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 buiphucminhtam/forgewright --skill prompt-optimizergit clone --depth 1 https://github.com/buiphucminhtam/forgewrightWrote 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/buiphucminhtam/forgewright/prompt-optimizer)<a href="https://agentmods.dev/skills/buiphucminhtam/forgewright/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/buiphucminhtam/forgewright/prompt-optimizer/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/buiphucminhtam/forgewright/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/buiphucminhtam/forgewright/prompt-optimizer.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 | $0.00053 | $0.04332 |
| Opus 5 | $0.00026 | $0.02166 |
| Sonnet 5 | $0.00011 | $0.00866 |
| Haiku 4.5 | $0.00005 | $0.00433 |
Grade A, and why
prompt-optimizer scanned grade A with 1 finding 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 6d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
os.system(f"python optimize_skill.py --skill {skill_name}") The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
1 file 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.
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.
- 6d ago First seen · 570 lines · 53 tokens per session scan A 26d0f5a98e51
prompt-optimizer is a skill published in the GitHub repository buiphucminhtam/forgewright (49 stars, last pushed 4d ago), with no licence file. It adds 53 tokens to every session and 4,332 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
prompt-engineer
Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW).
prompt-engineering
Use when advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-consistency, meta-prompting, system design, debugging, and optimization for production AI systems. Use when working with prompt engineering.
dspy
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming.
dspy
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming.
dify-workflow
Use when dify AI workflow platform — LLM apps, knowledge bases, agents, workflow orchestration, API deployment. Use when working with dify workflow.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.