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/jikig-ai/soleurWrote 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/jikig-ai/soleur/soleur-engineering-prompt-engineer)<a href="https://agentmods.dev/agents/jikig-ai/soleur/soleur-engineering-prompt-engineer"><img src="https://agentmods.dev/badge/agents/jikig-ai/soleur/soleur-engineering-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/jikig-ai/soleur/soleur-engineering-prompt-engineer"><img src="https://agentmods.dev/badge/agents/jikig-ai/soleur/soleur-engineering-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 | $0.00091 | $0.00124 |
| Opus 5 | $0.00046 | $0.00062 |
| Sonnet 5 | $0.00018 | $0.00025 |
| Haiku 4.5 | $0.00009 | $0.00012 |
Grade A, and why
soleur-engineering-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 10d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 10d ago First seen · 8 lines · 91 tokens per session scan A 431f90443687
soleur-engineering-prompt-engineer is an agent published in the GitHub repository jikig-ai/soleur (15 stars, last pushed today), with no licence file. It adds 91 tokens to every session and 124 once invoked, about $0.0005 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.
Other agents, from other repositories
prompt-engineer
Prompt & guardrail engineering specialist. MUST BE USED for system-prompt design, prompt templates, prompt eval/test suites, prompt-injection defense, and LLM-judge rubrics. PROACTIVELY treats prompts as versioned, test-covered, injection-resistant contracts.
token-cost-optimizer
Use this agent when you need to apply token and cost optimizations to LLM call sites, enabling prompt caching for stable prefixes, trimming redundant or re-sent context, routing clearly-easy tasks to a cheaper model tier, setting sensible maxtokens, and batching independent calls, while explaining the estimated saving…
prompt-optimizer
Agente que transforma prompts ordinarios en prompts profesionales para IA usando arquitectura en 5 capas.
prompt-reviewer
LLM prompt-engineering expert for the review-panel skill. Spawned when the diff touches LLM/API prompts, prompt templates, or inline model instructions in application code (system/user prompts, few-shot templates, prompt-string builders). Reviews prompting quality, output contracts, context economy, injection surface…
prompt-engineer
Sharpens an existing system prompt into a tighter, more concrete, more testable one. Use when reviewing or improving a prompt rather than authoring one cold.
prompt-eng
A role for designing, testing, and improving the instructions given to AI models, often called prompts. It keeps each prompt and its changes in files so they can be compared and reviewed.