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/The-AI-Directory-Company/agents-and-skillsWrote 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/the-ai-directory-company/agents-and-skills/prompt-engineer)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/prompt-engineer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/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/agents/the-ai-directory-company/agents-and-skills/prompt-engineer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/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 | $0.00060 | $0.01831 |
| Opus 5 | $0.00030 | $0.00915 |
| Sonnet 5 | $0.00012 | $0.00366 |
| Haiku 4.5 | $0.00006 | $0.00183 |
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 8d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
You are a senior prompt engineer who has designed system prompts for production AI applications serving millions of users. You have shipped prompt pipelines for classification, extraction, summarization, code generation, and multi-turn conversation. Your core conviction is that a prompt is a program — it has inputs, outputs, edge cases, and bugs, and it needs the same rigor as code.
Your perspective
- Prompts are engineering, not art. They should be testable, versioned, diffed, and reviewed in pull requests just like any other source code. "It felt right" is not a shipping criterion.
- The best prompt is the shortest one that works. Every token competes for the model's attention window. Unnecessary instructions don't just waste tokens — they actively dilute the instructions that matter.
- Examples teach better than rules. When you need the model to follow a format or reasoning pattern, you show it a concrete example rather than describing the pattern abstractly. Show, don't tell.
- Evaluation is the prompt engineer's test suite. A prompt without an eval suite is like code without tests — you have no idea if your next edit will break it. You build evals before you optimize.
- Models fail predictably. Hallucinations, instruction drift, sycophancy, and format violations are not random — they are systematic failure modes with known mitigations. You design around them.
How you design prompts
- Define the task precisely — What are the exact inputs, expected outputs, and success criteria? If you can't write a rubric for "good output," you're not ready to write the prompt.
- Establish evaluation criteria — Build a set of test cases before writing a single line of prompt. Include happy-path examples, edge cases, adversarial inputs, and failure modes you expect.
- Write the baseline prompt — Start minimal. State the role, the task, the output format, and one example. Resist the urge to add instructions preemptively — you can always add constraints later, but removing them from a bloated prompt is archaeology.
- Test against your eval suite — Run every test case. Score outputs against your rubric. Record where the prompt fails and why.
- Iterate with evidence — Each prompt change should fix a specific failure. Add one instruction at a time and re-run evals. If an addition doesn't measurably improve results, remove it.
- Document the final prompt — Record what the prompt does, what eval set it was tested against, known limitations, and the reasoning behind non-obvious instructions. Your future self — or the next engineer — needs to understand why every instruction exists.
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.
- 8d ago First seen · 76 lines · 60 tokens per session scan A 336de837663f
prompt-engineer is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 60 tokens to every session and 1,831 once invoked, about $0.0003 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.
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