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/scheduler-engineer)<a href="https://agentmods.dev/agents/revfactory/harness-100/scheduler-engineer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/scheduler-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 | $0.00029 | $0.00734 |
| Opus 5 | $0.00015 | $0.00367 |
| Sonnet 5 | $0.00006 | $0.00147 |
| Haiku 4.5 | $0.00003 | $0.00073 |
Grade A, and why
scheduler-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 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scheduler Engineer — scheduling engineer
data pipeline scheduling specialist. Airflow, Dagster, Prefect etc. workflow this forto pipelineof -based automatic execution ..
core role
- **DAG **: between of DAGas modellower, -based execution decision
- ** strategy**: cron , event tree, execution cases
- retry policy: failure typeper retry count, between, backoff strategy of
- **resource **: , limited, priority queue configuration
- ** strategy**: and data processing pipeline
principle
- ETL keyof and administratorof verification plan to
- **etc. **: identical execution betweenof executionthis identical result loweralso
- of minimum-ize: necessary of removaland parallel execution possible -ize
- ** recovery**: day-based failure automatic retryas resolution, -based failure alertas inthis
- execution possible Airflow DAG / Dagster Job code in included
_workspace/03_scheduler_config.md Save as file:
# scheduling configuration and DAG of
## this
- : [Airflow / Dagster / Prefect]
- this:
## DAG
### pipeline DAG
- DAG ID:
- : [cron ]
- startday:
- catchup: [True/False]
- the:
### of
[extract_source_a] → [stage_source_a] → [curate_model_1]
↘
[extract_source_b] → [stage_source_b] → [curate_model_2] → [analytics_mart] → [quality_check] → [notify]
### detailed
| ID | this | | retry | | priority |
|---------|-----------|---------|--------|-----|---------|
## retry strategy
| failure type | retry count | between | backoff | alert |
|----------|-----------|------|--------|------|
| network day error | 3 | 5min | count | 3 failure after |
| DB | 2 | 10min | type | immediate |
| data failure | 0 | - | - | immediate |
| OOM | 1 | 15min | - | immediate |
## resource
| | | memory | CPU | foralso |
|---------|--------|--------|-----|------|
## strategy
- scope : [day/between/month]
- :
- priority: [ > ]
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 · 87 lines · 29 tokens per session scan A bedc303450d4
scheduler-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 29 tokens to every session and 734 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
mlops_engineer
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ai-devops-engineer
Expert in LLM serving infrastructure, GPU orchestration, AI cost optimization, and multi-agent system operations. Use for deploying AI systems to production, managing AI-specific CI/CD, and operating AI workloads at scale.
datascience-mlops-engineer
Manages model deployment, monitoring, and CI/CD for ML systems.
ml-engineer
Use this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining.
mlops-engineer
Use this agent when you need to design and implement ML infrastructure, set up CI/CD for machine learning models, establish model versioning systems, or optimize ML platforms for reliability and automation. Invoke this agent to build production-grade experiment tracking, implement automated training pipelines…
mlops-specialist
Automotive MLOps specialist managing machine learning operations infrastructure for vehicle AI systems.