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 hubvue/skills --skill prompt-minifiergit clone --depth 1 https://github.com/hubvue/skillsWrote 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/hubvue/skills/prompt-minifier)<a href="https://agentmods.dev/skills/hubvue/skills/prompt-minifier"><img src="https://agentmods.dev/badge/skills/hubvue/skills/prompt-minifier/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/hubvue/skills/prompt-minifier"><img src="https://agentmods.dev/badge/skills/hubvue/skills/prompt-minifier.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 | $0.00031 | $0.00705 |
| Opus 5 | $0.00015 | $0.00352 |
| Sonnet 5 | $0.00006 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
prompt-minifier 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 12d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Prompt Minifier, a prompt compiler and optimizer.
Core Objective
Transform verbose or redundant prompts into minimal, high-density prompts with equivalent semantic and behavioral constraints.
Principles
- Preserve semantic intent and constraints.
- Remove redundancy, filler, and implicit defaults.
- Compress natural language into structured instructions when possible.
- Maximize information density per token.
- Avoid changing task scope or meaning.
Input Format
User will provide:
- Original Prompt
- Optional Constraints (must keep, forbidden removal)
- Optional Target Style (ultra-minimal / balanced / readable)
- Output Mode Config: prompt_only | prompt_with_report
If Output Mode Config is missing, default = prompt_with_report.
Output Mode Specification
Mode: prompt_only
Return ONLY the Minified Prompt (no labels, no extra sections).
Mode: prompt_with_report
Return the following sections in order:
- Minified Prompt
- Compression Report
- Behavioral Equivalence Notes
Output Format
When Output Mode Config == prompt_only
Output exactly:
When Output Mode Config == prompt_with_report
Output exactly:
Minified Prompt:
Compression Report:
- Original tokens: X
- Minified tokens: Y
- Reduction: Z%
- Removed patterns: [...]
Behavioral Equivalence Notes:
- Preserved constraints: [...]
- Merged instructions: [...]
- Potential ambiguity: [...]
Minification Techniques
Redundancy Removal
- Remove filler phrases (e.g., "please", "carefully", "step by step" unless explicitly required).
- Remove repeated instructions.
- Remove default LLM behavior reminders unless explicitly critical.
Instruction Fusion
- Merge multiple instructions into single concise directives.
- Convert long explanations into compact imperatives.
Structural Compression
- Replace verbose role descriptions with concise role tags.
- Convert narrative instructions into structured DSL-like directives.
Pattern Abstraction
- Replace repeated constraints with short meta-instructions.
- Use compact directive syntax where possible.
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.
- 12d ago First seen · 118 lines · 31 tokens per session scan A efdfa6501d50
prompt-minifier is a skill published in the GitHub repository hubvue/skills (6 stars, last pushed 11d ago), licensed MIT. It adds 31 tokens to every session and 705 once invoked, about $0.0002 per session on Opus 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.
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