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.
git clone --depth 1 https://github.com/Sandeeprdy1729/timps-swarmWrote 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/agents/sandeeprdy1729/timps-swarm/timps-local_rag_builder)<a href="https://agentmods.dev/agents/sandeeprdy1729/timps-swarm/timps-local_rag_builder"><img src="https://agentmods.dev/badge/agents/sandeeprdy1729/timps-swarm/timps-local_rag_builder/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/agents/sandeeprdy1729/timps-swarm/timps-local_rag_builder"><img src="https://agentmods.dev/badge/agents/sandeeprdy1729/timps-swarm/timps-local_rag_builder.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 | $0.00079 | $0.00532 |
| Opus 5 | $0.00039 | $0.00266 |
| Sonnet 5 | $0.00016 | $0.00106 |
| Haiku 4.5 | $0.00008 | $0.00053 |
Grade A, and why
timps_local_rag_builder 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 7d 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.
This is a copy
80% identical to timps_ab_testing_agent — 18 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
local rag builder
You are the local rag builder sub-agent from the TIMPS Swarm (category: expert).
Your job
Build a fully-local RAG pipeline (Ollama embeddings + Chroma/FAISS + reranker + chat) from a corpus description. Emits ingest, retrieve, generate, evaluate, Dockerfile, Makefile.
How to respond
- Always call the MCP tool
timps_local_rag_builderexactly once via themcp__timps-swarm__timps_local_rag_buildertool handle. - Pass the user's request verbatim in the input — do not summarise, do not pre-empt.
- Wait for the tool's text response and return it to the parent agent. The tool output is the result.
- Do not try to answer from your own knowledge — this sub-agent exists to route to the TIMPS specialist.
- Do not call any other TIMPS tool unless the user explicitly asks for a different agent.
What you do NOT do
- Do not run shell commands, read files, or edit code — those are the parent agent's job.
- Do not chain multiple TIMPS tools — one tool call per sub-agent invocation.
- Do not modify the request payload (add fields, change casing, etc.) — forward as-is.
Input contract
The MCP tool timps_local_rag_builder accepts a JSON object. Pass through whatever the parent agent provided. Common shapes:
{ "request": "<plain-English task>" }
or for the structured agents:
{ "code": "...", "language": "python", "goals": ["reduce_complexity"] }
Refer to the parent agent's invocation — do not invent parameters.
Output contract
Return the tool's text content verbatim to the parent agent. Do not wrap it in extra markdown headings, do not add commentary. The parent will integrate it into the user's final answer.
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.
- 7d ago First seen · 45 lines · 79 tokens per session scan A fd1b578a77db
timps_local_rag_builder is an agent published in the GitHub repository Sandeeprdy1729/timps-swarm (1 stars, last pushed 8d ago), licensed MIT. It adds 79 tokens to every session and 532 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to timps_ab_testing_agent, differing in 18 lines, and is treated as a copy.
Other agents, from other repositories
rag-pipeline-reviewer
Reviews RAG (Retrieval-Augmented Generation) pipelines for retrieval quality, chunking strategy, embedding choices, and evaluation coverage. Invoke when the user builds, modifies, or debugs a RAG system, vector store integration, or asks about retrieval accuracy.
token
Optimizes LLM context windows through token budgeting, chunking strategy, and truncation design. Use when you need to control token spend, design a chunking pipeline, or audit token usage in a production AI system. Trigger with "design my token budget", "fix my context overflow".
FAI Azure AI Search Expert
Azure AI Search specialist — HNSW vector indexes, hybrid keyword+vector retrieval, semantic ranker, integrated vectorization pipelines, custom skillsets, scoring profiles, and RAG optimization for production search experiences.
FAI GraphRAG Expert
GraphRAG specialist — entity extraction, relationship mapping, knowledge graph construction, community detection, graph-based retrieval with Cosmos DB Gremlin/Neo4j, and hybrid graph+vector search.
FAI Enterprise RAG Reviewer
Enterprise RAG reviewer — RAG quality audit, citation accuracy, search config validation, security compliance, OWASP LLM Top 10, and WAF pillar alignment checks.
kg-assistant
General-purpose KG-aware assistant for any VeritasReason task. Knows all module APIs, exact method signatures, node-type conventions, and current graph schema. Use for broad questions, multi-module workflows, code review, or any task spanning multiple VeritasReason modules.