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 arcobaleno64/agy-plugin-cc --skill gemini-promptinggit clone --depth 1 https://github.com/arcobaleno64/agy-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/arcobaleno64/agy-plugin-cc/gemini-prompting)<a href="https://agentmods.dev/skills/arcobaleno64/agy-plugin-cc/gemini-prompting"><img src="https://agentmods.dev/badge/skills/arcobaleno64/agy-plugin-cc/gemini-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/arcobaleno64/agy-plugin-cc/gemini-prompting"><img src="https://agentmods.dev/badge/skills/arcobaleno64/agy-plugin-cc/gemini-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 | $0.00031 | $0.00825 |
| Opus 5 | $0.00015 | $0.00413 |
| Sonnet 5 | $0.00006 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
gemini-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 9d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini Prompting
Use this skill when gemini:gemini-rescue needs to prepare a prompt for Gemini or AGY.
Prompt the model 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 run. Split unrelated asks into separate runs.
- Tell the model 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.
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 the model 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.
Model selection guidance (<model_selection> block):
- Debug or fix tasks →
--effort high(→ gemini-3.1-pro-preview) - Research or review tasks →
--effort medium(→ gemini-3-flash-preview) - Quick formatting or structure tasks →
--effort low(→ gemini-3-flash-preview) - AGY engine:
--modeltakes an exact model id as listed byagy models;--efforttakes a nativelow,medium, orhigh; the two are mutually exclusive; and Gemini aliases are rejected for AGY.
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 the model stays narrow and avoids unrelated refactors.
Working rules:
- Prefer explicit prompt contracts over vague nudges.
- Use stable XML tag names.
- Do not raise reasoning or complexity first. Tighten the prompt and verification rules before adding more model power.
- 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 the model 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.
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.
- 9d ago First seen · 57 lines · 31 tokens per session scan A 524e110806a2
gemini-prompting is a skill published in the GitHub repository arcobaleno64/agy-plugin-cc (10 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 825 once invoked, about $0.0002 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.
Other skills, from other repositories
meta-prompting
Enhanced reasoning patterns via slash commands (/think, /verify, /adversarial, /edge, /compare, /confidence, /budget, /constrain, /json, /flip, /assumptions, /tensions, /analyze, /trade) or natural language ("argue against", "what could break", "show reasoning", "deep review", "meta-prompts", "thinking modes"…
review-prompt
Review LLM prompts against the prompt-engineering skill's principles — leading with where each line came from — and report the findings without modifying files. Use when reviewing prompt quality, auditing a prompt, evaluating a system prompt, or checking whether prompt issues are high-confidence and fixable.
refine-prompt
Transforms vague or rough prompts into precise, structured AI instructions. Use when asked to "refine prompt", "improve prompt", "make this prompt better", "promptify", "optimize prompt", "rewrite prompt", "enhance prompt", or "sharpen instructions".
gemini-3-prompting
Guidance for writing effective prompts for Antigravity (agy) / Gemini 3 models.
xml-tag-structure
Guide for structuring, parsing, and validating XML-like tags in LLM inputs, system prompts, outputs, and agent-to-agent communication. Use this skill when designing prompt templates, parsing structured content from text, or handling nested tag boundaries.
gemini-3-prompting
How to write effective prompts when delegating to or reviewing with agy (Gemini via the Antigravity CLI). Use when constructing an agy-bridge prompt for --type search/code/analysis/review/implement, when a delegated result comes back off-target, or when writing an adversarial review brief for Gemini. Triggers on…