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 ckanner/agent-skills --skill prompt-optimizergit clone --depth 1 https://github.com/ckanner/agent-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/skills/ckanner/agent-skills/prompt-optimizer)<a href="https://agentmods.dev/skills/ckanner/agent-skills/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/ckanner/agent-skills/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/ckanner/agent-skills/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/ckanner/agent-skills/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 | $0.00083 | $0.01634 |
| Opus 5.5 | $0.00033 | $0.00654 |
| Sonnet 5.5 | $0.00017 | $0.00327 |
| Haiku 4.5 | $0.00008 | $0.00163 |
Grade A, and why
prompt-optimizer 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.
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimizer
Overview
This skill transforms user-provided prompts into high-quality, clear, and effective instructions optimized for AI models. Apply proven prompt engineering principles to enhance clarity, specificity, structure, and effectiveness. The skill uses a systematic workflow to analyze, identify improvement opportunities, and restructure prompts based on industry best practices.
When to Use This Skill
Activate this skill when users:
- Explicitly request prompt optimization or improvement
- Provide vague or unclear instructions that need refinement
- Ask for help making their requests more effective
- Submit poorly structured prompts that would benefit from reorganization
- Request guidance on how to better communicate with AI models
- Present complex tasks that need to be broken down into clearer instructions
Optimization Workflow
Follow this systematic process to optimize any prompt:
Step 1: Analyze the Original Prompt
Examine the user's prompt and identify:
Clarity issues:
- Ambiguous terms or vague requirements
- Implicit assumptions that should be explicit
- Missing context or background information
Specificity gaps:
- Lack of concrete constraints or requirements
- Undefined success criteria
- Missing audience or purpose information
- Unclear scope or boundaries
Structure problems:
- Disorganized or stream-of-consciousness format
- Missing logical flow
- Lack of clear sections or hierarchy
Format considerations:
- No specified output format
- Unclear expectations about length, tone, or style
- Missing examples or templates
Complexity assessment:
- Determine if the task is too complex for a single prompt
- Identify if the request would benefit from prompt chaining
- Assess if step-by-step reasoning is needed
Step 2: Identify the Core Intent
Determine the fundamental objective behind the user's request:
- What is the user ultimately trying to accomplish?
- What problem are they trying to solve?
- What would constitute a successful output?
- Who is the intended audience or consumer of the output?
What ships with it
3 files 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.
- 1mo ago First seen · 241 lines · 83 tokens per session scan A 732c264a15ca
prompt-optimizer is a skill published in the GitHub repository ckanner/agent-skills (23 stars, last pushed 19d ago), licensed Apache-2.0. It adds 83 tokens to every session and 1,634 once invoked, about $0.0003 per session on Opus 5.5. 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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