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ai-sdk

Vercel AI SDK expert guidance. Use when building AI-powered features — chat interfaces, text generation, structured output, tool calling, agents, MCP integration, streaming, embeddings, reranking, image generation, or working with any LLM provider.

74

1.00x
Quality

75%

Does it follow best practices?

Impact

95%

1.00x

Average score across 2 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/ai-sdk/SKILL.md

The canonical home for this skill is ai-sdk in vercel/ai

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.

A well-structured, disciplined body whose central design — distrust memory, verify everything against the installed version's bundled docs — is both non-obvious and consistently enforced, with executable commands and a type-check feedback loop. Weaknesses are mild: some repeated warnings, a slightly redundant introductory section, and no worked example of searching the bundled docs.

Suggestions

Merge the duplicated memory-distrust warnings ('Do Not Trust Your Own Memory' and 'Never use model IDs from memory' in Choosing a Model) into one stated principle referenced once, trimming the token cost.

Cut or compress the 'What the AI SDK Is' opening, which mostly restates what Claude already knows about the package, down to the one non-obvious fact (docs ship inside node_modules).

Add one concrete example of searching the bundled docs (e.g. a grep command against node_modules/ai/docs/) so the doc-verification workflow has a fully executable first step.

DimensionReasoningScore

Conciseness

The body is lean and imperative and assumes competence ("Never write AI SDK code from memory"). Minor over-explanation remains: the memory-distrust warning is repeated across 'Do Not Trust Your Own Memory' and 'Choosing a Model' ('Never use model IDs from memory'), and the 'What the AI SDK Is' opening restates broadly known facts. Efficient with minor trim opportunities — anchor 4.

4 / 5

Actionability

Contains executable, copy-paste commands (curl for the model list, 'npm view ai version', concrete node_modules/ai/docs/ paths including monorepo variants). No TypeScript examples, but omitting them is the skill's explicit, justified strategy — code from memory would contradict its version-matched-docs rule. Anchor 4: mostly executable with minor gaps (e.g. no example of grepping the bundled docs).

4 / 5

Workflow Clarity

A clear sequence runs verify-install → read version-matched docs → implement → 'Run the project's type checker', with a feedback loop ('re-check the current docs and source when they occur') and an explicit installed-vs-latest version comparison. No destructive or batch operations, so no validation cap applies; it stops short of anchor 5's full validate→fix→retry checkpoints and checklists.

4 / 5

Progressive Disclosure

No bundle files exist; the body is well-sectioned and delegates all version-sensitive detail one level deep to the installed package's docs (node_modules/ai/docs/) and ai-sdk.dev URLs — an appropriate split for a fast-moving SDK. Above the under-50-line simple-skill carve-out, so anchor 4 ('good structure; most content appropriately placed; references mostly clear') fits best.

4 / 5

Total

16

/

20

Passed

Description

75%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.

A strong description with an explicit and richly enumerated 'Use when...' trigger clause covering the SDK's real feature surface. Its main weakness is a vague what-claim ('expert guidance') backed by domain nouns rather than concrete actions, plus a broad 'any LLM provider' tail that raises mild conflict risk.

Suggestions

Replace 'Vercel AI SDK expert guidance' with concrete verbs, e.g. 'Build AI features with the Vercel AI SDK: implement chat interfaces, structured output, tool-calling agents, and streaming UIs' so the what-claim is actionable rather than a domain list.

Add the API names users actually mention when asking for help — 'useChat', 'streamText', 'generateText' — as trigger synonyms to reach anchor-5 keyword coverage.

Tighten the trailing 'or working with any LLM provider' qualifier so the description doesn't compete with generic LLM/provider skills for broad prompts.

DimensionReasoningScore

Specificity

Enumerates ten concrete feature areas ("chat interfaces, text generation, structured output, tool calling, agents, MCP integration, streaming, embeddings, reranking, image generation"), but the core action is the generic phrase "expert guidance" — capability domains rather than concrete actions. Matches anchor 4; not 5 because there are no concrete action verbs like the anchor's 'extract/fill/merge', and clearly above 3's '1-2 concrete actions'.

4 / 5

Completeness

Both parts are present: an explicit "Use when building AI-powered features — ..." trigger clause with concrete triggers, and a what-claim. However the what ("Vercel AI SDK expert guidance") is vague, so it sits at anchor 4 rather than 5's fully concrete what+when pairing; the explicit 'when' keeps it above the weaker anchors.

4 / 5

Trigger Term Quality

Natural phrases users would say are present ("chat interfaces", "tool calling", "streaming", "embeddings", "any LLM provider"), giving good keyword coverage. Falls short of anchor 5 because common variations users actually type — e.g. 'useChat', 'streamText', 'Vercel AI' — are absent from the description itself.

4 / 5

Distinctiveness Conflict Risk

Clear niche (Vercel AI SDK) with distinct triggers, but the trailing "or working with any LLM provider" broadens the trigger surface into overlap with general LLM/provider skills. Anchor 4 ('mostly distinct; minor overlap risk with closely related skills') is the best fit.

4 / 5

Total

16

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

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

Warning

Total

13

/

16

Passed

Repository
vercel/vercel-plugin
Reviewed

Table of Contents

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