metaprompt
Skill0 starsChanged 3 months agoMITView on GitHub
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.
1 other project uses a version of this.
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Most recently taken up by
- JairoTorregrosa/jaiskillstheir own version, first seen Oct 25, 2026
Other versions1 project uses a version of this.
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Wrote their own version · 1
- JairoTorregrosa/jaiskills★ 11same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository8 other files it gives its agents.
- advisorSkill
- codex-judgeSkill
- compound-knowledgeSkill
- file-todosSkill
- goal-loopSkill
- remote-agentsSkill
- skill-creatorSkill
- verify-before-doneSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 38
If any is missing or ambiguous, ask before drafting — a prompt optimized for the wrong model/harness combination isn't a safe default to …
- Distinctive: line 46
Look up the target model's current official prompting guidance (WebSearch/ WebFetch its vendor's docs).
- Distinctive: line 50
Look up the target harness's own conventions and docs (a harness-specific instructions-file format, its tool-calling conventions, what …
- Distinctive: line 53
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 …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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-apiskill 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
- The ready-to-paste prompt, in a code block.
- Parameter recommendations if applicable (reasoning/thinking effort, temperature, and similar) for the target model.
- 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.
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