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sync-model-catalog

Regenerate and refresh the curated agent model catalog (the label/description/pricing/ratings behind the agent model picker). Use when the pinned @earendil-works/pi-ai version bumps, when a Claude Code build changes its accepted alias set, or before a release when the curated Claude/Pi facts (lineup, pricing, ratings) need refreshing from current public sources. Owns the data files under sdks/python/agenta/sdk/agents/data/; never edits capabilities.py logic.

67

Quality

81%

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SecuritybySnyk

Low

Low-risk findings worth noting

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 operational skill with concrete commands and clear job sequencing. Validation is present but the error-recovery loop is implicit, and jobs 2-3 rely on guidance rather than executable steps.

Suggestions

Make the validation feedback loop explicit in the Validate section (e.g. 'if the test fails, fix the data file and re-run pytest until it passes').

Add a concrete probe command or code snippet for job 2 so the live-runner sync step is executable rather than purely instructional.

Consider moving the additions-retirement and Fable-resolution mechanism detail into a short reference file to keep the body leaner.

DimensionReasoningScore

Conciseness

Dense and largely free of padding, assuming Claude's competence; a few explanatory prose passages (e.g. the additions-retirement rationale) could be trimmed slightly.

4 / 5

Actionability

Job 1 gives copy-paste bash with glob resolution and a runnable pytest command, but jobs 2 and 3 are instruction-only with file references rather than executable steps.

4 / 5

Workflow Clarity

Three jobs are clearly numbered and sequenced with a dedicated Validate section, but the validate->fix->revalidate feedback loop is implicit ("fails loud") rather than an explicit checkpoint.

4 / 5

Progressive Disclosure

Well-organized sections (What it owns, three jobs, Validate, When to run) with a one-level-deep pointer to design docs; no bundle files exist, and some inline mechanism detail could live in references.

4 / 5

Total

16

/

20

Passed

Description

87%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, specific description with an explicit Use-when clause and concrete triggers tied to a well-scoped niche. It is slightly verb-light on actions and uses some internal jargon, but answers both what and when clearly.

DimensionReasoningScore

Specificity

Names concrete actions ("Regenerate and refresh the curated agent model catalog") plus the specific artifacts owned (label/description/pricing/ratings), but only two verbs, leaving minor coverage gaps.

4 / 5

Completeness

Clearly states what it does (regenerate/refresh the catalog) and gives an explicit "Use when..." clause with multiple concrete trigger phrases.

5 / 5

Trigger Term Quality

Explicit "Use when" triggers like "pi-ai version bumps", "before a release", and "need refreshing from current public sources" are natural for the audience, though "accepted alias set" leans technical.

4 / 5

Distinctiveness Conflict Risk

A narrow niche (the agent model picker's curated catalog) with distinct triggers (pi-ai bumps, alias-set changes, pre-release refresh) gives minimal overlap with other skills.

5 / 5

Total

18

/

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

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Repository
Agenta-AI/agenta
Reviewed

Table of Contents

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