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.
npx skills add RudyCity/superagent --skill prompt-engineering-patternsgit clone --depth 1 https://github.com/RudyCity/superagentWrote 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/skills/rudycity/superagent/prompt-engineering-patterns)<a href="https://agentmods.dev/skills/rudycity/superagent/prompt-engineering-patterns"><img src="https://agentmods.dev/badge/skills/rudycity/superagent/prompt-engineering-patterns.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 | $0.00074 | $0.01087 |
| Opus 5 | $0.00037 | $0.00544 |
| Sonnet 5 | $0.00015 | $0.00217 |
| Haiku 4.5 | $0.00007 | $0.00109 |
Grade A, and why
prompt-engineering-patterns 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.
This is a copy
91% identical to prompt-engineering-patterns — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering Patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
When to Use This Skill
- Designing complex prompts for production LLM applications
- Optimizing prompt performance and consistency
- Implementing structured reasoning patterns (chain-of-thought, tree-of-thought)
- Building few-shot learning systems with dynamic example selection
- Creating reusable prompt templates with variable interpolation
- Debugging and refining prompts that produce inconsistent outputs
- Implementing system prompts for specialized AI assistants
- Using structured outputs (JSON mode) for reliable parsing
Core Capabilities
1. Few-Shot Learning
- Example selection strategies (semantic similarity, diversity sampling)
- Balancing example count with context window constraints
- Constructing effective demonstrations with input-output pairs
- Dynamic example retrieval from knowledge bases
- Handling edge cases through strategic example selection
2. Chain-of-Thought Prompting
- Step-by-step reasoning elicitation
- Zero-shot CoT with "Let's think step by step"
- Few-shot CoT with reasoning traces
- Self-consistency techniques (sampling multiple reasoning paths)
- Verification and validation steps
3. Structured Outputs
- JSON mode for reliable parsing
- Pydantic schema enforcement
- Type-safe response handling
- Error handling for malformed outputs
4. Prompt Optimization
- Iterative refinement workflows
- A/B testing prompt variations
- Measuring prompt performance metrics (accuracy, consistency, latency)
- Reducing token usage while maintaining quality
- Handling edge cases and failure modes
5. Template Systems
- Variable interpolation and formatting
- Conditional prompt sections
- Multi-turn conversation templates
- Role-based prompt composition
- Modular prompt components
6. System Prompt Design
- Setting model behavior and constraints
- Defining output formats and structure
- Establishing role and expertise
- Safety guidelines and content policies
- Context setting and background information
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/few-shot-examples.json 4.1 KB
- assets/prompt-template-library.md 3.2 KB
- references/chain-of-thought.md 9.2 KB
- references/details.md 9.0 KB
- references/few-shot-learning.md 11 KB
- references/prompt-optimization.md 12 KB
- references/prompt-templates.md 11 KB
- references/system-prompts.md 5.3 KB
- scripts/optimize-prompt.py 8.9 KB runs code
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 · 145 lines · 74 tokens per session scan A 3a3a8a20f008
prompt-engineering-patterns is a skill published in the GitHub repository RudyCity/superagent (20 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 1,087 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to prompt-engineering-patterns, differing in 6 lines, and is treated as a copy.
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