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microsoft-foundry

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

71

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-structured routing overview: it sequences pre-execution requirements, sub-skill selection, and infrastructure/agent lifecycle workflows with concrete commands and clear navigation to one-level-deep references. It loses points mainly on length and on delegating some validation detail to sub-skill files.

Suggestions

Tighten conciseness by consolidating the restated onboarding routing (sub-skills table, Infrastructure Lifecycle, and the onboarding Tip) into a single canonical path to remove duplication.

Inline one or two key validate->fix->retry loops (e.g., for deploy and agent-optimizer) directly in the body rather than relying solely on sub-skill docs, to lift workflow_clarity toward 5.

Move the lengthy 7-step 'Common Project Context Resolution' into its own reference file (alongside agent-metadata-contract.md) and keep only a short pointer plus the resolution-precedence table in the body.

DimensionReasoningScore

Conciseness

The body is dense, operational, and assumes Claude's competence — it never explains what Foundry or agents are — but at ~280 lines with some restated routing (onboarding flow appears twice) it is heavier than a lean 5-anchor example; minor trimming is possible. Not a 3 because there is no padding or explanation of known concepts.

4 / 5

Actionability

Concrete executable commands are present (the dependency-check script invocation, the full 'az cognitiveservices account show' block with query), alongside specific MCP tool names (foundry, prompt_optimize, evaluator_catalog_get, agent_get) and concrete file layouts. Minor gaps exist because common-case commands are often delegated to sub-skill docs rather than inlined, but the references are real files.

4 / 5

Workflow Clarity

Multi-step flows are explicitly sequenced (dependency check -> azd-guidance -> sub-skill, in order) and the 7-step context-resolution section is numbered with decision branches and gating ('wait for it to finish before continuing', management-plane confirmation before acting on network errors). Not a 5 because the explicit validate->fix->retry loops mostly live in referenced sub-skill docs rather than fully inline.

4 / 5

Progressive Disclosure

A real bundle exists (references/ x3, scripts/ x2) and the body is an overview routing to ~25 clearly signaled one-level-deep sub-skill docs plus reference files, all with working markdown links. Not a 5 because substantial inline blocks (the 7-step context resolution, the intent-routing tables) could be pushed further into references; structure and signaling are clearly above the 3 anchor.

4 / 5

Total

16

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is comprehensive and tightly scoped: it enumerates concrete actions, supplies an extensive natural-language trigger list, and draws explicit negative boundaries to avoid mis-triggering. Third-person voice is used throughout, satisfying the rubric's voice requirement.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions ('Build, deploy, evaluate, optimize, fine-tune, and manage') plus granular sub-actions (azd provision/deploy, prompt agent create, invoke agent, batch eval, SFT/DPO/RFT, RBAC, quota), giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Clearly answers both 'what' (build/deploy/evaluate/optimize/fine-tune/manage end to end) and 'when' via an explicit 'USE FOR:' trigger list, plus a 'DO NOT USE FOR' negative boundary.

5 / 5

Trigger Term Quality

Comprehensive natural trigger terms including synonyms and specifics ('foundry', 'azd ai agent', 'create agent', 'batch eval', 'fine-tune', 'SFT/DPO/RFT', 'RBAC', 'large file upload'), matching the natural phrases a user would say.

5 / 5

Distinctiveness Conflict Risk

A clear Microsoft Foundry niche with an explicit 'DO NOT USE FOR' boundary (Azure Functions, App Service, general Azure deploy/prep), minimizing overlap with sibling azure-deploy/azure-prepare skills.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 45 missing, 42 deeper-than-1-level

Warning

Total

15

/

16

Passed

Repository
microsoft/azure-skills
Reviewed

Table of Contents

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