Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add DNYoussef/context-cascade --skill agentdb-advanced-featuresgit clone --depth 1 https://github.com/DNYoussef/context-cascadeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dnyoussef/context-cascade/agentdb-advanced-features)<a href="https://agentmods.dev/skills/dnyoussef/context-cascade/agentdb-advanced-features"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/agentdb-advanced-features/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dnyoussef/context-cascade/agentdb-advanced-features"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/agentdb-advanced-features.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.04659 |
| Opus 5.5 | $0.00020 | $0.01864 |
| Sonnet 5.5 | $0.00010 | $0.00932 |
| Haiku 4.5 | $0.00005 | $0.00466 |
Grade B, and why
agentdb-advanced-features scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo ufw allow 4433/udp How it starts
The opening of the file, as written. The whole thing — 666 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LIBRARY-FIRST PROTOCOL (MANDATORY)
Before writing ANY code, you MUST check:
Step 1: Library Catalog
- Location:
.claude/library/catalog.json - If match >70%: REUSE or ADAPT
Step 2: Patterns Guide
- Location:
.claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md - If pattern exists: FOLLOW documented approach
Step 3: Existing Projects
- Location:
D:\Projects\* - If found: EXTRACT and adapt
Decision Matrix
| Match | Action |
|---|---|
| Library >90% | REUSE directly |
| Library 70-90% | ADAPT minimally |
| Pattern exists | FOLLOW pattern |
| In project | EXTRACT |
| No match | BUILD (add to library after) |
When NOT to Use This Skill
- Local-only operations with no vector search needs
- Simple key-value storage without semantic similarity
- Real-time streaming data without persistence requirements
- Operations that do not require embedding-based retrieval
Success Criteria
- Vector search query latency: <10ms for 99th percentile
- Embedding generation: <100ms per document
- Index build time: <1s per 1000 vectors
- Recall@10: >0.95 for similar documents
- Database connection success rate: >99.9%
- Memory footprint: <2GB for 1M vectors with quantization
Edge Cases & Error Handling
- Rate Limits: AgentDB local instances have no rate limits; cloud deployments may vary
- Connection Failures: Implement retry logic with exponential backoff (max 3 retries)
- Index Corruption: Maintain backup indices; rebuild from source if corrupted
- Memory Overflow: Use quantization (4-bit, 8-bit) to reduce memory by 4-32x
- Stale Embeddings: Implement TTL-based refresh for dynamic content
- Dimension Mismatch: Validate embedding dimensions (384 for sentence-transformers) before insertion
Guardrails & Safety
- NEVER expose database connection strings in logs or error messages
- ALWAYS validate vector dimensions before insertion
- ALWAYS sanitize metadata to prevent injection attacks
- NEVER store PII in vector metadata without encryption
- ALWAYS implement access control for multi-tenant deployments
- ALWAYS validate search results before returning to users
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/example-1-quic-sync.md 12 KB
- examples/example-2-multi-database.md 16 KB
- examples/example-3-sharding.md 17 KB
- graphviz/workflow.dot 6.2 KB
- readme.md 12 KB
- references/distributed-patterns.md 15 KB
- references/performance-optimization.md 12 KB
- references/quic-protocol.md 15 KB
- resources/readme.md 12 KB
- resources/scripts/custom_metrics.py 12 KB runs code
- resources/scripts/multi_db_manage.sh 7.5 KB runs code
- resources/scripts/quic_sync.py 6.4 KB runs code
- resources/scripts/readme.md 6.6 KB
- resources/templates/distributed-db.json 6.2 KB
- resources/templates/hybrid-search.yaml 7.1 KB
- resources/templates/quic-config.yaml 4.2 KB
- SKILL-meta.yaml 417 B
- tests/test-1-quic-sync.md 7.2 KB
- tests/test-2-multi-db.md 11 KB
- tests/test-3-hybrid-search.md 16 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 666 lines · 51 tokens per session scan B 31bf84b7cb02
agentdb-advanced-features is a skill published in the GitHub repository DNYoussef/context-cascade (32 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 4,659 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
qdrant
Vector search engine for production RAG systems.
chroma
Embedding database for RAG and semantic search.
pinecone
Managed vector DB for production RAG and search.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. Trigger when user asks to: Store or search vector embeddings in PostgreSQL Set up semantic search, similarity search, or nearest neighbor search Create HNSW or IVFFlat indexes for vectors…
postgres-hybrid-text-search
Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). Trigger when user asks to: Combine keyword and semantic search Implement hybrid search or multi-modal retrieval Use BM25/pgtextsearch with pgvector together Implement RRF (Reciprocal…