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baoyu-image-gen

AI image generation with OpenAI GPT Image 2, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.

72

Quality

92%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

This is a well-structured, highly actionable skill that handles significant complexity (10+ providers, multiple generation modes, extensive configuration) with clear workflows and excellent progressive disclosure. Its main weakness is moderate verbosity — the identity-preservation guidance appears twice, the Codex OAuth section is lengthy, and some explanatory passages could be tightened to save tokens. Overall it's a strong skill that effectively balances comprehensiveness with navigability.

Suggestions

Consolidate the two identity-preservation sections (under 'Usage' and the standalone 'Reference-Image Identity Preservation' section) into a single section to eliminate redundancy.

Condense the Codex OAuth explanation — the core message ('Codex login ≠ OPENAI_API_KEY; use --provider codex-cli instead') can be stated in 3-4 lines with a reference link for details, rather than the current ~15 lines.

DimensionReasoningScore

Conciseness

The skill is quite long and includes some redundant sections (e.g., identity-preservation guidance is stated twice in slightly different forms, the Codex OAuth explanation is verbose and could be condensed). However, much of the content is genuinely necessary given the complexity of supporting 10+ providers. Some sections like the environment variables table are exhaustive but arguably needed as reference.

2 / 3

Actionability

The skill provides fully executable CLI commands with concrete examples for every major use case (basic generation, reference images, batch mode, provider-specific invocations). The options table, environment variables, and model resolution priority are all specific and copy-paste ready.

3 / 3

Workflow Clarity

The workflow is clearly sequenced: Step 0 (blocking config load) → provider selection → model resolution → generation. The batch mode has explicit validation (retry up to 3 attempts, success/failure counts). Error handling covers missing keys, invalid ratios, and unsupported provider/model combos with specific fallback paths. The generation mode decision table clearly guides when to use sequential vs batch vs subagents.

3 / 3

Progressive Disclosure

The skill has excellent progressive disclosure with a clear overview in the main file and well-signaled one-level-deep references to provider-specific guides, config schema, setup flow, usage examples, and fallback documentation. The references table at the bottom provides a clean navigation index. Content is appropriately split between the main skill and reference files.

3 / 3

Total

11

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is an excellent skill description that covers all key dimensions well. It provides specific capabilities, names concrete API providers, includes natural trigger terms, and has explicit 'Use when' guidance. The additional nuance about sequential vs batch parallel generation adds helpful context for skill selection.

DimensionReasoningScore

Specificity

Lists multiple concrete actions and capabilities: text-to-image generation, reference images, aspect ratios, batch generation from saved prompt files, sequential vs parallel generation modes. Also enumerates specific API providers.

3 / 3

Completeness

Clearly answers both 'what' (AI image generation with multiple APIs, supporting text-to-image, reference images, aspect ratios, batch generation) and 'when' (explicit 'Use when user asks to generate, create, or draw images' clause, plus guidance on when to use batch parallel generation).

3 / 3

Trigger Term Quality

Includes strong natural trigger terms users would say: 'generate', 'create', 'draw images', 'text-to-image', 'reference images', 'aspect ratios', 'batch generation'. Also names specific providers (OpenAI, Google, Replicate, etc.) that users might mention.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive with a clear niche: AI image generation via specific API providers. The enumeration of providers and specific capabilities (text-to-image, batch generation) makes it very unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Validation

72%

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

Validation8 / 11 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

8

/

11

Passed

Repository
jimliu/baoyu-skills
Reviewed

Table of Contents

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