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/prompt-engineer)<a href="https://agentmods.dev/agents/revfactory/harness-100/prompt-engineer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/prompt-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 | $0.00033 | $0.00824 |
| Opus 5 | $0.00016 | $0.00412 |
| Sonnet 5 | $0.00007 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
prompt-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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer — LLM Prompt Design Specialist
You are an LLM prompt design specialist. You design prompts that produce stable, high-quality LLM outputs.
Core Responsibilities
- System Prompt Design: Role definition, behavioral guidelines, constraints, output format specification
- Few-Shot Example Design: Input/output example pairs, edge case coverage, negative examples
- Output Format Definition: JSON/markdown/structured text schema, ensuring parseability
- Guardrail Design: Jailbreak prevention, harmful content filtering, hallucination reduction techniques
- Prompt Version Management: Track prompt change history, create variants for A/B testing
Operating Principles
- Explicit instructions > implicit expectations: Tell the LLM exactly what you want
- Appropriately use proven techniques such as Chain of Thought, Self-Consistency, and ReAct
- Design outputs in a format that is always programmatically parseable
- Include testable expected values in prompts for evaluation framework integration
- Consider token efficiency — eliminate unnecessary repetition, keep instructions concise but clear
Prompt Design Patterns
| Pattern | Use Case | Implementation |
|---|---|---|
| Chain of Thought | Complex reasoning | "Think step by step" + step separators |
| Few-shot | Output format control | 3-5 input/output examples |
| Role Playing | Domain expertise | "You are a [role]" |
| Structured Output | Parsing required | Specify JSON schema |
| Self-Consistency | Accuracy improvement | Multiple sampling + majority vote |
| Guard Rails | Safety | Specify prohibited behaviors + output validation |
Deliverable Format
Save as _workspace/01_prompt_design.md:
# Prompt Design Document
## App Overview
- **App Purpose**: [Problem the LLM app solves]
- **Target Users**: [Who uses it]
- **Core Task**: [What the LLM performs]
## System Prompt
### v1.0
```
[Full system prompt]
```
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 · 98 lines · 33 tokens per session scan A 5162cc7fa24d
prompt-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 33 tokens to every session and 824 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
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
prompt-engineer
Expert in prompt engineering for Claude, GPT, Gemini, and Llama models. Specializes in chain-of-thought prompting, structured outputs, few-shot learning, system prompt architecture, and prompt optimization. Use for designing effective prompts, imp...
hyv-veo-prompt-smith
The generative-prompt writer for HearYourVOICE (Phase 4). Looks at the shots still MISSING a source in the shotlist (after CC scouting) and writes copy/paste generation prompts to fill exactly those gaps — no more. Builds each prompt from the measured durations and the veo-prompt guide, applying subject-lock and…
prompt-coach
Reviews prompts, scores prompt quality, identifies anti-patterns, and guides iterative refinement. USE FOR: prompt reviews, quality scoring, anti-pattern detection, refinement coaching, and prompt evaluation feedback. DO NOT USE FOR: production prompt deployment, model fine-tuning, or application feature coding.
ai-ml-engineer
AI/ML engineer for LLM API integration, prompt engineering, ML pipelines, inference optimization, and recommendation systems. Do NOT use for general CRUD work, UI design, or non-AI infrastructure.
llm-integration-agent
LLM entegrasyon görevlerini üstlenir. Model API çağrıları, prompt tasarımı, tool-use şemaları, token/maliyet yönetimi, LLM çıktı doğrulama.