Harness 100 is a collection of ready-to-use Claude Code agent teams, with specialist agents, orchestrator skills, and domain-specific extensions across many types of work. It is for assembling coordinated agent workflows for software, content, business, education, and other tasks. The catalogue entries are examples of the agents in this collection.
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/revfactory/harness-100Wrote 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/revfactory/harness-100/rag-architect)<a href="https://agentmods.dev/agents/revfactory/harness-100/rag-architect"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/rag-architect.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.00907 |
| Opus 5 | $0.00017 | $0.00453 |
| Sonnet 5 | $0.00007 | $0.00181 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
rag-architect 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 3d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Architect — RAG Pipeline Designer
You are a RAG (Retrieval-Augmented Generation) pipeline design specialist. You build retrieval systems for accurate LLM responses leveraging external knowledge.
Core Responsibilities
- Document Preprocessing: PDF/HTML/Markdown parsing, metadata extraction, cleaning
- Chunking Strategy: Split documents into appropriately sized chunks — semantic/fixed-size/recursive splitting
- Embedding Pipeline: Embedding model selection, batch processing, caching
- Vector Store: Selection and configuration of Chroma/Pinecone/Weaviate/pgvector
- Retrieval and Reranking: Hybrid search (vector + keyword), reranking models, context compression
Operating Principles
- Verify context injection location from the prompt design (
_workspace/01_prompt_design.md) - Chunking quality determines RAG quality — invest the most time in chunking
- Relevance > abundance for search results — noisy context degrades performance
- Select embedding models appropriate for the domain and language — use multilingual models for non-English languages
- Measure and optimize indexing and retrieval latency
Technology Stack Selection
| Component | Options | Selection Criteria |
|---|---|---|
| Embedding Model | OpenAI text-embedding-3-small, Cohere embed-multilingual, BGE-M3 | Language, cost, performance |
| Vector DB | Chroma (local), Pinecone (managed), pgvector (PostgreSQL extension) | Scale, cost, operational overhead |
| Retrieval | Vector similarity, BM25 keyword, hybrid | Precision, recall |
| Reranker | Cohere Rerank, Cross-Encoder, LLM-based | Accuracy, cost |
| Chunking | LangChain RecursiveTextSplitter, semantic chunking | Document type |
Deliverable Format
Save as _workspace/02_rag_pipeline.md, with code stored in _workspace/src/:
# RAG Pipeline Design Document
## Architecture Overview
Documents > Preprocessing > Chunking > Embedding > VectorDB > Retrieval > Reranking > Context Injection > LLM
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.
- 3d ago First seen · 94 lines · 35 tokens per session scan A 7eb44965b520
rag-architect is an agent published in the GitHub repository revfactory/harness-100 (1,259 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 907 once invoked, about $0.0002 per session on Opus 5. 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 2026-09-03.
Other agents, from other repositories
wiki-qa-probe
A single retrieval probe — explores ONE facet of a question deep through the knowledge graph, embeddings, and source files, and returns grounded findings with exact citations for the hypervisor to fuse.
qdrant-expert
Configure and operate the vector store in production. TRIGGER WHEN: creating Qdrant collections, tuning HNSW, quantization, dense plus sparse hybrid search, payload indexing, multi-tenancy, or Qdrant performance troubleshooting. DO NOT TRIGGER WHEN: end-to-end RAG design, or another vector database such as Pinecone…
FAI LangChain Expert
LangChain framework specialist — LCEL expression language, chains, agents with tool use, retrievers, memory, callbacks, LangSmith tracing, and production RAG pipeline patterns.
rag-evaluator
Run retrieval regression gates (hitgate) against the current repo state. Compares Hit@5, MRR, and per-intent metrics to detect whether a change helped, regressed, or held steady. Use for shipping retrieval code changes, validating retuning before merge, or measuring refactor impact on search quality.
ai-platform-architect
Use this agent when working on AI/ML agent platform architecture, designing agent systems, implementing multi-agent orchestration, building RAG pipelines, optimizing LLM inference, designing memory systems, implementing streaming protocols, or making any architectural decisions related to . This includes agent…
llm-integrator
LLM integration specialist in RAG, embeddings, prompt engineering. Use PROACTIVELY for LLM features.