Multi-file
ai-mlOfficial
Official Provider SkillView repo
Use this skill to generate an export from agentspec.json without running a build. Produces a single self-contained HTML file with three tabs: Brief (design state), Evaluations (test sets and results), and How-To Guide (step-by-step MCS build instructions). Use when the user wants a summary, status update, or shareable document.
Files5 files
SKILL.md70 lines
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Skill details
Versionv1.0.0
AuthorMicrosoft
Categoryai-ml
Skill IDmicrosoft/MCS-Agent-Builder/.claude/skills/mcs-report
Files5 files
Related skills
FeedbackFile a bug report or feature suggestion on GitHub via gh CLI. Gathers details conversationally, previews, and creates an issue. Use when the user wants to report a bug or suggest a feature.IterateUse this skill when the user asks Claude to autonomously test, fix, review, and merge a change end-to-end. Detects which lane(s) changed (frontend / backend / framework / agentspec), spawns relevant verifiers (agentic-test-loop with --require-oracle for false-green prevention; backend-verify for contracts/types/unit/server), runs a facilitator review loop using qa-challenger via Agent tool with worktree isolation until score>=9 and zero critical findings, calls multi-model-review.js review-mergeMcs BuildUse this skill to build an agent in Copilot Studio from a researched agentspec.json. Runs pre-build validation (auth, env, connections, tools, model), then creates the agent, configures instructions, knowledge, tools, model, topics, and publishes. Supports single-agent, multi-agent, and connected-agent architectures. Use after /mcs-research when agentspec.json is ready, or to resume a partially completed build.Mcs ContextUse this skill to pull M365 history about a customer or company before research. Searches emails, meetings, documents, Teams, and people via WorkIQ MCP to compile a customer context file. Use when starting a new project, when the user mentions a customer name, or when you need business context for architecture decisions.Mcs EvalUse this skill to test an agent's quality after building or fixing it. Runs eval test sets against a published agent via Direct Line API (default, auto), CopilotStudio SDK, Power CAT Kit, or MCS Native Eval (for MCP agents). Results are written per-test to evalSets[].tests[].lastResult in agentspec.json. Use after /mcs-build or /mcs-fix, or when the user wants to re-test.Mcs FixUse this skill to fix an agent after eval failures. Classifies root causes (instruction gaps, boundary violations, routing failures, knowledge gaps, scoring issues, decision mismatches), applies targeted fixes via PE and TE, then re-evaluates. Use after /mcs-eval shows failures, not during initial build (which has its own fix loop).