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 Leeroo-AI/leeroopedia-mcp --skill self_evolve_raggit clone --depth 1 https://github.com/Leeroo-AI/leeroopedia-mcpWrote 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/leeroo-ai/leeroopedia-mcp/self_evolve_rag)<a href="https://agentmods.dev/skills/leeroo-ai/leeroopedia-mcp/self_evolve_rag"><img src="https://agentmods.dev/badge/skills/leeroo-ai/leeroopedia-mcp/self_evolve_rag/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/leeroo-ai/leeroopedia-mcp/self_evolve_rag"><img src="https://agentmods.dev/badge/skills/leeroo-ai/leeroopedia-mcp/self_evolve_rag.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 | $0.00000 | $0.00555 |
| Opus 5 | $0.00000 | $0.00278 |
| Sonnet 5 | $0.00000 | $0.00111 |
| Haiku 4.5 | $0.00000 | $0.00056 |
Grade A, and why
self_evolve_rag scanned grade A with 0 findings 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 11d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Leeroopedia Knowledge Base Tools — Reference
This document describes how to use the Leeroopedia MCP tools for the Self-Evolving RAG task. It is kept as a reference and is NOT included in the agent prompt.
Leeroopedia Knowledge Base Tools
You have access to the Leeroopedia MCP tools. Use them to learn implementation patterns, but ALWAYS validate KB recommendations against your actual empirical results. The KB provides general best practices — your specific corpus (IBM TechQA) may behave differently.
When to use KB tools:
- At the START of each phase, to learn implementation patterns (chunking strategies, atomic swap patterns, hybrid retrieval fusion methods).
- When you encounter a BUG or ERROR you cannot resolve from code alone.
- When you need to verify correctness of a specific algorithm (e.g., score normalization formula).
When NOT to use KB tools:
- Do NOT use KB recommendations to override empirical results. If your data shows BM25 outperforms vector search, trust your data — even if the KB suggests semantic search is generally superior.
- Do NOT use
query_hyperparameter_priorsto set initial fusion weights or chunk sizes. Use the defaults specified in this proposal (0.5/0.5 weights, 512-token chunks) and let the evolution loop adapt based on actual metrics. - Do NOT use KB recommendations to decide how many documents to re-chunk. The diagnosis logic should determine scope based on failure analysis, not KB heuristics.
Tool-specific guidance:
-
search_knowledge: Use to look up implementation patterns (atomic index swaps, hybrid retrieval fusion, chunking strategies). Focus on HOW to implement correctly, not WHAT hyperparameters to use. -
build_plan: Use ONCE at the start to get a high-level implementation skeleton. Do not request plans for individual evolution rounds — the evolution logic should be data-driven, not plan-driven. -
verify_code_math: Use to verify correctness of score normalization, metric computation, and atomic swap logic. This is the highest-value tool — use it after writing critical code sections.
What ships with it
6 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.
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.
- 11d ago First seen · 34 lines · 0 tokens per session scan A 389e50c70ac7
self_evolve_rag is a skill published in the GitHub repository Leeroo-AI/leeroopedia-mcp (14 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 555 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
embeddings-and-search
Use when generating embeddings, calling the 12 web-search providers, or running OCR over documents with the 4 OCR providers through liter-llm. Covers embed, search, and ocr methods plus reranking.
ingest
Populate the gnosis-mcp knowledge base — from local files, git history, or a crawled website. Handles the full matrix of flags (--force, --prune, --wipe, --embed, --include-crawled) in one place.
setup
First-time setup wizard for Gnosis MCP. Install, init the database, ingest a docs folder, wire your editor — in that order.
tune
Find the chunk-size and retrieval config that maximizes quality on YOUR corpus. Sweeps chunk sizes, runs a golden-query set, reports nDCG / MRR / Hit@5. Use after first ingest or whenever your corpus changes shape significantly.
memobase
A Russian-language skill for searching a local knowledge base built from documents and other sources such as web pages, YouTube, audio, and Obsidian notes.