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 m-ghalib/gemini-plugin-cc --skill gemini-3-promptinggit clone --depth 1 https://github.com/m-ghalib/gemini-plugin-ccWrote 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/m-ghalib/gemini-plugin-cc/gemini-3-prompting)<a href="https://agentmods.dev/skills/m-ghalib/gemini-plugin-cc/gemini-3-prompting"><img src="https://agentmods.dev/badge/skills/m-ghalib/gemini-plugin-cc/gemini-3-prompting/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/m-ghalib/gemini-plugin-cc/gemini-3-prompting"><img src="https://agentmods.dev/badge/skills/m-ghalib/gemini-plugin-cc/gemini-3-prompting.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 | $0.00032 | $0.00906 |
| Opus 5 | $0.00016 | $0.00453 |
| Sonnet 5 | $0.00006 | $0.00181 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
gemini-3-prompting 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 11d 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.
This is a copy
78% identical to gpt-5-4-prompting — 37 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini 3 Prompting
Use this skill when gemini:gemini-rescue needs to ask Gemini for help.
Prompt Gemini like an operator, not a collaborator. Keep prompts compact and block-structured with XML tags. State the task, the output contract, the follow-through defaults, and the small set of extra constraints that matter.
Core rules:
- Prefer one clear task per Gemini run. Split unrelated asks into separate runs.
- Tell Gemini what done looks like. Do not assume it will infer the desired end state.
- Add explicit grounding and verification rules for any task where unsupported guesses would hurt quality.
- Prefer better prompt contracts over raising reasoning or adding long natural-language explanations.
- Use XML tags consistently so the prompt has stable internal structure. Gemini 3 responds well to explicit
<thinking>tags and to JSON schemas given inline — use both when the output shape matters.
Default prompt recipe:
<task>: the concrete job and the relevant repository or failure context.<structured_output_contract>or<compact_output_contract>: exact shape, ordering, and brevity requirements.<default_follow_through_policy>: what Gemini should do by default instead of asking routine questions.<verification_loop>or<completeness_contract>: required for debugging, implementation, or risky fixes.<grounding_rules>or<citation_rules>: required for review, research, or anything that could drift into unsupported claims.
When to add blocks:
- Coding or debugging: add
completeness_contract,verification_loop, andmissing_context_gating. - Review or adversarial review: add
grounding_rules,structured_output_contract, anddig_deeper_nudge. - Research or recommendation tasks: add
research_modeandcitation_rules. - Write-capable tasks: add
action_safetyso Gemini stays narrow and avoids unrelated refactors.
How to choose prompt shape:
- Gemini has no built-in review mode, so all review jobs go through
revieworadversarial-reviewsubcommands that already carry the review contract. - Use
taskwhen the task is diagnosis, planning, research, or implementation and you need to control the prompt more directly. - Use
task --resume-lastfor follow-up instructions on the same Gemini thread. Send only the delta instruction instead of restating the whole prompt unless the direction changed materially.
Use Gemini's built-in subagents when the task benefits from dedicated exploration:
codebase_investigator(ships with thecodebase-investigationskill) — invoke it inside a prompt when Gemini needs to map call sites, trace invariants, or summarize a subsystem before answering. It is configured to usegemini-3.1-pro-preview. Mention it by name in the prompt body (e.g., "use the codebase_investigator subagent to map X before finalizing the fix") when you want Gemini to reach for it instead of guessing from a shallow read.
Working rules:
- Prefer explicit prompt contracts over vague nudges.
- Use stable XML tag names that match the block names from the reference file.
- Do not raise reasoning or complexity first. Tighten the prompt and verification rules before escalating.
- Ask Gemini for brief, outcome-based progress updates only when the task is long-running or tool-heavy.
- Keep claims anchored to observed evidence. If something is a hypothesis, say so.
Prompt assembly checklist:
- Define the exact task and scope in
<task>. - Choose the smallest output contract that still makes the answer easy to use.
- Decide whether Gemini should keep going by default or stop for missing high-risk details.
- Add verification, grounding, and safety tags only where the task needs them.
- Remove redundant instructions before sending the prompt.
Reusable blocks live in references/gemini-prompt-blocks.md. Concrete end-to-end templates live in references/gemini-prompt-recipes.md. Common failure modes to avoid live in references/gemini-prompt-antipatterns.md.
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.
- 11d ago First seen · 58 lines · 32 tokens per session scan A 8071fe729dc1
gemini-3-prompting is a skill published in the GitHub repository m-ghalib/gemini-plugin-cc (19 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 906 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to gpt-5-4-prompting, differing in 37 lines, and is treated as a copy.
Other skills, from other repositories
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…