Loading...
Loading...
Found 1,950 Skills
Use when building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based evaluation.
A skill for huawei cloud container(CCE) assessment. It automatically collects metrics and configurations from containerized application environments on Huawei Cloud to generate a comprehensive assessment report. Use this when users want to evaluate if their Huawei Cloud applications align with cloud-native best practices and identify areas for improvement.
Consult the accepted standard and strongest supported exemplars for a decision, plan, design, completed artifact, or diff, then return a source-grounded recommendation or evaluation.
Structured comparison of competing options with weighted scoring matrices, trade-off analysis, decision frameworks, and recommendation templates. Use when evaluating alternatives, making purchase decisions, or comparing strategies.
INVOKE THIS SKILL when creating, running, or analyzing Arize experiments. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI.
OpenGame is an open-source agentic framework for end-to-end web game creation from a single text prompt, using LLMs, Game Skill (Template + Debug), and headless browser evaluation.
Create, run, diagnose, and iteratively improve Agent Skill evaluations (evals) with the skill-up CLI / 使用 skill-up CLI 创建、运行、诊断并持续改进 Agent Skill 评测. Use when the user asks to evaluate, test, regress, verify, fix, improve, iterate, or evolve a Skill; add or strengthen eval cases; write eval.yaml/case.yaml; run skill-up run/validate/list-cases/report/import/init; or migrate from Anthropic evals.json. Handles Skill discovery, eval scaffolding, judge authoring, validation, runs, reports, and evidence-based repair loops.
Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.
Used for authorized OT/ICS security assessments, covering Purdue model zoning, PLC/SCADA exposure, industrial protocol discovery, and safe passive-first evaluation.
Launch a sub-agent judge to evaluate results produced in the current conversation
Evaluate solutions through multi-round debate between independent judges until consensus
Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior