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 iliaal/whetstone --skill meta-promptinggit clone --depth 1 https://github.com/iliaal/whetstoneWrote 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/iliaal/whetstone/meta-prompting)<a href="https://agentmods.dev/skills/iliaal/whetstone/meta-prompting"><img src="https://agentmods.dev/badge/skills/iliaal/whetstone/meta-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/iliaal/whetstone/meta-prompting"><img src="https://agentmods.dev/badge/skills/iliaal/whetstone/meta-prompting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00104 | $0.00948 |
| Opus 5.5 | $0.00042 | $0.00379 |
| Sonnet 5.5 | $0.00021 | $0.00190 |
| Haiku 4.5 | $0.00010 | $0.00095 |
Grade A, and why
meta-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 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta-Prompting
Enhanced reasoning via /commands or natural language. Commands combine left-to-right: /verify /adversarial. Auto-trigger when context warrants — note which pattern applied.
Patterns
/think | /show — Show reasoning step-by-step: decision points, alternatives considered, why each accepted/rejected. With /think doubt: after each step, flag what could be wrong and why before proceeding.
/adversarial | /argue — After answering, argue against it. 3 strongest counterarguments ranked by severity. Identify blind spots and unstated assumptions.
/constrain | /strict — Tight constraints: 3 sentences max, cite sources, no hedging. Override inline: /constrain 5 sentences.
/json | /format — Respond in valid JSON code block, no surrounding prose unless asked. Default schema:
{"analysis": "", "confidence_score": 0-100, "methodology": "", "limitations": []}
Custom keys: /json {keys: summary, risks, recommendation}
/budget | /deep — Extended thinking space (~500 words) showing dead ends and reasoning pivots, then clearly separated final answer.
/compare | /vs — Compare options as table. Default dimensions: speed, accuracy, cost, complexity, maintenance. Custom: /compare [dim1, dim2].
/confidence | /conf — Rate each claim 0-100. Flag below 70 as SPECULATIVE. Group by tier: HIGH (85+), MEDIUM (70-84), LOW (<70). Include assumptions made and rate each 1-10 on confidence.
/edge | /break — 5+ inputs/scenarios that break the approach. Code: null/empty, concurrency, overflow, encoding, auth bypass. Strategies: market conditions, timing, dependencies.
Auto-triggers on: security, validation, parsing contexts.
/verify | /check — Three phases: (1) Answer direct response, (2) Challenge 3 ways it could be wrong, (3) Verify investigate each, update if needed. Mark final as VERIFIED ANSWER: or REVISED ANSWER:.
Auto-triggers on: architecture decisions, critical choices, "Am I right?"
What ships with it
1 file 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.
- 1mo ago First seen · 62 lines · 104 tokens per session scan A 842acf0a0078
meta-prompting is a skill published in the GitHub repository iliaal/whetstone (35 stars, last pushed today), licensed MIT. It adds 104 tokens to every session and 948 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
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.
gemini-prompting
Internal guidance for composing Gemini and AGY prompts for coding, review, diagnosis, and research tasks inside the Gemini Claude Code plugin.
model-sovereignty
This skill should be used when the user asks about "local models", "custom models", "fine-tuning", "self-hosting models", "model selection", "which model should I use", "data privacy and models", "LoRA", "RAG vs fine-tuning", "Ollama", "vLLM", or wants guidance on whether to build, host, or customise their own AI…
grok-prompting
Internal guidance for composing sharp Grok prompts for consultation and review inside the Grok plugin.
prompt-optimizer
Optimize prompts for Claude's latest models (Claude 5 family — Opus 5, Sonnet 5, Fable 5 — plus the still-active Opus 4.8, Sonnet 4.6, and Haiku 4.5) using Anthropic's official prompt-engineering guidance. Use when users want to improve, refine, debug, or create prompts or system prompts for Claude. Triggers include…
grok-prompting
Internal guidance for composing Grok prompts for coding, review, diagnosis, and research tasks inside the Grok Claude Code plugin.