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 AcKeskin/contexture --skill improve-promptgit clone --depth 1 https://github.com/AcKeskin/contextureWrote 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/ackeskin/contexture/improve-prompt)<a href="https://agentmods.dev/skills/ackeskin/contexture/improve-prompt"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/improve-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/ackeskin/contexture/improve-prompt"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/improve-prompt.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 | $0.00113 | $0.02434 |
| Opus 5.5 | $0.00045 | $0.00974 |
| Sonnet 5.5 | $0.00023 | $0.00487 |
| Haiku 4.5 | $0.00011 | $0.00243 |
Grade A, and why
improve-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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve Prompt
Overview
Turn a vague or under-specified prompt into one that reliably produces the output the user actually wants — for any target model, not just Claude. The skill is model-agnostic: it uses techniques that hold across vendors (OpenAI, Google, Meta, Mistral, Midjourney, Stable Diffusion, Sora, Veo, ElevenLabs, etc.) and only mentions a vendor when a technique is genuinely vendor-specific, flagged as optional.
Core principle: A good prompt removes the model's need to guess. Most weak prompts fail because they leave the task, the context, the output shape, or the success criteria implicit. The job is to make those explicit without bloating the prompt.
This is a collaborator, not an auto-rewriter. Never auto-fire. Never silently rewrite. When the prompt is underspecified, ask before guessing — see The Iron Rule.
When to Use
- User types
/improve-prompt. - User pastes a prompt (or describes one) and asks to improve / sharpen / fix / "make it better" / "why isn't this working".
- User wants help phrasing a request to any AI model — text, image, video, or audio.
Don't use for: writing a prompt from a blank slate where the user hasn't said what they want at all (interview them first about the goal), or for non-prompt copy-editing.
The Iron Rule
DO NOT GUESS AT MISSING INTENT. ASK, THEN REWRITE.
If the prompt is missing something that materially changes the output — the audience, the format, the constraints, the target model's medium — you ask before rewriting. Inventing those details produces a confident rewrite that solves the wrong problem. The exception: if the gap has an obvious, low-risk default, fill it and state the assumption in the rationale so the user can correct it.
Workflow
1. Classify → text/LLM prompt or generative (image/video/audio)?
2. Diagnose → score against the relevant dimensions; list what's missing
3. Decide → gaps that change the output → interview; trivial gaps → assume + flag
4. Rewrite → produce the improved prompt
5. Explain → short rationale: what changed, why, and any assumptions made
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 · 180 lines · 113 tokens per session scan A 728ed13cc2f6
improve-prompt is a skill published in the GitHub repository AcKeskin/contexture (2 stars, last pushed 2mo ago), licensed MIT. It adds 113 tokens to every session and 2,434 once invoked, about $0.0005 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 2026-09-03.
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