rag-system-builder-advanced-reranking
Sub-skill of rag-system-builder: Advanced: Reranking.
Best use case
rag-system-builder-advanced-reranking is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of rag-system-builder: Advanced: Reranking.
Teams using rag-system-builder-advanced-reranking should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/advanced-reranking/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How rag-system-builder-advanced-reranking Compares
| Feature / Agent | rag-system-builder-advanced-reranking | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of rag-system-builder: Advanced: Reranking.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Advanced: Reranking
## Advanced: Reranking
Add a reranking step for improved precision:
```python
from sentence_transformers import CrossEncoder
class Reranker:
def __init__(self, model_name='cross-encoder/ms-marco-MiniLM-L-6-v2'):
self.model = CrossEncoder(model_name)
def rerank(self, query, candidates, top_k=5):
"""Rerank candidates using cross-encoder."""
pairs = [(query, c['text']) for c in candidates]
scores = self.model.predict(pairs)
for i, score in enumerate(scores):
candidates[i]['rerank_score'] = float(score)
reranked = sorted(candidates, key=lambda x: x['rerank_score'], reverse=True)
return reranked[:top_k]
# Usage in RAG pipeline
def query_with_rerank(self, question, initial_k=20, final_k=5):
# First pass: retrieve more candidates
candidates = semantic_search(self.db_path, question, self.model, top_k=initial_k)
# Second pass: rerank for precision
reranked = self.reranker.rerank(question, candidates, top_k=final_k)
return reranked
```Related Skills
repo-ecosystem-hygiene
Interpret the daily read-only repo ecosystem hygiene audit and route remediation through approved workflows.
provider-session-ecosystem-audit-and-exporters
Build and maintain cross-provider session-log audits for Codex, Codex, Hermes, and Gemini, including exporter design, normalization, and behavioral verification.
ecosystem-terminology
Canonical names, abbreviations, and relationship vocabulary for the workspace-hub ecosystem. Load this when naming repos, modules, machines, files, or expanding acronyms to ensure consistency across humans and agents. type: reference
llm-wiki-ecosystem-gap-to-issues
Review the workspace-hub LLM-wiki/document-intelligence ecosystem, identify high-leverage gaps, and create grounded GitHub feature issues without duplicating existing work.
hermes-ecosystem-integration
Wire Hermes into workspace-hub ecosystem — multi-repo skills, config sync, session export to learning pipeline, memory cross-pollination, skill patch tracking, and cross-machine health checks.
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.
mcp-builder
Guide for building high-quality Model Context Protocol (MCP) servers that allow LLMs to interact with external services. Use when creating new MCP integrations, tools, or servers for Codex or other AI systems.
rag-system-builder
Build Retrieval-Augmented Generation (RAG) Q&A systems with Codex or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.
knowledge-base-builder
Build searchable knowledge bases from document collections (PDFs, Word, text files). Use for creating technical libraries, standards repositories, research databases, or any large document collection requiring full-text search.
interactive-dashboard-builder
Create self-contained HTML/JavaScript dashboards with Chart.js, filters, and professional styling
skill-ecosystem-curation
Class-level skill ecosystem curation: housekeeping, deduplication/collision reconciliation, archival, and consolidation governance.
provider-session-ecosystem-audit
Audit Codex/Codex/Hermes/Gemini session logs, normalize provider-specific quirks, and wire recurring exports/reporting for ongoing ecosystem health checks.