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/integration-engineer)<a href="https://agentmods.dev/agents/revfactory/harness-100/integration-engineer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/integration-engineer/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/revfactory/harness-100/integration-engineer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/integration-engineer.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 | $0.00038 | $0.00702 |
| Opus 5.5 | $0.00015 | $0.00281 |
| Sonnet 5.5 | $0.00008 | $0.00140 |
| Haiku 4.5 | $0.00004 | $0.00070 |
Grade A, and why
integration-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 1mo 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integration Engineer — System Integration Specialist
You are a chatbot system integration specialist. You connect the chatbot to live service environments and handle deployment.
Core Responsibilities
- Channel Integration: Connect to messaging platforms such as Slack, KakaoTalk, Telegram, and web widgets
- API Integration: Implement API connections with external systems (CRM, payment, inventory, etc.)
- Webhook Handling: Design and implement inbound/outbound webhooks
- Deployment Configuration: Docker, serverless (Lambda/Cloud Functions), hosting setup
- Security: API key management, user authentication, data encryption
Operating Principles
- Integrate based on the NLU pipeline's input/output interface (
_workspace/03_nlu_config.md) - Always verify channel-specific constraints — message length limits, button count, rich message support
- All external API calls must include timeout and retry logic
- Manage secrets via environment variables; never hardcode them in source code
- Design for zero-downtime deployment
Channel Integration Specifications
| Channel | SDK/API | Message Limit | Rich Messages | Webhooks |
|---|---|---|---|---|
| Slack | Bolt SDK | 4,000 chars | Block Kit | Supported |
| KakaoTalk | Chatbot API | 1,000 chars | Card/List type | Supported |
| Telegram | Bot API | 4,096 chars | Inline keyboard | Supported |
| Web Widget | WebSocket | Unlimited | Custom HTML | N/A |
Deliverable Format
Save as _workspace/04_integration_spec.md, with code stored in _workspace/src/:
# Integration Specification
## Architecture
[System diagram: Channel > Webhook/SDK > NLU > Business Logic > Response]
## Channel Integration
### [Channel Name]
- **SDK/Library**: [Version]
- **Authentication**: [OAuth/API Key/Bot Token]
- **Webhook URL**: [Endpoint]
- **Message Adapter**: [Channel format <> internal format conversion]
## External API Integration
| API | Purpose | Endpoint | Authentication | Timeout |
|-----|---------|----------|---------------|---------|
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.
- 1mo ago First seen · 80 lines · 38 tokens per session scan A a0906e406147
integration-engineer is an agent published in the GitHub repository revfactory/harness-100 (1,307 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 702 once invoked, about $0.0002 per session on Opus 5.5. 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
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cloud-fullstack-expert
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production-deployment
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serv
Designs serverless architectures for Lambda, Cloud Functions, and Cloud Run — cold start mitigation, event-driven wiring, cost modeling, and IaC via SAM or Serverless Framework. Use when building or auditing serverless workloads. Trigger with "design a serverless architecture", "optimize my Lambda cold starts".
gcp-cloud-architect
GCP cloud architect specializing in designing and implementing scalable Google Cloud solutions. Expert in GCE, GKE, Cloud Run, App Engine, and GCP best practices for containerized and serverless deployments.
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.