clip-aware-embeddings
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".
npx skills add majiayu000/claude-skill-registry --skill clip-aware-embeddings-curiositech-some-claude-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# CLIP-Aware Image Embeddings Smart image-text matching that knows when CLIP works and when to use alternatives. ## MCP Integrations | MCP | Purpose | |-----|---------| | **Firecrawl** | Research latest CLIP alternatives and benchmarks | | **Hugging Face** (if configured) | Access model cards and documentation | ## Quick Decision Tree ``` Your task: ├─ Semantic search ("find beach images") → CLIP ✓ ├─ Zero-shot classification (broad categories) → CLIP ✓ ├─ Counting objects → DETR, Faster R-CNN ✗ ├─ Fine-grained ID (celebrities, car models) → Specialized model ✗ ├─ Spatial relations ("cat left of dog") → GQA, SWIG ✗ └─ Compositional ("red car AND blue truck") → DCSMs, PC-CLIP ✗ ``` ## When to Use This Skill ✅ **Use for**: - Semantic image search - Broad category classification - Image similarity matching - Zero-shot tasks on new categories ❌ **Do NOT use for**: - Counting objects in images - Fine-grained classification - Spatial understanding - Attribute binding - Negation handling ## Installation ```bash pip install transformers pillow torch sentence-transformers --break-system-packages ``` **Validation**: Run `python scripts/validate_setup.py` ## Basic Usage ### Image Search ```py
- MCP Integrations
- Quick Decision Tree
- When to Use This Skill
- Installation
- Basic Usage
- Image Search
- Common Anti-Patterns
- Anti-Pattern 1: "CLIP for Everything"
- Anti-Pattern 2: Fine-Grained Classification
- Anti-Pattern 3: Spatial Understanding
- Anti-Pattern 4: Attribute Binding
- Evolution Timeline
- 2021: CLIP Released
- 2022-2023: Limitations Discovered
pip install transformers pillow torch sentence-transformers --break-system-packages python scripts/validate_clip_usage.py \ python scripts/diagnose_clip_issue.py --image path/to/image --query "your query"
What does the clip-aware-embeddings skill do?
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill clip-aware-embeddings-curiositech-some-claude-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.