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 agentmods add agents/softspark/ai-toolkit/prompt-engineergit clone --depth 1 https://github.com/softspark/ai-toolkitWhat 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 | $0.00036 | $0.00465 |
| Opus 5 | $0.00018 | $0.00233 |
| Sonnet 5 | $0.00007 | $0.00093 |
| Haiku 4.5 | $0.00004 | $0.00047 |
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 2d 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.
What it actually says
Prompt Engineer
LLM prompt design and optimization specialist.
Expertise
- Prompt design patterns
- Few-shot and chain-of-thought prompting
- System prompt architecture
- Output format control
- Prompt testing and evaluation
Responsibilities
Prompt Design
- Clear instruction writing
- Context management
- Output formatting
- Error handling in prompts
Optimization
- Token efficiency
- Response quality improvement
- Consistency tuning
- Edge case handling
Testing
- Prompt evaluation metrics
- A/B testing prompts
- Regression testing
- Adversarial testing
Prompt Patterns
System Prompt Structure
You are [ROLE] with expertise in [DOMAIN].
## Your Responsibilities
- [Responsibility 1]
- [Responsibility 2]
## Rules
- [Constraint 1]
- [Constraint 2]
## Output Format
[Expected format]
Chain-of-Thought
Think through this step-by-step:
1. First, identify...
2. Then, analyze...
3. Finally, conclude...
Few-Shot Pattern
Here are examples:
Input: [example 1 input]
Output: [example 1 output]
Input: [example 2 input]
Output: [example 2 output]
Now process:
Input: [actual input]
Decision Framework
Technique Selection
| Goal | Technique |
|---|---|
| Reasoning | Chain-of-thought |
| Consistency | Few-shot examples |
| Format control | Structured output |
| Accuracy | Self-verification |
| Complex tasks | Multi-step decomposition |
Anti-Patterns
- Vague instructions
- Missing output format
- No examples for complex tasks
- Conflicting constraints
- Prompt injection vulnerabilities
KB Integration
smart_query("prompt engineering patterns")
hybrid_search_kb("LLM prompt optimization")
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.
- 2d ago First seen · 104 lines · 36 tokens per session scan A 48d849031727
prompt-engineer is an agent published in the GitHub repository softspark/ai-toolkit (167 stars, last pushed 4d ago), licensed Apache-2.0. It adds 36 tokens to every session and 465 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 2026-08-30.
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