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Use when writing Python code with `axllm` for string signatures, field descriptors, JSON schema output, validation, and typed tool argument shapes.

70

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

87%

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

A concise, actionable reference of executable axllm patterns with clean sectioning and clearly signaled references. The gap is the absence of an explicit verify/test checkpoint in the suggested workflow.

Suggestions

Add an explicit verification step after generating code, e.g. run a no-key example to confirm the signature builds and to_json_schema produces the expected shape before handing code to the user.

Provide a short ordered workflow (pick a starting example → adapt the signature → render the schema → validate output) with a checkpoint so the sequence is explicit rather than implied.

DimensionReasoningScore

Conciseness

The body is lean — bullet facts and copy-paste code with no explanations of Python or library basics — and every section earns its place, matching the lean-and-efficient anchor.

3 / 3

Actionability

Multiple complete, executable examples (s(...), ax(...), the fluent f() builder, to_json_schema, program.forward) with imports are copy-paste ready, satisfying the fully-executable anchor.

3 / 3

Workflow Clarity

A loose sequence exists (Core Pattern → More Patterns → Reuse, plus "Start from the complete programs under examples/"), but there is no explicit validate/test checkpoint for the generated code, so checkpoints are only implicit.

2 / 3

Progressive Disclosure

The single SKILL.md is well-organized into clear sections and signals one-level-deep references ("Package API docs: API.md and axir-api.json", "Runnable examples: examples/", the external gallery URL) without nested pointer chains.

3 / 3

Total

11

/

12

Passed

Description

85%

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 tight, well-scoped description with an explicit Use-when trigger and concrete capabilities. Its main weakness is trigger phrasing that leans on package jargon rather than words users naturally say.

Suggestions

Add user-facing trigger phrasing alongside the technical terms, e.g. "Use when the user asks to define input/output contracts, build typed tool arguments, or generate JSON schemas with axllm."

Soften jargon like "field descriptors" and "typed tool argument shapes" with phrasings users actually say (e.g. "define fields", "tool argument types").

DimensionReasoningScore

Specificity

The description enumerates multiple concrete capabilities — "string signatures, field descriptors, JSON schema output, validation, and typed tool argument shapes" — matching the anchor for listing several specific actions.

3 / 3

Completeness

It states both what it does (the listed signature/schema capabilities) and an explicit "Use when writing Python code with `axllm`" trigger, satisfying the what-and-when anchor.

3 / 3

Trigger Term Quality

"Python code with `axllm`" and "signatures"/"JSON schema" are reasonable triggers, but "field descriptors" and "typed tool argument shapes" are technical jargon users would rarely say verbatim, so coverage is only partial.

2 / 3

Distinctiveness Conflict Risk

Scoping to the `axllm` package for signature/schema work carves a clear niche unlikely to fire for general Python skills.

3 / 3

Total

11

/

12

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

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
ax-llm/ax
Reviewed

Table of Contents

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