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 JHostalek/dotclaude --skill query-refinergit clone --depth 1 https://github.com/JHostalek/dotclaudeWrote 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/jhostalek/dotclaude/query-refiner)<a href="https://agentmods.dev/skills/jhostalek/dotclaude/query-refiner"><img src="https://agentmods.dev/badge/skills/jhostalek/dotclaude/query-refiner/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/jhostalek/dotclaude/query-refiner"><img src="https://agentmods.dev/badge/skills/jhostalek/dotclaude/query-refiner.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 | $0.00038 | $0.00410 |
| Opus 5.5 | $0.00015 | $0.00164 |
| Sonnet 5.5 | $0.00008 | $0.00082 |
| Haiku 4.5 | $0.00004 | $0.00041 |
Grade A, and why
query-refiner 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 9d 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.
What it actually says
Query Refiner
Rewrite the user's rough input in expert register to elicit a stronger LLM response. Reformulate only; never answer, execute, or comment on it.
Expert register recruits the target model's domain priors. Write as a practitioner would: use the field's terms of art, name the underlying concepts, make implicit assumptions and constraints explicit, and render in fluent, idiomatic English. The result should read as if an expert wrote it, even when the user is not one.
- Terms of art, not jargon: "optimize runtime complexity" is register; "leverage synergistic paradigms" is noise. No hollow intensifiers ("comprehensive", "holistic", "actionable insights", "robust").
- Fidelity: translate, don't reinterpret. Keep intent, scope, deliverable, and task type (a request to write stays a request to write, not to outline).
- Preserve verbatim: names, project context, affect and uncertainty ("I'm stuck"), and explicit constraints ("not X", "without Y"). Never genericize.
- Minimal edit: expand only where it adds clarity; leave simple inputs short. Add no response structure (sections, frameworks, templates) the user didn't request.
By input type:
- Question: tighten scope and terminology.
- Task (write, build, analyze, design): sharpen the deliverable's parameters and name the domain concepts; leave open what the user left open.
- Strategic or exploratory: sharpen the problem statement; keep the ask open-ended. Depth, not frameworks or checklists.
- Creative: tighten the brief in the craft's vocabulary (literary, design, musical); leave creative choices to the model.
Output only the refined prompt: no preamble, quotes, or explanation.
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.
- 9d ago First seen · 24 lines · 38 tokens per session scan A b3472e64480e
query-refiner is a skill published in the GitHub repository JHostalek/dotclaude (11 stars, last pushed 3d ago), licensed CC0-1.0. It adds 38 tokens to every session and 410 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 2026-10-16.
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