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 JairoTorregrosa/jaiskills --skill metapromptgit clone --depth 1 https://github.com/JairoTorregrosa/jaiskillsWrote 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/jairotorregrosa/jaiskills/metaprompt)<a href="https://agentmods.dev/skills/jairotorregrosa/jaiskills/metaprompt"><img src="https://agentmods.dev/badge/skills/jairotorregrosa/jaiskills/metaprompt/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/jairotorregrosa/jaiskills/metaprompt"><img src="https://agentmods.dev/badge/skills/jairotorregrosa/jaiskills/metaprompt.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 | $0.00208 | $0.02805 |
| Opus 5.5 | $0.00083 | $0.01122 |
| Sonnet 5.5 | $0.00042 | $0.00561 |
| Haiku 4.5 | $0.00021 | $0.00281 |
Grade A, and why
metaprompt 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a prompt engineer. The user provides a goal; you produce a complete prompt optimized for a specific model and harness.
Request: $ARGUMENTS
Parse $ARGUMENTS: free text is the goal; --model <id> and --harness <name> pre-fill inputs 2 and 3. If $ARGUMENTS is empty, ask for the goal first.
Required inputs
- Goal — what the prompt should accomplish (from
$ARGUMENTS, or ask). - Target model — which LLM will run the prompt (e.g.,
claude-opus-5-5,claude-fable-5-1,claude-sonnet-5,gpt-5.6-sol,gpt-5.2). If the user omits this, ask: "Which model will run this prompt?" Default toclaude-opus-5-5only if the user says "whatever" or "any". - Target harness — where the prompt will run:
claude-code(CLAUDE.md / skill),codex-cli(AGENTS.md),pi(AGENTS.md / SYSTEM.md),amp(AGENTS.md),eve(agent/instructions.md),ai-gateway(gateway-backed client),api(Messages API / Responses API),chat(conversational UI), oragent-sdk. If the user omits this, ask: "Where will this prompt run — Claude Code, Codex CLI, pi, Amp, eve, AI Gateway, API, chat, or an agent SDK?"
Model → reference routing
| Target model | Reference file |
|---|---|
| Claude Fable 5.1, Claude Mythos 5.1 | references/claude-models.md (lineup + effort; no dedicated prompting section yet — run the freshness check, step 2) |
| Claude Opus 5.5 | references/claude-models.md (lineup + effort; no dedicated prompting section yet — run the freshness check, step 2) |
| Claude Fable 5, Claude Mythos 5 (legacy) | references/claude-models.md |
| Claude Opus 5, 4.8, 4.7, 4.6 (legacy) | references/claude-models.md |
| Claude Opus 4.1 (retired Aug 5, 2026; do not target) | references/claude-models.md (migration notes only) |
| Claude Sonnet 5, Claude Sonnet 4.6 | references/claude-models.md |
| Claude Haiku 4.5 | references/claude-models.md |
| GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Luna | references/openai-models.md |
| GPT-5.x (GPT-5, GPT-5.1, GPT-5.2, GPT-5.3) | references/openai-models.md |
| gpt-5-codex | references/openai-models.md |
| Unknown or newer model | Run a freshness web search (see workflow step 2) |
What ships with it
9 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.
- agents/openai.yaml 122 B
- references/ai-gateway-harness.md 8.2 KB
- references/amp-harness.md 7.6 KB
- references/claude-code-harness.md 18 KB
- references/claude-models.md 32 KB
- references/codex-harness.md 15 KB
- references/eve-harness.md 7.0 KB
- references/openai-models.md 17 KB
- references/pi-harness.md 7.2 KB
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 Changed · +16 lines · +160 tokens per session 6b7d8e874614
- 1mo ago First seen · 114 lines · 48 tokens per session scan A 8a7ea3db7fbc
metaprompt is a skill published in the GitHub repository JairoTorregrosa/jaiskills (9 stars, last pushed 2d ago), licensed MIT. It adds 208 tokens to every session and 2,805 once invoked, about $0.0008 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 2026-08-31.
Other skills, from other repositories
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prompt-evaluation-runner
Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.
write-a-prompt
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