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gemini-3-image-generation

Generate images with Gemini 3 Pro Image (Nano Banana Pro). Covers 4K generation, text rendering, grounded generation with Google Search, conversational editing, and cost optimization. Use when creating images, generating 4K images, editing images conversationally, fact-verified image generation, or image output tasks.

Quality

77%

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SecuritybySnyk

Low

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tessl review fix ./skills/gemini-3-image-generation/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

53%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 delivers executable, copy-paste-ready guidance for each core task and is well wired into a real one-level-deep bundle of references and scripts. Its weaknesses are verbosity (duplicated capability lists and repeated code boilerplate) and the absence of validation/recovery loops around batch generation, which caps workflow clarity at 3.

Suggestions

Collapse the duplicated capability lists (Overview 'Key Capabilities', 'When to Use This Skill', and the closing 'Summary' checklist) into a single section, and factor the repeated genai.configure/model/`if response.parts:` boilerplate out of the seven task code blocks, referencing scripts/generate-image.py instead.

Add error recovery to the batch generation loop (per-prompt try/except with retry and verification that each file was written) so batch operations have explicit validation checkpoints.

Replace generic prompting advice ('Be specific and detailed', 'Example Editing Commands' like 'Make it darker/lighter') with model-specific behavior Claude would not already know, or move it into references/generation-guide.md.

DimensionReasoningScore

Conciseness

The capability list is stated three times ('Key Capabilities', 'When to Use This Skill', and the 'Summary' ✅ checklist), all seven tasks repeat the same genai.configure/model/`if response.parts:` save boilerplate, and sections like 'Example Editing Commands' ('Make it darker/lighter') restate what Claude already knows. This is noticeably verbose with several padded sections, not merely in need of tightening (anchor 3).

2 / 5

Actionability

Examples are concrete and executable (configure, generate, save; error handling with specific exception types like `exceptions.ResourceExhausted`), but minor gaps remain: Task 6 defines three prompts yet only executes `prompt_169`, and Tasks 2, 4, and 7 rely on a `model` variable defined in earlier blocks rather than being self-contained. This falls just short of anchor 5's fully copy-paste-ready coverage.

4 / 5

Workflow Clarity

Each task is a clearly sequenced procedure with response checks (`if response.parts and hasattr(response.parts[0], 'inline_data')`), but the batch generation loop has no retry or per-image verification, and 'Handle Errors: Implement retry logic' is advice with no implementation. Per the rubric, batch operations without validation checkpoints cap this dimension at 3.

3 / 5

Progressive Disclosure

Good structure: every task ends with a clearly signaled one-level-deep pointer (e.g., 'See: references/generation-guide.md') and the References section lists all six reference guides and three scripts, all of which exist in the bundle. However, the ~500-line body inlines seven full task code examples whose boilerplate overlaps the already-provided scripts/references, leaving minor organization gaps versus anchor 5's appropriately split content.

4 / 5

Total

13

/

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.

A strong description: it explicitly answers both what the skill does and when to use it with concrete, user-natural trigger phrases, and it is anchored to a specific model that distinguishes it from sibling skills. The only shortcoming is a few missing natural synonyms for image generation.

DimensionReasoningScore

Specificity

Names five concrete capabilities — '4K generation, text rendering, grounded generation with Google Search, conversational editing, and cost optimization' — under the primary action 'Generate images', giving comprehensive domain coverage with no gaps. Anchor 4's 'minor gaps in coverage' does not apply.

5 / 5

Completeness

Both halves are explicit: the 'what' is 'Generate images with Gemini 3 Pro Image (Nano Banana Pro). Covers 4K generation, text rendering...' and the 'when' is a concrete 'Use when...' clause with trigger phrases. This matches the anchor-5 example structure exactly, so a 4 ('when' could be more explicit) would be too low.

5 / 5

Trigger Term Quality

'Use when creating images, generating 4K images, editing images conversationally, fact-verified image generation, or image output tasks' provides good natural-phrase coverage, but misses common variations such as 'image generation', 'text-to-image', or 'make an image'. This matches anchor 4 ('good keyword coverage; a few natural terms missing') rather than anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

It names a specific model ('Gemini 3 Pro Image (Nano Banana Pro)') and scopes triggers to generation/output ('creating images... editing images conversationally... image output tasks'), establishing a clear niche distinct from image-analysis skills. Only the broad phrase 'image output tasks' leaves minimal overlap risk, which still fits anchor 5 better than anchor 4.

5 / 5

Total

19

/

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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (512 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

fernandezbaptiste/Skrillz

Path

skills/gemini-3-image-generation/SKILL.md

Reviewed

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