Results for “automatic-calibration”

Iteratively improves a compiled LaTeX paper through two rounds of external LLM review, fix implementation, and recompilation.

[OMX] Strict autonomous loop: $deep-interview -> $ralplan -> $ultragoal (+ $team if needed) -> $code-review -> $ultraqa

Autonomous evolutionary code improvement engine with tournament selection

Configures AutoML pipelines with algorithm selection, feature engineering, and ensemble methods

Autonomously repairs failed task verification by choosing retry, decompose, prune, or escalate strategies before surfacing to the user.

When reviewing code, generate the complete corrected version — not just the issues, but the exact fixed code ready to copy-paste.

Autonomous experiment loop — iteratively mutate code, measure a metric, keep only improvements (hill-climbing ratchet)

Audits code quality against a shared contract, producing structured findings and blocking pipeline phases on critical issues without modifying code.

This skill should be used when the user wants to "improve a skill", "update skill based on feedback", "optimize skill after use", or says "自更新", "/自更新". Collects user feedback after using a skill and automatically updates that skill with improvements.

建议在逻辑间隔处手动压缩上下文，以在任务阶段中保留上下文，而非任意的自动压缩。

Turn "is this account a real fit?" into a stacked verdict. A composite 0-100 fit score built from every signal you have, a breakdown by source, override flags for when a scoring tool is wrong, a channel classification, and a written rationale. Built for reps, marketers, and RevOps, customizable to your CRM and whatever scoring you use. Trigger on "is {account} a real fit?", "score this list against our profile", "find lookalikes to our best customers", "why is the score wrong on {account}?", "validate this prospect list", "what's our coverage in {segment}?", or any account or list qualification.

Expand test automation coverage for codebase. Use when user says "lets expand test coverage" or "I want to automate tests"