# Metaprompt > Given a goal, a target model, and a target harness, produces a complete, ready-to-use prompt engineered for that specific combination — researching current per-model and per-harness prompting techniques rather than relying on stale memorized guidance, then self-reviewing the draft against a quality checklist. Use when writing a prompt, system message, or agent instructions meant to run on a specific model (Claude, GPT-5.x, Gemini, etc.) inside a specific harness (Claude Code, Codex CLI, a custom agent loop, raw API, chat) — especially one that isn't the model/harness currently running this conversation. - Skill: `jsvillalbat/metaprompt` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add jsvillalbat/metaprompt` - Raw SKILL.md: https://api.skillmd.com/api/skills/jsvillalbat/metaprompt/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: jsvillalbat (https://skillmd.com/u/jsvillalbat) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/jsvillalbat/metaprompt --- # Metaprompt A prompt written by feel, without checking what actually works for the specific model and harness that will run it, tends to either under-specify (missing a technique that model responds well to) or over-specify (re-stating things the harness already guarantees, which dilutes attention). This skill treats prompt-writing as a small research task with three required inputs, not a one-shot creative exercise. ## When to use this - Writing a prompt, system message, or agent instructions meant to run on a specific model inside a specific harness — most valuably when that combination differs from the one currently running this conversation (you can't rely on your own default behavior as a stand-in for how the target will behave). - The user asks to "write a prompt for X model" or "write the system prompt for this agent" and names or implies a target model/harness. ## Instructions ### 1. Get all three required inputs - **Goal** — what should the resulting prompt make the model do? - **Target model** — the specific model/family (e.g. "Claude Opus 4.8," "GPT-5.2," "Gemini 3 Pro"). Techniques differ meaningfully even within one vendor's lineup — don't accept "an LLM" as an answer. - **Target harness** — what will actually run this prompt: Claude Code, Codex CLI, a custom agent loop, the raw API, a chat UI, something else. This determines what the harness already handles for you vs. what the prompt must spell out. If any is missing or ambiguous, ask before drafting — a prompt optimized for the wrong model/harness combination isn't a safe default to fall back on. ### 2. Research before writing — don't rely on memorized specifics Model and harness idioms change as often as the models themselves ship. Before drafting: - Look up the target model's **current** official prompting guidance (WebSearch/ WebFetch its vendor's docs). If a skill already available in this environment is specifically authoritative for that model/vendor (for example this repo's own `claude-api` skill for Claude/Anthropic), prefer it over general web search. - Look up the target harness's own conventions and docs (a harness-specific instructions-file format, its tool-calling conventions, what context it injects automatically). - Treat anything you already "know" about a model's quirks as a hypothesis to verify, not a fact to assert — this is exactly the staleness this skill exists to avoid. `references/research-checklist.md` lists the categories to investigate for the model and the harness — deliberately not a list of hardcoded facts, since those go stale. ### 3. Apply what you found - **Model-specific**: structuring conventions the model responds to (XML tags, markdown, JSON), how it handles extended/visible reasoning, how literally it follows instructions, anything relevant to the goal around refusals/safety framing. - **Harness-specific**: what the harness already provides automatically (tool definitions, cwd/git context, sandboxing, available tools) — don't re-specify it, that's redundant and dilutes the prompt. What output format the harness expects and parses. Where the harness's own extension points are (system prompt vs. user message vs. a dedicated config/instructions file). **The harness guide is the authority on what to omit, not just what to add.** ### 4. Assemble, then self-review before delivering Draft the complete prompt, then check it against: - Is the goal unambiguous to someone with zero other context? - Are format instructions precise and actually match what the harness parses? - Does it redundantly restate anything the harness already guarantees? - Any internal contradictions between sections? - Is it as concise as the goal allows — a bloated prompt degrades attention as much as a vague one does? Full checklist in `references/research-checklist.md`. ### 5. Deliver three things 1. The ready-to-paste prompt, in a code block. 2. Parameter recommendations if applicable (reasoning/thinking effort, temperature, and similar) for the target model. 3. One sentence naming the key model- or harness-specific technique that shaped this prompt — so the user understands *why* it's written this way, not just what to paste. ## Reference files - `references/research-checklist.md` — what to look up per model and per harness, and the pre-delivery self-review checklist.