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Run 250+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok

72

Quality

89%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

86%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 well-structured and highly actionable, with executable examples and a clean progressive-disclosure split into real reference files. The main gap is the lack of an explicit validation/feedback loop around task execution and run completion.

Suggestions

Add an explicit workflow with a validation checkpoint, e.g. 'infsh task get <id>' and check for a succeeded state before reporting results, plus a retry/inspect path on failure.

Trim the Related Skills and Documentation link lists to the essentials to improve token efficiency.

DimensionReasoningScore

Conciseness

The body is largely lean — tables and executable code blocks carry most of the content — but the Related Skills and Documentation sections add peripheral length that could be trimmed without losing actionability.

4 / 5

Actionability

Fully executable, copy-paste-ready commands cover the common cases (image, video, LLM, search, Twitter, 3D, local uploads) and the Commands table gives concrete infsh invocations with real app IDs and flags.

5 / 5

Workflow Clarity

A clear sequence is implied via the Commands table (list → get → sample → run → task get), and 'Check task status' acts as a status checkpoint; however, there is no explicit validate/verify loop and the run→status feedback path is only implicit.

4 / 5

Progressive Disclosure

The body is a concise overview with four clearly signaled, one-level-deep references (references/authentication.md, app-discovery.md, running-apps.md, cli-reference.md), all of which exist as real files; bulk detail is appropriately deferred to those references.

5 / 5

Total

18

/

20

Passed

Description

92%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 strong: specific, action-oriented, with a comprehensive explicit trigger list and clear what/when structure. Its main weakness is overlap risk with the closely related sibling skills it cross-promotes.

Suggestions

Tighten distinctiveness by leading with the platform-scoped trigger ('inference.sh' / 'infsh') as the primary signal and demoting generic category words like 'image generation' and 'web search' that overlap with sibling skills.

Consider narrowing the model enumeration so the description reads as a trigger for this CLI skill rather than a capability list that competes with its own sub-skills.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities (image generation, video creation, LLMs, search, 3D, Twitter automation) alongside a comprehensive enumeration of named models (FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter), giving comprehensive coverage of concrete actions.

5 / 5

Completeness

Clearly answers 'what' ('Run 250+ AI apps in the cloud... image generation, video creation, LLMs...') and explicitly answers 'when' with the 'Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter' clause plus concrete trigger phrases.

5 / 5

Trigger Term Quality

The explicit 'Triggers:' tail provides comprehensive natural-language terms and synonyms users would actually say (inference.sh, infsh, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok).

5 / 5

Distinctiveness Conflict Risk

Platform-specific triggers (inference.sh, infsh) carve a niche, but broad category triggers (image generation, video generation, web search, twitter api) heavily overlap with the sibling skills this skill itself promotes (ai-image-generation, ai-video-generation, web-search), creating moderate overlap risk.

3 / 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
itsablabla/garza-os-github
Reviewed

Table of Contents

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