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 IchenDEV/prompt-optimizer-plugins --skill optimize-gemini-promptsgit clone --depth 1 https://github.com/IchenDEV/prompt-optimizer-pluginsWrote 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/ichendev/prompt-optimizer-plugins/optimize-gemini-prompts)<a href="https://agentmods.dev/skills/ichendev/prompt-optimizer-plugins/optimize-gemini-prompts"><img src="https://agentmods.dev/badge/skills/ichendev/prompt-optimizer-plugins/optimize-gemini-prompts.svg" alt="Measured on agentmods" 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 | $0.00126 | $0.01602 |
| Opus 5 | $0.00063 | $0.00801 |
| Sonnet 5 | $0.00025 | $0.00320 |
| Haiku 4.5 | $0.00013 | $0.00160 |
Grade A, and why
optimize-gemini-prompts 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 7d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize Gemini Prompts
Turn rough ideas and existing prompt stacks into copy-ready prompts for Gemini. Preserve the user's intent and hard constraints, ask for genuinely blocking information, and add model-specific structure only when it changes behavior.
Use the official basis
Read references/official-guidance.md when model-specific rationale, runtime guidance, or the longer checklist is needed. Treat its prose as a dated summary and its URLs as canonical sources. Fetch the live Google AI pages before making claims about current model IDs, API fields, thinking controls, tool support, context limits, knowledge cutoffs, availability, or pricing.
Follow the workflow
1. Capture the prompt contract
Identify:
- the intended outcome, audience, language, and desired level of detail;
- the target Gemini model or model family when version-specific behavior matters;
- the text, documents, images, audio, video, examples, or other inputs Gemini will receive;
- facts, definitions, and hard constraints to preserve;
- the required output content, structure, schema, and length;
- allowed sources, grounding, tools, calculations, citations, and validation needed for success;
- fallback behavior when supplied context or tools do not support an answer.
Treat these as diagnostic dimensions, not mandatory headings. Preserve system, developer, user, tool-description, and untrusted-data boundaries when the user supplies a layered prompt stack.
2. Apply the clarity gate
Ask a question only when two reasonable answers would materially change the outcome, source boundary, model-specific strategy, multimodal interpretation, schema, permissions, or success criteria.
Treat these gaps as blocking by default:
- a vague topic and generic verb with no identifiable deliverable or audience;
- an input or source set whose identity changes what Gemini may conclude;
- a multimodal request that does not identify which media or regions the instructions refer to;
- a strict output contract with undefined fields, allowed values, or missing-value behavior;
- an external or irreversible action without clear authorization;
- conflicting requirements or a request for version-specific advice without a target model when the answer differs materially by version.
What ships with it
2 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.
- 7d ago First seen · 96 lines · 126 tokens per session scan A 1e2e196f773f
optimize-gemini-prompts is a skill published in the GitHub repository IchenDEV/prompt-optimizer-plugins (6 stars, last pushed yesterday), licensed MIT. It adds 126 tokens to every session and 1,602 once invoked, about $0.0006 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 2026-08-31.
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