARAEV)\n🧑🏽‍💻 n8n: https:\u002F\u002Fn8n.partnerlinks.io\u002Fh372ujv8cw80\n📈 Rize: https:\u002F\u002Flink.nicksaraev.com\u002Frize-short (25% off with promo code NICK)\n\nFollow me on other platforms 😈\n📸 Instagram: https:\u002F\u002Fwww.instagram.com\u002Fnick_saraev\n🕊️ Twitter\u002FX: https:\u002F\u002Ftwitter.com\u002Fnicksaraev\n🤙 Blog: https:\u002F\u002Fnicksaraev.com\n\nWhy watch?\nIf this is your first view—hi, I’m Nick! TLDR: I spent six years building automated businesses with Make.com (most notably 1SecondCopy, a content company that hit 7 figures). Today a lot of people talk about automation, but I’ve noticed that very few have practical, real world success making money with it. So this channel is me chiming in and showing you what *real* systems that make *real* revenue look like.\n\nHopefully I can help you improve your business, and in doing so, the rest of your life 🙏\n\nLike, subscribe, and leave me a comment if you have a specific request! Thanks.\n\nChapters","md",false,{},true,"\u002Fsummaries\u002F079d6f57e5fb787a-claude-managed-agents-replace-n8n-for-ai-automatio-summary","2026-04-08 18:42:51","2026-04-10 03:07:48",{"title":5,"description":57},{"loc":62},"079d6f57e5fb787a","Nick Saraev","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Ob5Vu-gD3mo","summaries\u002F079d6f57e5fb787a-claude-managed-agents-replace-n8n-for-ai-automatio-summary",[73,74,75,76],"agents","automation","ai-tools","llm","Prompt Claude to build hosted agents that parse transcripts into ClickUp tasks—no API keys needed, full debugging, deploys in minutes, outpacing no-code 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This shifts Claude from single-response assistant to autonomous executor that handles errors, makes decisions, and delivers complete outcomes.",[22,3669,3670],{},"In Claude Code, a terminal-based agent for development, skills include built-in functions (file system access, bash execution, code interpretation, web browsing), tool integrations, MCP (Model Context Protocol) for structured external communication, and sub-agent delegation. Claude selects skills dynamically—for instance, debugging involves reading logs, searching code, running tests, web lookups, editing files, and re-testing—coordinating outputs sequentially without predefined scripts.",[22,3672,3673],{},"Shared brand context injects persistent details like tone guidelines, business priorities, and task state into every skill call, ensuring coherence. Memory types include in-context (current window), external (retrieved from databases\u002Fvector stores), and episodic (past session summaries), preventing redundant work across runs.",[17,3675,3677],{"id":3676},"chaining-patterns-for-robust-workflows","Chaining Patterns for Robust Workflows",[22,3679,3680],{},"Skill chaining passes one skill's output directly as input to the next, enabling workflows like querying CRM for uncontacted leads, drafting personalized emails, and sending them—all in one goal-based instruction. Conditional branching lets Claude evaluate mid-flow decisions, such as skipping emails for recent replies or retrying failed tests, using reasoning instead of hard-coded rules.",[22,3682,3683],{},"Loops handle iteration over lists, like summarizing all quarterly contracts or pulling competitor pricing per product, without explicit loop definitions. Error handling is adaptive: Claude reasons on failures (e.g., API errors), choosing retries, alternatives, skips, or human escalation, making workflows more resilient than rigid automations.",[17,3685,3687],{"id":3686},"multi-agent-orchestration-and-business-impact","Multi-Agent Orchestration and Business Impact",[22,3689,3690],{},"Claude acts as a kernel-like orchestrator, breaking goals into sub-tasks, delegating to specialized agents (e.g., vision models for images, code models for execution), synthesizing results, and parallelizing for speed. It can also serve as a sub-agent via MCP in larger systems.",[22,3692,3693],{},"Real workflows include: content pipelines (research keyword, outline, draft with brand voice, format for CMS—half-day task to 10 minutes); support triage (classify tickets, check CRM history, draft\u002Froute responses); competitive intel (scrape sites, compare pricing to prior data via memory, report via Slack). SoftProdigy plugin (@softprodigy-ai\u002Fagent npm package) adds 120+ pre-built skills (e.g., HubSpot updates, social images) with built-in auth, retries, and rate limiting, plus no-code builder for workflows—reducing setup overhead.",[22,3695,3696],{},"This architecture scales complexity without single-agent bottlenecks, specializing roles and enabling production AI automation for 2025 business operations.",{"title":49,"searchDepth":50,"depth":50,"links":3698},[3699,3700,3701],{"id":3663,"depth":50,"text":3664},{"id":3676,"depth":50,"text":3677},{"id":3686,"depth":50,"text":3687},[89],{"content_references":3704,"triage":3719},[3705,3710,3714,3717],{"type":3706,"title":3707,"author":3708,"context":3709},"tool","Claude Code","Anthropic","mentioned",{"type":3706,"title":3711,"publisher":3712,"context":3713},"SoftProdigy Agent Skills Plugin","SoftProdigy","recommended",{"type":3715,"title":3716,"author":3708,"context":3709},"other","Model Context Protocol (MCP)",{"type":3715,"title":3718,"author":3708,"context":3709},"Anthropic’s documentation on building agents",{"relevance":3720,"novelty":3721,"quality":3721,"actionability":3721,"composite":3722,"reasoning":3723},5,4,4.35,"Category: AI Automation. The article provides in-depth insights into how Claude's agentic operating system can automate complex workflows, addressing the audience's need for practical applications of AI in product development. It discusses specific features like skill chaining and error handling, which are directly applicable for builders looking to implement AI-driven automation.","\u002Fsummaries\u002Fbe6c94bf724c728d-claude-s-agentic-os-chains-skills-into-full-workfl-summary","2026-05-05 16:01:01","2026-05-06 16:13:48",{"title":3653,"description":49},{"loc":3724},"be6c94bf724c728d","Towards AI","article","https:\u002F\u002Fpub.towardsai.net\u002Fwhat-is-claudes-agentic-operating-system-48ec4834e2cc?source=rss----98111c9905da---4","summaries\u002Fbe6c94bf724c728d-claude-s-agentic-os-chains-skills-into-full-workfl-summary",[73,76,74,75],"Claude becomes an agentic operating system by combining tool use, multi-step planning, and persistent context to orchestrate skills like file access, APIs, and sub-agents, automating business processes end-to-end without manual intervention.",[],"-6qSkV-0e7SAbCVlphXnEqpvWk8l1oAHauCGmW-EzZA",{"id":3739,"title":3740,"ai":3741,"body":3746,"categories":3774,"created_at":56,"date_modified":56,"description":49,"extension":58,"faq":56,"featured":59,"kicker_label":56,"meta":3775,"navigation":61,"path":3790,"published_at":3791,"question":56,"scraped_at":3792,"seo":3793,"sitemap":3794,"source_id":3795,"source_name":3796,"source_type":3731,"source_url":3797,"stem":3798,"tags":3799,"thumbnail_url":56,"tldr":3800,"tweet":56,"unknown_tags":3801,"__hash__":3802},"summaries\u002Fsummaries\u002F97db07c06b7d3fc4-7-signs-to-switch-browser-ai-to-desktop-agents-summary.md","7 Signs to Switch Browser AI to Desktop Agents",{"provider":7,"model":8,"input_tokens":3742,"output_tokens":3743,"processing_time_ms":3744,"cost_usd":3745},7562,1400,21503,0.00218975,{"type":14,"value":3747,"toc":3769},[3748,3752,3755,3759,3762,3766],[17,3749,3751],{"id":3750},"multi-file-analysis-and-persistent-updates-beat-browser-limits","Multi-File Analysis and Persistent Updates Beat Browser Limits",[22,3753,3754],{},"Browser AI caps at 3-10 files per chat (fewer for large files), risking errors on 10-20 files like invoices or client meeting notes. Desktop agents like Claude Cowork or CodeX process entire folders, extracting insights (e.g., rename expenses, populate Excel trackers) without limits. For weekly dashboard\u002FExcel updates, avoid degrading intelligence in long browser threads—use a dedicated folder where fresh chats access persistent artifacts, ensuring high performance as new data integrates seamlessly.",[17,3756,3758],{"id":3757},"sub-agents-self-improvement-and-long-running-tasks-unlock-depth","Sub-Agents, Self-Improvement, and Long-Running Tasks Unlock Depth",[22,3760,3761],{},"Demand holistic research? Browser AI sequences steps linearly; desktop spawns sub-agents for parallel dives (e.g., separate AIs per competitor, synthesizing holistic reports). Build self-improving agents by having them write\u002Fupdate lessons-learned files or rules in-folder—feedback like \"avoid this error\" persists across fresh chats, turning tools into compounding assets. Complex jobs (30s-5min typical; 30min+ possible) run uninterrupted on desktop, skipping browser's repeated \"continue\" prompts (e.g., Claude Opus).",[17,3763,3765],{"id":3764},"custom-connectors-and-scheduling-enable-autonomy","Custom Connectors and Scheduling Enable Autonomy",[22,3767,3768],{},"No pre-built connector for your system? Desktop AI builds it: describe target\u002Faction, provide API key (fetch via Atlas browser if needed), and it codes read\u002Fwrite access—no coding required. Schedule recurring tasks (e.g., Mondays 9am, hourly) far more reliably than browser options (ChatGPT limited; Claude browser lacks). Three universal signs hit most: recurring file updates, self-improving rules, scheduled runs. Always \"yes and\"—browser for sessions, desktop for systems preserving state across time.",{"title":49,"searchDepth":50,"depth":50,"links":3770},[3771,3772,3773],{"id":3750,"depth":50,"text":3751},{"id":3757,"depth":50,"text":3758},{"id":3764,"depth":50,"text":3765},[89],{"content_references":3776,"triage":3786},[3777,3780,3782,3784],{"type":3715,"title":3778,"url":3779,"context":3709},"Presentation (with prompts)","https:\u002F\u002Fd-squared70.github.io\u002F7-Signs-You-ve-Outgrown-ChatGPT-and-What-to-Use-Next-\u002F",{"type":3706,"title":3781,"context":3713},"Claude Cowork",{"type":3706,"title":3783,"context":3713},"CodeX",{"type":3706,"title":3785,"context":3709},"Atlas browser",{"relevance":3721,"novelty":3787,"quality":3721,"actionability":3721,"composite":3788,"reasoning":3789},3,3.8,"Category: AI Automation. The article discusses the advantages of using desktop AI agents over browser-based ones, addressing specific pain points like handling multiple files and automating tasks, which is relevant for product builders. It provides actionable insights on when to switch to desktop agents, making it practical for the audience.","\u002Fsummaries\u002F97db07c06b7d3fc4-7-signs-to-switch-browser-ai-to-desktop-agents-summary","2026-05-04 18:00:31","2026-05-05 16:05:08",{"title":3740,"description":49},{"loc":3790},"679bde90433bb55b","Dylan Davis","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=NYCMcWEk0Dg","summaries\u002F97db07c06b7d3fc4-7-signs-to-switch-browser-ai-to-desktop-agents-summary",[75,73,74,76],"Upgrade from browser ChatGPT\u002FClaude to desktop Claude Cowork\u002FCodeX when handling 10+ files, recurring file updates, self-improving tasks, or scheduled automation—keeps AI intelligence high via folder persistence without long threads.",[],"gJ8g7chT3hTBc2kVndg0MFnfr4XHkOQ1OEJIDuJ2NL4",{"id":3804,"title":3805,"ai":3806,"body":3811,"categories":4148,"created_at":56,"date_modified":56,"description":49,"extension":58,"faq":56,"featured":59,"kicker_label":56,"meta":4149,"navigation":61,"path":4162,"published_at":4163,"question":56,"scraped_at":4164,"seo":4165,"sitemap":4166,"source_id":4167,"source_name":4168,"source_type":3731,"source_url":4169,"stem":4170,"tags":4171,"thumbnail_url":56,"tldr":4172,"tweet":56,"unknown_tags":4173,"__hash__":4174},"summaries\u002Fsummaries\u002Fda5a49aff4f5aaa2-eval-driven-skills-boost-agent-performance-on-supa-summary.md","Eval-Driven Skills: Boost Agent Performance on Supabase",{"provider":7,"model":8,"input_tokens":3807,"output_tokens":3808,"processing_time_ms":3809,"cost_usd":3810},8616,2988,40179,0.00319455,{"type":14,"value":3812,"toc":4142},[3813,3817,3836,3846,3853,3862,3873,3882,3886,3889,3892,3898,3954,3957,3963,3967,3970,3973,4010,4016,4022,4025,4030,4042,4047,4058,4069,4075,4081,4085,4111,4116],[17,3814,3816],{"id":3815},"agent-skills-structure-for-progressive-disclosure","Agent Skills Structure for Progressive Disclosure",[22,3818,3819,3820,3824,3825,3827,3828,3831,3832,3835],{},"Agent skills are folders containing a required ",[3821,3822,3823],"code",{},"skill.md"," file and optional references\u002Fscripts, designed to provide targeted context without bloating the agent's initial context window. The ",[3821,3826,3823],{}," uses YAML frontmatter with ",[3821,3829,3830],{},"name"," and ",[3821,3833,3834],{},"description"," fields—these load first as an \"envelope,\" enabling progressive disclosure: the agent decides when to fetch full content based on need.",[22,3837,3838,3839,3841,3842,3845],{},"Inside ",[3821,3840,3823],{},", add instructions, workflows, or links to files in a ",[3821,3843,3844],{},"reference\u002F"," folder (Markdown for docs, scripts like Bash\u002FPython for actions). This forms a graph—reference files can link others—acting like a book's index linking chapters. Scripts run locally (tied to your OS env, e.g., Linux\u002FMac compatible), unlike remote MCP tools.",[22,3847,3848,3852],{},[3849,3850,3851],"strong",{},"Key principle",": Skills deliver custom info\u002Fworkflows too verbose for MCP tool descriptions. Example structure:",[3854,3855,3860],"pre",{"className":3856,"code":3858,"language":3859},[3857],"language-text","---\nname: Department Stats Skill\ndescription: Guides creating SQL views for dept salary averages and counts from profiles table.\n---\nTo compute department stats:\n1. Query `profiles` table.\n2. GROUP BY `department`.\n3. AVG(`salary`), COUNT(*).\n\nReference: [exact SQL template](.\u002Freference\u002Fdept-stats.sql)\n","text",[3821,3861,3858],{"__ignoreMap":49},[22,3863,3864,3865,3868,3869,3872],{},"Reference files are plain Markdown or scripts, e.g., ",[3821,3866,3867],{},"dept-stats.sql"," with ",[3821,3870,3871],{},"CREATE OR REPLACE VIEW department_stats AS SELECT department, AVG(salary), COUNT(*) FROM profiles GROUP BY department;",". This setup teaches agents precise patterns, avoiding hallucinated SQL.",[22,3874,3875,3878,3879,3881],{},[3849,3876,3877],{},"Common mistake",": Overloading ",[3821,3880,3823],{}," content—keep it concise; offload details to references. Bad pattern: Vague descriptions like \"DB tools\" lead to ignored skills. Good: Specific triggers, e.g., \"Use when querying aggregates by department.\"",[17,3883,3885],{"id":3884},"skills-vs-mcp-tools-complementary-for-integrations","Skills vs. MCP Tools: Complementary for Integrations",[22,3887,3888],{},"Skills ≠ MCP tools. MCP (Multi-Context Provider) servers expose remote, env-agnostic tools (e.g., Supabase's 20+ tools: list tables, exec SQL, apply migrations, run DB advisor). Agent calls them directly—no local setup.",[22,3890,3891],{},"Skills augment with context: Define workflows (e.g., \"Always test views post-creation\"), docs, or local scripts. Use MCP for integrations (no bash access); skills for everything else.",[22,3893,3894,3897],{},[3849,3895,3896],{},"Trade-offs",":",[3899,3900,3901,3917],"table",{},[3902,3903,3904],"thead",{},[3905,3906,3907,3911,3914],"tr",{},[3908,3909,3910],"th",{},"Aspect",[3908,3912,3913],{},"Skills",[3908,3915,3916],{},"MCP Tools",[3918,3919,3920,3932,3943],"tbody",{},[3905,3921,3922,3926,3929],{},[3923,3924,3925],"td",{},"Env",[3923,3927,3928],{},"Local (OS-specific)",[3923,3930,3931],{},"Remote\u002Fserver-side",[3905,3933,3934,3937,3940],{},[3923,3935,3936],{},"Purpose",[3923,3938,3939],{},"Context\u002Fworkflows",[3923,3941,3942],{},"Actions\u002Ftools",[3905,3944,3945,3948,3951],{},[3923,3946,3947],{},"Loading",[3923,3949,3950],{},"Progressive (frontmatter first)",[3923,3952,3953],{},"Full desc in context",[22,3955,3956],{},"In Supabase workflows, combine: MCP for DB ops, skills for schema-specific guidance. Misconception: Skills replace MCP—false; they stack for DAX (agent dev experience).",[22,3958,3959,3962],{},[3849,3960,3961],{},"Pitfall",": Scripts fail cross-OS (e.g., Windows-incompatible Bash). Solution: Prefer MCP for portability; reserve scripts for local prototyping.",[17,3964,3966],{"id":3965},"eval-driven-development-define-metrics-test-iterate","Eval-Driven Development: Define Metrics, Test, Iterate",[22,3968,3969],{},"Test skills like code: Unit (manual runs), integration (evals), E2E (full workflows). With LLMs, use evals—nondeterministic tests evaluating reasoning\u002Ftools\u002Fsteps, not exact output.",[22,3971,3972],{},"Adopt OpenAI's framework:",[3974,3975,3976,3983,3992,3998,4004],"ol",{},[3977,3978,3979,3982],"li",{},[3849,3980,3981],{},"Define metrics",": What \"good\" means, e.g., \"Correct SQL syntax (100%), Uses GROUP BY (90%), Calls apply_migration tool (80%).\" Tailor to skill: Forwarding to docs? Workflow adherence?",[3977,3984,3985,3988,3989,3991],{},[3849,3986,3987],{},"Build skill",": Write ",[3821,3990,3823],{},"\u002Frefs\u002Fscripts.",[3977,3993,3994,3997],{},[3849,3995,3996],{},"Run evals",": Input (task prompt), expected (tools\u002Fsteps\u002Foutput). Use Braintrust for observability—logs agent traces, scores metrics (pass\u002Ffail, LLM-as-judge).",[3977,3999,4000,4003],{},[3849,4001,4002],{},"Grade\u002FInspect",": Check tool calls, reasoning. Nondeterministic? Run 10-50x, avg scores.",[3977,4005,4006,4009],{},[3849,4007,4008],{},"Iterate",": Tweak (e.g., add examples), re-run.",[22,4011,4012,4015],{},[3849,4013,4014],{},"Braintrust setup",": Platform for evals; defines scenarios (input\u002Fexpected), runs agent, visualizes traces. Like Datadog for agents. CEO quote (podcast): Emphasizes full behavior picture.",[22,4017,4018,4021],{},[3849,4019,4020],{},"Manual testing baseline",": Prompt agent (e.g., Claude) on Supabase demo app: \"Create department_stats view: avg salary, count by dept.\" Without skill: Agent lists tables, crafts wrong SQL (e.g., joins wrong table), applies migration—view created but buggy (misses salary avg).",[22,4023,4024],{},"With skill: Agent references skill, uses exact template—correct view. App query shows dept breakdowns.",[22,4026,4027,3897],{},[3849,4028,4029],{},"Quality criteria",[4031,4032,4033,4036,4039],"ul",{},[3977,4034,4035],{},"Skill used? (Trace shows load).",[3977,4037,4038],{},"Performance delta: Baseline 40% success → Skill 85%.",[3977,4040,4041],{},"Holds under variants: Bad instructions drop to 20%; precise ones sustain.",[22,4043,4044,3897],{},[3849,4045,4046],{},"Failure modes",[4031,4048,4049,4052,4055],{},[3977,4050,4051],{},"Unused: Vague desc.",[3977,4053,4054],{},"Misleading: Conflicts MCP docs.",[3977,4056,4057],{},"Fragile: No examples, fails edge cases.",[22,4059,4060,4061,4064,4065,4068],{},"Demo repo (hudripppn\u002Fimprove-skills-workshop-aieurope): Next.js app (performance reviews on Supabase Postgres), MCP.json for local server, seeded DB (employees\u002Fmanagers\u002FHR). Setup: ",[3821,4062,4063],{},"npx @supabase\u002Fcreate-supabase",", ",[3821,4066,4067],{},"npm run dev",". Eval harness at end.",[22,4070,4071,4074],{},[3849,4072,4073],{},"Exercise",": Clone repo, baseline agent on reports view, add skill, run 20 evals via Braintrust—tune till 90%+.",[22,4076,4077,4080],{},[3849,4078,4079],{},"Prerequisites",": Agent familiarity (Claude\u002FCursor), Supabase basics (Postgres BaaS: DB\u002Fauth\u002Fstorage\u002Fedge funcs). Fits mid-workflow: After agent prototyping, before prod.",[17,4082,4084],{"id":4083},"key-takeaways","Key Takeaways",[4031,4086,4087,4090,4093,4096,4099,4102,4105,4108],{},[3977,4088,4089],{},"Start every skill with precise frontmatter description triggering use—vague ones get ignored.",[3977,4091,4092],{},"Combine skills (context) + MCP (tools) for Supabase: Skills guide workflows, MCP executes.",[3977,4094,4095],{},"Eval-driven: Define 3-5 metrics upfront (e.g., tool calls, SQL correctness) before writing.",[3977,4097,4098],{},"Use Braintrust for traces: Run 20+ evals\u002Fiteration; aim for 80%+ delta over baseline.",[3977,4100,4101],{},"Test bad patterns: Overload content, poor refs—quantify drops to validate fixes.",[3977,4103,4104],{},"Progressive disclosure principle: Frontmatter envelope + refs = scalable context.",[3977,4106,4107],{},"Local scripts? Prototype only—migrate to MCP for prod portability.",[3977,4109,4110],{},"Iterate cycle: Metrics → Skill → Evals → Grade → Repeat, like TDD for agents.",[22,4112,4113,3897],{},[3849,4114,4115],{},"Notable Quotes",[3974,4117,4118,4126,4129,4136,4139],{},[3977,4119,4120,4121,4125],{},"\"Progressive disclosure is basically when the agent... load",[4122,4123,4124],"span",{},"s"," the exact amounts of information that allows the agent to choose to load the rest... once it actually needs it.\" (Explaining skill.md design for context efficiency.)",[3977,4127,4128],{},"\"Skills actually just provide more context to your agent... everything that you don't have space to define on the MCP tools descriptions you can define them on skills.\" (Clarifying skills' role vs. tools.)",[3977,4130,4131,4132,4135],{},"\"You can basically do exactly the same ",[4122,4133,4134],{},"as code testing",". ... since we have an LLM in the loop, you'll have something called evaluations.\" (Mapping traditional testing to agent evals.)",[3977,4137,4138],{},"\"The core loop of the workshop is simple: write a Skill, run evals, inspect results, and iterate.\" (From description; distills the method.)",[3977,4140,4141],{},"\"If you're building anything that it's an integration, you should use MCP... skills actually just provide more context.\" (Practical usage rule.)",{"title":49,"searchDepth":50,"depth":50,"links":4143},[4144,4145,4146,4147],{"id":3815,"depth":50,"text":3816},{"id":3884,"depth":50,"text":3885},{"id":3965,"depth":50,"text":3966},{"id":4083,"depth":50,"text":4084},[],{"content_references":4150,"triage":4159},[4151,4155,4157],{"type":3715,"title":4152,"author":4153,"context":4154},"Systematically evaluate agent skills","OpenAI","cited",{"type":3706,"title":4156,"context":3713},"Braintrust",{"type":3706,"title":4158,"context":3709},"Supabase MCP server",{"relevance":3720,"novelty":3721,"quality":3721,"actionability":3720,"composite":4160,"reasoning":4161},4.55,"Category: AI & LLMs. The article provides a detailed framework for developing agent skills using eval-driven development, addressing practical applications for AI-powered product builders. It includes specific examples and a clear structure for implementing skills, making it immediately actionable.","\u002Fsummaries\u002Fda5a49aff4f5aaa2-eval-driven-skills-boost-agent-performance-on-supa-summary","2026-05-04 16:00:06","2026-05-05 16:04:36",{"title":3805,"description":49},{"loc":4162},"cfb75be1962e65c9","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GmAQKINjv1E","summaries\u002Fda5a49aff4f5aaa2-eval-driven-skills-boost-agent-performance-on-supa-summary",[73,76,75,74],"Use eval-driven development to craft agent skills: define metrics first, structure with progressive disclosure in skill.md, test via Braintrust evals on Supabase workflows, iterate to fix failure modes like unused skills or bad instructions.",[],"GEIqECEIBL3beeNMMXzBuPM6ZUDR2wAG59rCgK3E7YU",{"id":4176,"title":4177,"ai":4178,"body":4183,"categories":4230,"created_at":56,"date_modified":56,"description":49,"extension":58,"faq":56,"featured":59,"kicker_label":56,"meta":4231,"navigation":61,"path":4245,"published_at":4246,"question":56,"scraped_at":4247,"seo":4248,"sitemap":4249,"source_id":4250,"source_name":4251,"source_type":3731,"source_url":4252,"stem":4253,"tags":4254,"thumbnail_url":56,"tldr":4255,"tweet":56,"unknown_tags":4256,"__hash__":4257},"summaries\u002Fsummaries\u002F64d3d23cbbd7470e-6-projects-to-go-from-ai-user-to-builder-in-2026-summary.md","6 Projects to Go from AI User to Builder in 2026",{"provider":7,"model":8,"input_tokens":4179,"output_tokens":4180,"processing_time_ms":4181,"cost_usd":4182},6182,2085,36370,0.00225655,{"type":14,"value":4184,"toc":4225},[4185,4189,4196,4199,4202,4206,4209,4212,4216,4219,4222],[17,4186,4188],{"id":4187},"use-skills-and-rag-for-efficient-context-handling","Use Skills and RAG for Efficient Context Handling",[22,4190,4191,4192,4195],{},"Start with Skills, the highest-leverage project: create a folder with a ",[3821,4193,4194],{},"skills.md"," file containing YAML metadata (name and description fields only) followed by markdown instructions. Claude reads just the description first to check relevance via progressive disclosure—loading full instructions and referenced files only if needed—avoiding context window bloat even with 50 skills. To build one, pick a weekly task like status updates, prompt Claude Coder or Anti-Gravity to generate it from plain English instructions. This automates repetitive context explanation without engineering.",[22,4197,4198],{},"Next, implement RAG to ground LLMs in your data: split documents into chunks (a few paragraphs), embed via an embedding model into vectors where semantic similarity clusters concepts (e.g., \"hypertension\" near \"high blood pressure\" despite no shared words), store in a vector index. For queries, embed the question, retrieve top 5-10 matches, and feed to LLM for grounded generation. Unlike NotebookLM (a destination tool), RAG is a reusable component for agents or apps. Use it to make proprietary data queryable, as base models lack your specifics.",[22,4200,4201],{},"These two deliver quick wins: Skills for agent instructions, RAG for data retrieval, forming the base for production AI.",[17,4203,4205],{"id":4204},"expose-tools-via-mcp-and-wire-voice-agents","Expose Tools via MCP and Wire Voice Agents",[22,4207,4208],{},"Build an MCP (Model Context Protocol) server to universalize access: mark Python functions (e.g., your RAG retriever) with fastMCP SDK, which handles plumbing so any MCP-compatible client (Claude Desktop, Cursor, Gemini) calls it. MCP, released by Anthropic in late 2024, saw 970x SDK downloads in 18 months, was donated to Linux Foundation in Dec 2025, and is now standard across ChatGPT, Cursor, Gemini. Transform scripts into shareable infrastructure—wrap RAG in ~few lines, enabling team-wide or agent use.",[22,4210,4211],{},"Layer voice agents on top using Gemini 3.1 Flash Live API (launched March 2026): processes raw audio natively (90+ languages, barge-in interrupts, 90%+ multi-step tool calling from audio), slashing latency from 2-3s (old VAD\u002FSTT\u002FLLM\u002FTTS stack) to under 1s round trips. Speak a query, Gemini calls your MCP\u002FRAG server as a tool, responds aloud—e.g., query company docs while driving. This stacks projects 2-3 for real-time, private voice search impossible two years ago.",[17,4213,4215],{"id":4214},"run-local-models-and-fine-tune-for-control","Run Local Models and Fine-Tune for Control",[22,4217,4218],{},"Run models locally for privacy\u002Foffline\u002Fzero-cost: combine open-weights models (Gemma 4: 2B\u002F4B\u002F26B\u002F31B params; smaller on 8GB laptop RAM), 4-bit quantization (3x memory reduction, tiny quality loss), and Ollama runtime (Docker-like: one command pulls\u002Fruns, exposes API). Point Ollama Gemma at your RAG\u002FMCP for local querying, trading some speed\u002Fquality for no per-token costs.",[22,4220,4221],{},"Fine-tune only for behavior shaping (not knowledge addition): use LoRA (low-rank adaptation) to train a \u003C1% parameter adapter on a frozen base model, customizing voice\u002Fjargon (e.g., legal\u002Fmedical). Skip unless hitting walls—master first five for 90% needs; deeper than others.",[22,4223,4224],{},"Pick 1-2 scariest\u002Fclosest-to-job projects; building end-to-end proves value over prompting.",{"title":49,"searchDepth":50,"depth":50,"links":4226},[4227,4228,4229],{"id":4187,"depth":50,"text":4188},{"id":4204,"depth":50,"text":4205},{"id":4214,"depth":50,"text":4215},[],{"content_references":4232,"triage":4243},[4233,4235,4237,4239,4241],{"type":3706,"title":4234,"context":3709},"fastMCP",{"type":3706,"title":4236,"context":3709},"Ollama",{"type":3706,"title":4238,"context":3709},"Gemini Live API",{"type":3706,"title":4240,"context":3709},"NotebookLM",{"type":3706,"title":4242,"context":3709},"Claude Coder",{"relevance":3720,"novelty":3721,"quality":3721,"actionability":3720,"composite":4160,"reasoning":4244},"Category: AI & LLMs. The article provides practical projects that directly address the needs of builders looking to integrate AI into their workflows, such as implementing RAG for data retrieval and using Skills for context handling. It offers specific, actionable steps that can be immediately applied, making it highly relevant and useful for the target audience.","\u002Fsummaries\u002F64d3d23cbbd7470e-6-projects-to-go-from-ai-user-to-builder-in-2026-summary","2026-05-03 01:56:32","2026-05-03 16:45:04",{"title":4177,"description":49},{"loc":4245},"b6c581f6a107eb88","AI with Surya","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gwWlyN1Kl0g","summaries\u002F64d3d23cbbd7470e-6-projects-to-go-from-ai-user-to-builder-in-2026-summary",[76,73,75,74],"Build Skills (progressive disclosure folders), RAG (vector search over docs), MCP servers (universal tool adapter), voice agents (Gemini Live), local models (Ollama + Gemma), and fine-tuning (LoRA for behavior) to own AI workflows and stand out at work.",[],"VcWG36larbqHan0jQlZM0IBmCdntsYTK4Ix9LFI39bM"]