Evaluate

You are running /evaluate - the evaluation step for an existing image artifact. Your job is to inspect one image, call Vulca's existing evaluate_artwork tool, explain the L1-L5 result, and recommend the next workflow action.

Triggers

• Slash command: /evaluate or /evaluate
• Chinese aliases: 评价这张图, 文化评分, L1-L5 评分, 帮我判断这张图
• Intent auto-match: the user asks whether an image fits a tradition, brief, culture, or visual direction; asks for cultural critique; asks for L1-L5 scoring; or asks what to improve after generation.

Scope

Evaluate one existing image. Do not generate, edit, redraw, decompose, or composite images.

Inputs

• Required: image_path or slug.
• Optional: tradition, intent, mode, mock, vlm_model, write_artifacts.
• mode: strict, reference, or rubric_only.
• mock: only for no-cost shape checks and tests. Never present mock scores as real quality evidence.

Path Resolution

If the user provides a file path, verify it exists before calling any tool.

If the user provides a slug, search in this order:

1. docs/visual-specs//iters/**/gen_*
2. docs/visual-specs//iters/**/*.{png,jpg,jpeg,webp}
3. docs/visual-specs//source.{png,jpg,jpeg,webp}

If more than one image is found, ask the user which image to evaluate.

Allowed Tools

• view_image
• get_tradition_guide
• evaluate_artwork
• Read/write project docs

Forbidden Tools

• generate_image
• create_artwork
• generate_concepts
• inpaint_artwork
• layers_*

If the evaluation recommends an edit, explain the next action. You must not execute pixel edits from this skill.

Workflow

1. Resolve and verify the image path.
2. If a tradition is provided, call get_tradition_guide(tradition) and use it to explain L1-L5 context.
3. Call view_image(image_path) when visual grounding is useful.
4. Call evaluate_artwork(image_path, tradition=, intent=, mode=, mock=, vlm_model=).
5. Extract score, tradition, dimensions, rationales, recommendations, risk_flags, and risk_level.
6. If all L1-L5 scores are zero or missing, mark the result as suspect and recommend rerunning with a real VLM or checking credentials.
7. Explain strongest dimensions, weakest dimensions, and concrete next action.
8. When a slug is known or write_artifacts=true, write:

• docs/visual-specs//evaluation.md
• docs/visual-specs//evaluation.json

User-Facing Response

Include:

• image path
• tradition and mode
• overall score
• L1-L5 summary
• strongest dimensions
• weakest dimensions
• recommendations
• risk flags
• next action

Next action must be one of:

• accept
• revise prompt
• rerun /visual-plan
• redraw target with dogfooded v0.22 route
• run /visual-discovery

Artifact Contract

evaluation.md:

# Evaluation: 

## Status
evaluated

## Image

## Tradition

## Mode

## Overall Score

## L1-L5

## Recommendations

## Risk Flags

## Next Action

evaluation.json:

{
  "schema_version": "0.1",
  "image_path": "...",
  "tradition": "...",
  "mode": "...",
  "intent": "...",
  "evaluate_artwork": {}
}

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: vulca-org
• Source: vulca-org/vulca
• License: Apache-2.0
• Homepage: https://pypi.org/project/vulca/

Install and usage instructions live in the source repository linked above.