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 refine-promptgit 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/refine-prompt)<a href="https://agentmods.dev/skills/iliaal/whetstone/refine-prompt"><img src="https://agentmods.dev/badge/skills/iliaal/whetstone/refine-prompt/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/refine-prompt"><img src="https://agentmods.dev/badge/skills/iliaal/whetstone/refine-prompt.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 | $0.00062 | $0.00600 |
| Opus 5.5 | $0.00025 | $0.00240 |
| Sonnet 5.5 | $0.00012 | $0.00120 |
| Haiku 4.5 | $0.00006 | $0.00060 |
Grade A, and why
refine-prompt 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Refining Prompts
Process
- Assess — Identify what the prompt is missing:
| Element | Check |
|---|---|
| Task | Is the core action explicit and unambiguous? |
| Constraints | Are length, format, tone, and scope defined? |
| Output format | Does it specify the expected structure? |
| Context | Does the model have enough background to act? |
| Examples | Would a demonstration clarify the expected output? |
| Edge cases | Are failure modes and boundary conditions addressed? |
-
Rewrite — Transform into specification language: precise, imperative, no filler. Treat the prompt as a spec, not conversation.
-
Validate — Check the rewrite against the assessment table. Every gap identified in step 1 must be addressed.
Rules
- Length: 0.75x–1.5x the original. Conciseness is a feature — add only what's missing, cut what's vague.
- Never invent — only use information present in the original prompt or conversation context. If critical info is missing, ask instead of assuming.
- Instruction hierarchy — order sections by priority: task → constraints → examples → input data → output format. Place the most important instruction first.
- Progressive complexity — start with the simplest prompt that could work. Add few-shot examples, chain-of-thought, or role framing only when the task demands it, not by default.
- Specific verbs — replace vague actions ("analyze", "process", "handle") with measurable ones ("list the top 3", "classify as A/B/C", "return JSON with keys X, Y").
- One output format — specify exactly one format (JSON schema, markdown template, numbered list). Ambiguous format expectations cause inconsistent results.
- No meta-commentary — output only the refined prompt as markdown. No preamble ("Here's an improved version..."), no explanation of changes unless explicitly requested.
Anti-Patterns
| Problem | Fix |
|---|---|
| Vague verbs ("look into", "deal with") | Replace with concrete actions ("list", "compare", "extract") |
| Missing output spec | Add explicit format section with example structure |
| Examples contradict instructions | Align examples to match every stated rule |
| Over-engineered from the start | Strip to simplest working version, then add complexity only where output quality requires it |
| Prompt exceeds context with examples | Limit to 2–3 diverse examples; use one simple, one edge case |
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 · 48 lines · 62 tokens per session scan A 90212f93f9c5
refine-prompt is a skill published in the GitHub repository iliaal/whetstone (35 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 600 once invoked, about $0.0002 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.
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