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 codebygarv/Ai-skills --skill prompt-enhancergit clone --depth 1 https://github.com/codebygarv/Ai-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/codebygarv/ai-skills/prompt-enhancer)<a href="https://agentmods.dev/skills/codebygarv/ai-skills/prompt-enhancer"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/prompt-enhancer/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/codebygarv/ai-skills/prompt-enhancer"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/prompt-enhancer.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 | $0.00024 | $0.00299 |
| Opus 5.5 | $0.00010 | $0.00120 |
| Sonnet 5.5 | $0.00005 | $0.00060 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
prompt-enhancer 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.
What it actually says
Purpose
Transform loose, ambiguous, or incomplete user requests into high-precision, constraint-driven prompts with explicit roles, context requirements, step-by-step reasoning directives, output schemas, and boundary rules.
When to Use
- When an initial prompt produces hallucinated, generic, or off-target AI responses.
- Writing reusable prompt templates or system instructions for AI agent workflows.
- Refining complex coding or architectural prompts before executing them.
What to Analyze
- Role & Persona: Establish precise expertise level (e.g. "Senior Rust Systems Engineer").
- Context & Inputs: Identify missing variables, code snippets, or environment specs.
- Explicit Constraints: Negative rules (what NOT to do) and boundary constraints.
- Step-by-Step Directives: Breaking multi-faceted tasks into ordered execution steps.
- Output Format & Schema: Defining the exact structure (JSON schema, markdown headings, code-only).
Output Format
- Enhanced Prompt: Ready-to-use structured prompt using standard tagging or markdown.
- Key Improvements Explained: Why each constraint was added.
- Recommended Variables: Placeholders for dynamic inputs.
Avoid
- Overloading prompts with conversational filler phrases.
- Omitting negative constraints (which lead to unwanted verbosity or disclaimers).
What ships with it
2 files 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 · 34 lines · 24 tokens per session scan A a5af742c73ff
prompt-enhancer is a skill published in the GitHub repository codebygarv/Ai-skills (27 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 299 once invoked, about $0.0001 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.
Other skills, from other repositories
ai-prompt-writing
Write effective prompts for AI coding tools — structure task context, specify constraints, provide examples, and iterate based on output quality. Targets developers using Claude Code, Copilot, Cursor, and similar tools.
prompt-engineering-guide
Design, test, and optimize LLM prompts systematically — with evaluation frameworks, chain-of-thought patterns, output formatting, and iteration methodology for reliable AI outputs.
cost-aware-llm-pipeline
A planning guide for choosing language models and managing the amount of conversation context used by an AI coding workflow. It groups tasks by complexity and gives rules for avoiding context overflow during long sessions.
llm-patterns
AI-first application patterns, LLM testing, prompt management.
prompt-engineering
Use when one prompt must give the same right answer across reruns, models, and pasted-in hostile input: forcing a fixed schema, picking the few-shot set, ordering the prompt blocks, or the inline cases you run while tuning. NOT the agent loop, tools, or retrieval (that is building-agents), NOT a standing CI eval…
prompt-engineering
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic…