rag-surface-recon
From the author
Maps an authorized retrieval system's sources, connectors, chunking, embeddings, indexes, filters, ranking, caches, citations, and exposed retrieval fields. Use when scoping a RAG security assessment or explaining observed retrieval behavior.
Install
npx skills add zero-lab-ai/skills --skill rag-surface-recon -a claude-code
Installs to .claude/skills/rag-surface-recon/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
Want your agent to suggest skills while you type? See how to use skills from your IDE.
Safety checks on SKILL.md
No warnings.
Why 85
aiqbench computes the trust score from public signals. It is not a security audit, so read a skill before you install it.
Similar skills
Closest to this one by what they do and when they are used.
- ai-surface-reconzero-lab-ai/skillsMaps an authorized AI system's externally observable models, retrieval paths, agent tools, guardrails, endpoints, and privileged consumers. Use when scoping an AI security assessment or building an evidence-bound attack-surface inventory.Security★ 281
- rag-data-exposure-auditzero-lab-ai/skillsAssesses document authorization, tenant isolation, raw-chunk fields, citations, caches, exports, and sensitive-source leakage in retrieval systems. Use when an authorized RAG assessment must determine what data each identity can retrieve or disclose.Other★ 281
- network-reconzero-lab-ai/skillsPlans authorized network reconnaissance, checks proposed targets against explicit IP scope, chooses scanning tools by cost, and audits completed scans for coverage. Use when mapping a network, enumerating hosts and services, deciding which scanner to run, or determining whether an empty scan is a finding or a failure.Planning★ 281
- rag-architectJeffallan/claude-skillsDesigns and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.Planning★ 12K
- ragskillmds/skillmdImplements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.Other★ 190
- rag-evalagentscope-ai/OpenJudgeUse when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in RAG, retrieval quality, chunking optimization, or "is my RAG pipeline working." Outputs a diagnostic matrix that pinpoints whether problems are in retrieval or generation.Other★ 888
Ratings & reviews
—
no reviews
Tried this skill? Rate it to help others. Sign in
Discussion
Join the discussion. Sign in