'ai-engineering' 검색 결과

Run a local check of test evidence and retain source paths in the result. The workflow stops when required evidence is missing, keeping the findings tied to their sources.

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• Checking local test evidence
• Keeping source paths in check results
• Stopping a check when evidence is missing

Review a reusable Agent Skill package before implementation. Validate its identity fields, separate instructions from external capabilities and execution timing, and receive a validation report with a minimal set of building blocks and reasons for each choice.

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• Reviewing a workflow before turning it into a reusable skill
• Checking SKILL.md identity fields
• Choosing instructions, scripts, or delegation

Evaluate an Agent Skill bundle before release by checking structure, activation conditions, output quality, scripts, safety, file integrity, and host compatibility. Receive a detailed JSON report that distinguishes local evidence checks from production readiness backed by external attestation.

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• Reviewing a skill bundle before distribution
• Evaluating activation accuracy and output improvements
• Checking scripts, safety, and distributed files

Build a catalog from multiple skill directories using an explicit priority order. Review duplicate names, selection decisions, and omitted entries before loading the instruction text of the chosen skill.

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• Cataloging project and user skill directories together
• Checking duplicate skill names before activation
• Reviewing catalog limits and omitted entries

Prepare for the MCPA certification through interactive lessons, local Python labs, diagnostic assessments, and mock exams. Track progress, revisit weak domains, and resume learning, with guided support for learners who do not write code.

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• Starting an MCPA study plan
• Resuming the next unfinished lesson
• Learning through guided no-code labs

Study Model Context Protocol (MCP), which connects AI applications with external tools, through guided lessons and exercises. Inspect requests and responses, practice implementation and security checks, and record progress alongside observed results.

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• Learn MCP fundamentals through requests and responses
• Practice building MCP clients and servers
• Study authorization, security, and operational failures

Review Orchard deployment manifests, the files describing deployment settings, and keep source paths in the result. The workflow stops when evidence is missing, tying review findings to the available source material.

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• Reviewing Orchard deployment manifests
• Keeping source paths in review results
• Stopping a review when evidence is missing

Test your understanding of a phase in AI Engineering from Scratch through eight multiple-choice questions. Receive a score, explanations for incorrect answers, and specific lessons to review before continuing your studies.

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• Checking your understanding after completing a phase
• Assessing readiness for the next phase
• Finding lessons to review from incorrect answers

Learn to create, discover, invoke, secure, evaluate, package, and port Agent Skills through one interactive lesson at a time. Practical checkpoints and quizzes track demonstrated progress in a local file, so later sessions resume where you stopped.

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• Learn to build and invoke a minimal Agent Skill
• Understand discovery and automatic routing
• Check permission, sandbox, and trust boundaries

Review deployment evidence and recovery conditions when assessing a release. The skill directs the agent to consult the accompanying restore reference and include a timestamp in its decision, providing a narrowly defined release review procedure.

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• Reviewing deployment evidence
• Checking recovery conditions for a release
• Preparing a release decision with a timestamp

Review a release proposal by reading its deployment diff and quoting changed replica settings, which describe running instance counts. The review surfaces the specific configuration changes for inspection.

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• Review a release proposal's deployment diff
• Inspect changes to replica settings
• Quote changed settings in a release review

Review proposed file operations, commands, network access, secret handling, or destructive actions against explicit sandbox rules that define permitted activity. Receive a JSON verdict and the exact rule behind it without performing the proposed action.

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• Reviewing requested file operations before execution
• Checking commands or network access against sandbox rules
• Reviewing actions involving secrets or destructive changes

Prepare for four Claude certification tracks through guided lessons, practical exercises, quizzes, and mock exams. The tutor checks understanding and learner-created work, records progress, and uses assessment results to guide review.

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• Choosing a certification track for a learning goal
• Resuming lessons from saved progress
• Completing guided exercises without writing code

基于「AI工程造价+法律知识库」（1394人订阅，3149个内容）的造价+法律交叉领域AI技能。当用户需要进行工程造价司法鉴定、合同风险审查、变更索赔分析、结算纠纷诊断时使用。触发词包括但不限于：造价+法律、工程司法鉴证、合同风控、变更索赔、结算纠纷、鉴定意见、证据链、工期索赔、费用索赔。也适用于用户需要基于知识库内容进行案件分析、模拟鉴定、风险预警的场景。

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6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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[Code Quality] Use when a workflow step or the user asks for AI code or plan review: LLM calls, prompts, agents, RAG, evals and guardrails. --mode={code|plan}; --report-only is read-only.

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Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and p

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