The key idea: SkillOpt does not make the deployed agent larger. It trains a reusable external skill artifact offline, then ships only the improved procedure. That is a very different adaptation layer than fine-tuning, prompting, or adding more inference-time calls.
PROMPT ENGINEERING
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Ontology-Constrained Memory as Reasoning Boundary for Agents
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Ontology-constrained memory is a great way to frame it. The key insight that clicked for me is that the schema is not just a filter on what gets stored, it is also a reasoning boundary. What the agent cannot represent, it cannot hallucinate connections about.
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Optimization Stack in AI: From Weights to Skill Files
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YES, Optimization keeps moving up the stack. Weights, then prompts/harness, now skill files.
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Prompt Engineering with Omni for Video Generation
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One year later with Omni and this test can pass. I saw it getting pretty close, so I tweaked the prompt:
— fofr (@fofrAI) 26 mai 2026
> A video of a man counting to 10 on his fingers, show the number in the corner. A new number every 1s, no dialogue other than the numbers he says. He uses two hands for… https://t.co/TPd8HEl97W pic.twitter.com/09M1eYw0qVOne year later with Omni and this test can pass. I saw it getting pretty close, so I tweaked the prompt: > A video of a man counting to 10 on his fingers, show the number in the corner. A new number every 1s, no dialogue other than the numbers he says. He uses two hands for
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AI turns past prompts into reusable skills and subagents
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It will look through your past sessions and turn repeated prompts into reusable skills + subagents
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Troubleshooting AI behavior by removing persistent context
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I do use structured prompts, plan/goal mode, Notion context, and a few local reference docs. Memory is involved. Could be instruction/memory interference. I’ll try a clean run without persistent context and see if it changes the behavior. thanks!
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GPT instruction following worsened, hallucinations and errors compared to 5.5
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both id say. Instruction following has worsened. GPT repeatedly hallucinates during the task, then admits mistakes, tries to correct them, and makes further serious errors. I haven't encountered such serious errors with version 5.5.
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Turn Claude into Specialist Marketing & Business Agents
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If you find this useful, also check this out. Turn Claude into 20+ different specialists for marketing & business. Install real expertise, not just prompts. Get my Claude skills bundle
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Automated 30-day review of recent AI work using memory and git
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The text version of the prompt: "Look back over my recent work from the last 30 days using all available context. Use available evidence in this order:
– Session Memory summaries and MEMORY. md entries
– Git log and recent commit history across branches
– CLAUDE. md and -
Directing AI Agents: The Future Skill Beyond Prompting
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One catch though. The bright future goes to people who can direct an agent and check its work. Direction on its own just builds a faster mess.