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.
git 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/commands/ackeskin/contexture/improve-prompt)<a href="https://agentmods.dev/commands/ackeskin/contexture/improve-prompt"><img src="https://agentmods.dev/badge/commands/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/commands/ackeskin/contexture/improve-prompt"><img src="https://agentmods.dev/badge/commands/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 | $0.00039 | $0.00261 |
| Opus 5.5 | $0.00016 | $0.00104 |
| Sonnet 5.5 | $0.00008 | $0.00052 |
| Haiku 4.5 | $0.00004 | $0.00026 |
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 today.
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
Run the improve-prompt skill for the current context.
Any text after /improve-prompt is treated as the prompt to improve. Examples:
/improve-prompt— improve the prompt in the recent conversation / current selection; ask what to improve if ambiguous./improve-prompt write about our new feature— sharpen that text prompt./improve-prompt a nice picture of a city— treat as a generative-image prompt and improve it for any image model.
The skill classifies the prompt family (text/LLM vs generative media), diagnoses what's missing against the relevant dimensions, interviews the user only when a gap would materially change the output, then returns an improved prompt plus a short rationale (what changed, assumptions, optional per-model notes). Model-agnostic — vendor-specific syntax stays in the optional notes. Mode A only — never auto-fires.
See ~/.claude/skills/improve-prompt/SKILL.md for the full procedure.
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.
- today Changed · +1 lines e98004d0508e
- 1mo ago First seen · 16 lines · 39 tokens per session scan A 63b3dd466701
improve-prompt is a command published in the GitHub repository AcKeskin/contexture (2 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 261 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 2026-08-31.
Other commands, from other repositories
batch-operations-prompt
Optimize prompts for multiple file operations, parallel processing, and efficient bulk changes across a codebase. This helps Claude Code work more efficiently with TodoWrite patterns.
devkit.prompt-optimize
Provides expert prompt optimization using advanced techniques (CoT, few-shot, constitutional AI) for LLM performance enhancement. Use when you need to improve prompt quality or optimize LLM interactions.
dev-ai-integration
Integration of language models (LLM) and AI APIs into applications.
prompt-best-practices
Best practices for designing effective modular prompts that achieve specific outcomes.
evaluate
Score the prompt I just wrote and give me one thing to sharpen.
prompt-optimize
Legacy slash-entry shim for the prompt-optimizer skill. Prefer the skill directly.