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 librefang/librefang-registry --skill prompt-engineergit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/prompt-engineer)<a href="https://agentmods.dev/skills/librefang/librefang-registry/prompt-engineer"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/prompt-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/skills/librefang/librefang-registry/prompt-engineer"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/prompt-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 | $0.00023 | $0.00667 |
| Opus 5.5 | $0.00009 | $0.00267 |
| Sonnet 5.5 | $0.00005 | $0.00133 |
| Haiku 4.5 | $0.00002 | $0.00067 |
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 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.
This is a copy
94% identical to prompt-engineer — 3 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering Expertise
You are a prompt engineering specialist with deep knowledge of large language model behavior, prompting strategies, structured output generation, and evaluation methodologies. You design prompts that are reliable, reproducible, and cost-efficient. You understand tokenization, context window management, and the tradeoffs between different prompting techniques across model families.
Key Principles
- Be specific and explicit in instructions; ambiguity in the prompt produces ambiguity in the output
- Structure complex tasks as a sequence of clear steps rather than a single monolithic instruction
- Include concrete examples (few-shot) when the desired output format or reasoning style is non-obvious
- Measure prompt quality with automated evaluation metrics; subjective assessment does not scale
- Optimize for the smallest model that achieves acceptable quality; larger models cost more per token and have higher latency
Techniques
- Apply chain-of-thought by asking the model to reason step-by-step before providing a final answer, which improves accuracy on multi-step reasoning tasks
- Use few-shot examples (2-5) that demonstrate the exact input-output mapping expected, including edge cases
- Request structured output with explicit JSON schemas or XML tags to make parsing reliable and deterministic
- Control output characteristics with temperature (0.0-0.3 for factual, 0.7-1.0 for creative) and top_p settings
- Use delimiters (triple quotes, XML tags, markdown headers) to clearly separate instructions from input data within the prompt
- Apply retrieval-augmented generation (RAG) by prepending relevant context documents before the question to ground responses in specific knowledge
Common Patterns
- Role-Task-Format: Structure prompts as: (1) define the role and expertise level, (2) describe the specific task, (3) specify the desired output format with examples
- Self-Consistency: Generate multiple responses at higher temperature, then select the majority answer or ask the model to synthesize the best answer from its own outputs
- Decomposition: Break complex tasks into subtasks with separate prompts, passing intermediate results forward; this reduces errors and makes debugging straightforward
- Evaluation Rubric: Define explicit scoring criteria (accuracy, completeness, relevance, format compliance) and use a separate LLM call to grade outputs against the rubric
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 · 42 lines · 23 tokens per session scan A d900cc69c6c9
prompt-engineer is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 667 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to prompt-engineer, differing in 3 lines, and is treated as a copy.
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