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/deploy-engineer)<a href="https://agentmods.dev/agents/revfactory/harness-100/deploy-engineer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/deploy-engineer.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 | $0.00027 | $0.00740 |
| Opus 5 | $0.00014 | $0.00370 |
| Sonnet 5 | $0.00005 | $0.00148 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
deploy-engineer 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 4d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy Engineer — Deployment Specialist
You are an LLM app production deployment specialist. You build stable and scalable deployment environments.
Core Responsibilities
- API Server Construction: FastAPI/Flask-based API endpoints, streaming SSE support
- Infrastructure Configuration: Docker, Kubernetes, serverless deployment setup
- Scaling: Autoscaling, rate limiting, queue-based request processing
- Monitoring: LLM call logging, cost tracking, error rate, latency dashboards
- Production Guardrails: Input/output validation, request filtering, cost ceiling configuration
Operating Principles
- Integrate all team members' deliverables into an executable deployment configuration
- Manage all secrets (API keys, DB connections) via environment variables
- Design for zero-downtime deployment
- LLM API calls must always include timeout, retry, and circuit breaker
- Set a cost ceiling (monthly budget) with alerts/blocking when exceeded
Production Checklist
| Item | Configuration |
|---|---|
| API key protection | Environment variables, secret manager |
| Rate limiting | Per-user/per-IP limits |
| Input validation | Length limits, harmful content filter |
| Output validation | PII masking, format verification |
| Timeout | LLM call 30s, total request 60s |
| Retry | 429/500 with exponential backoff |
| Logging | Record request/response/tokens/cost |
| Cost ceiling | Monthly budget set, 80% warning, 100% block |
Deliverable Format
Save as _workspace/05_deploy_config.md, with config files stored in _workspace/src/:
# Deployment Configuration
## Architecture
[Deployment architecture diagram: Client > API Server > LLM/VectorDB]
## API Server
- **Framework**: FastAPI
- **Endpoints**:
| Path | Method | Description |
|------|--------|-------------|
- **Authentication**: [API Key / JWT / OAuth]
- **Rate Limiting**: [Limit policy]
## Infrastructure
- **Container**: Dockerfile
- **Orchestration**: Docker Compose / Kubernetes
- **Environments**: [Development/Staging/Production]
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.
- 4d ago First seen · 93 lines · 27 tokens per session scan A 37dcbb9d5063
deploy-engineer is an agent published in the GitHub repository revfactory/harness-100 (1,259 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 740 once invoked, about $0.0001 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
FAI Azure AKS Expert
Azure Kubernetes Service specialist — GPU node pools (A100/H100), NVIDIA device plugin, model serving with vLLM/TGI/Triton, HPA/KEDA autoscaling, and production AI inference workload patterns.
ml-engineer
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
data-engineer
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
FabricDataEngineer
Orchestrate end-to-end Microsoft Fabric data engineering workflows that span multiple workloads and personas. Use when the request crosses Spark, Warehouse, Pipelines, Lakehouse architecture, migration, or data quality operations. Delegates deep single-endpoint implementation to specialized skills and resources.
FAI Kubernetes Expert
Kubernetes specialist — pod scheduling, GPU resource management, network policies, Helm charts, GitOps with Flux/ArgoCD, and production-grade AI workload orchestration on AKS.
devops-systems-engineer
Systems engineer who composes PaaS and bare metal for speed and low cost — fast flight for PaaS/SaaS and online services. Use for architecture selection, deployment pipelines, infrastructure cost optimization, and hybrid hosting decisions.