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name: visual_prompt_recovery_harness description: Run a bounded mechanism-faithful proxy recovery experiment for visual prompting papers.

Visual Prompt Recovery Harness

Use this skill to assemble a fast recovery experiment for Exploring Visual Prompts. The harness should exercise the generated prompt-template, CLIP-output-transformation, and prompt-training skills rather than duplicating their contracts silently. It is suitable for soft-mode proxy recovery when full CLIP training over paper datasets is blocked or too expensive.

Inputs

• Attempt directory with module_plan.json.
• Generated skill root.
• environment/runtime_handoff.json.
• A tiny synthetic or real classification dataset.

Outputs

• recovery/recovery_result.json with metric and paper target.
• recovery/logs/generated_data_item.json.
• recovery/logs/training_trace.json.
• recovery/logs/generated_skill_invocations.json.

Workflow

1. Read the module-plan target instead of hard-coding the paper metric.
2. Construct the smallest dataset that still needs a universal visual prompt.
3. Run frozen-model scoring before prompt optimization.
4. Optimize only prompt parameters and record before/after loss and accuracy.
5. Cross-check that output transformation uses CLIP-style text prompts/cosine probabilities.
6. Mark the result as proxy when full CLIP and datasets are not used, and include explicit mechanism checks.

Validation

The included script can be imported by recovery code to compute accuracy gain and write validator-compatible JSON fragments.

Limitations

This skill is not a replacement for a full paper reproduction. It documents a reduced executable recovery when allowed by recovery mode.