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azure-ai-projects-py

Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.

Quality

68%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./skills/azure-ai-projects-py/SKILL.md
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 content is a lean, highly actionable overview: executable agent-creation code, a fully specified batch-evaluation command, a sequenced safe workflow with validation checkpoints, and a well-organized one-level-deep reference bundle. The main defects are two dangling UPSTREAM file links, slightly repetitive security hedging, and a few core workflows that get policy prose where a short code snippet would do.

Suggestions

Add the missing UPSTREAM-NOTICE.md and UPSTREAM-LICENSE.txt files (or remove the links) — the body's second paragraph points to files that don't exist in the bundle.

Consolidate the credential/secret-handling cautions that repeat across 'Setup and version boundary', the code comment, and the Safe workflow into one place to trim tokens.

For the common cases deferred to references (deployment metadata inspection, async patterns), include one short inline snippet each so the body's core workflows are executable without opening a reference file.

DimensionReasoningScore

Conciseness

The body is dense and assumes competence — no space is spent explaining concepts Claude already knows, and the version-boundary material has its own dedicated section ("Setup and version boundary") rather than being scattered. It is a 4 rather than 5 because some security-policy phrasing repeats across sections ("inspect the credential chain without printing credentials", "do not log private traces or credentials", "Do not fetch connection secrets for a listing task") and a few hedging clauses ("It is not an estimate or spend enforcement mechanism") could be tightened.

4 / 5

Actionability

The Python example (create_version + PromptAgentDefinition + responses.create) and the run_batch_evaluation.py CLI invocation with full flags are concrete and copy-paste ready, and both are executable as written. It falls short of 5 because several core workflows described in the Safe workflow (reading deployment metadata, connection inspection, async patterns) get only policy-level direction with no code or command — those are deferred to references, leaving minor gaps in coverage of common cases.

4 / 5

Workflow Clarity

The 5-step Safe workflow has a clear sequence with real checkpoints: read metadata before selecting a model, validate tool-call arguments before execution, and "If resource creation times out, reconcile its outcome before retrying" — so the batch/destructive-operation cap of 3 does not apply, since validation and reconciliation guidance is explicitly present. It is a 4 rather than 5 because feedback loops are described in prose (e.g., "inspect the printed evaluation/run IDs and cancel through the installed service's documented API") without a concrete validate-fix-retry cycle for the main agent example.

4 / 5

Progressive Disclosure

The structure is genuinely good: a compact overview body with a topical References section pointing one level deep to 10 real, well-scoped reference files plus one real script, all clearly labeled by topic. It misses 5 because the body links to [UPSTREAM-NOTICE.md](UPSTREAM-NOTICE.md) and [UPSTREAM-LICENSE.txt](UPSTREAM-LICENSE.txt) in the second paragraph, and neither file exists in the bundle — dangling references that break navigation.

4 / 5

Total

16

/

20

Passed

Description

48%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 concise, domain-specific, and unlikely to trigger for unrelated skills, but it states only a generic action and completely lacks a 'when to use' trigger clause. Concrete capabilities (versioned agents, evaluations, connections, deployments) are absent even though the body covers them. Adding a 'Use when...' clause with task keywords would substantially improve it.

Suggestions

Add an explicit trigger clause, e.g. "Use when working with Microsoft Foundry / Azure AI Foundry project connections, agents, or evaluation datasets, or when the user mentions azure-ai-projects."

Enumerate 2-3 concrete capabilities instead of the generic "Build AI applications", e.g. "Create versioned agents, manage model deployments and connections, and run cloud evaluations on datasets using the azure-ai-projects SDK on Microsoft Foundry."

Include the product's common name variation "Azure AI Foundry" so users who use that phrasing match the skill.

DimensionReasoningScore

Specificity

"Build AI applications on Microsoft Foundry using the azure-ai-projects SDK" names the domain (Microsoft Foundry, azure-ai-projects) but the only action is the generic verb "Build" — no concrete capabilities like agents, deployments, or evaluations are listed, matching the anchor 'Names the domain but actions are minimal or generic'. It is not a 3 because no 1-2 concrete actions are enumerated, and not a 1 because the domain is explicitly named rather than pure abstraction.

2 / 5

Completeness

The 'what' is clear (build AI applications on Microsoft Foundry with the azure-ai-projects SDK) but there is no 'Use when...' or equivalent trigger clause, capping completeness at 3 per the judging guidelines. It is a 3 rather than 2 because the 'what' is concrete and domain-specific, not vague.

3 / 5

Trigger Term Quality

"Microsoft Foundry" and "azure-ai-projects SDK" are relevant, natural keywords, but common variations and synonyms are missing — the product's usual name "Azure AI Foundry", and the core task terms (agents, evaluations, datasets, connections) that the body itself lists. This fits 'Some relevant keywords but missing common variations or synonyms' rather than 4's 'good keyword coverage'.

3 / 5

Distinctiveness Conflict Risk

The specific SDK name "azure-ai-projects" and platform "Microsoft Foundry" create a mostly distinct niche, but the broad lead phrase "Build AI applications" leaves minor overlap risk with closely related Azure AI skills (e.g., azure-ai-agents or Azure OpenAI SDK skills mentioned in the body). Fits 'Mostly distinct; minor overlap risk with closely related skills' rather than 5, which requires distinct triggers that unambiguously route here.

4 / 5

Total

12

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 2 missing

Warning

Total

14

/

16

Passed

Repository

sickn33/agentic-awesome-skills

Path

skills/azure-ai-projects-py/SKILL.md

Reviewed

Table of Contents

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