SomethingBig loop optimizes agent skills for Opus 5

// 1h agoTUTORIAL

SomethingBig loop optimizes agent skills for Opus 5

SomethingBig shares a workflow strategy for maintaining and updating custom agent skills when transitioning to Opus 5. The method involves running an automated loop that uses Opus 5 to update skill instructions and generate a dedicated suite of test tasks to evaluate performance.

// ANALYSIS

Automating prompt and skill optimization via recursive feedback loops is becoming an essential practice as frontier AI models advance.

–Reduces manual re-prompting effort by leveraging the model to self-improve its own tool/skill definitions.
–Automated test task creation provides clear benchmarks for skill efficacy.
–Highlights practical adaptation strategies for developers integrating next-generation LLMs into existing workflows.

// TAGS

somethingbigaipromptsskillsopus-5automationworkflows

DISCOVERED

1h ago

2026-08-07

PUBLISHED

2h ago

2026-08-07

RELEVANCE

7/ 10

AUTHOR

mattshumer_

// KEEP READING

More AI developer news from the feed

EXPLORE FULL FEED

NEWS1h ago

Apple Rejects Astronomy App Dark Hours as Astrology

Developer Terry Godier created Dark Hours, an astronomy app designed to help everyday people view night sky events. Apple's App Review Board repeatedly rejected the submission after confusing astronomy with astrology and claiming it contained a non-existent live tarot feature.

NEWS1h ago

Chollet shifts LLM view on test-time compute

François Chollet reflects on the evolution of LLM capabilities, noting how test-time compute (TTC) techniques demonstrated by models like OpenAI's o3 on the ARC benchmark altered his perspective. While base LLM scaling hit traditional plateaus, dynamic inference compute enables scaling model skills on complex reasoning tasks relative to compute budget.

NEWS2h ago

Databricks Cuts Internal AI Coding Spend by 70%

Databricks shared its strategy for scaling AI coding assistant adoption across engineering teams while slashing overall expenses by 70%. By implementing dynamic task routing through their Unity AI Gateway to send routine requests to lower-cost models, optimizing prompt token overhead, and establishing coupled daily and monthly spending tripwires, Databricks prevented runaway API costs without compromising developer velocity.