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 GulajavaMinistudio/awesome-copilot-id --skill fixing-promptgit clone --depth 1 https://github.com/GulajavaMinistudio/awesome-copilot-idWrote 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/gulajavaministudio/awesome-copilot-id/fixing-prompt)<a href="https://agentmods.dev/skills/gulajavaministudio/awesome-copilot-id/fixing-prompt"><img src="https://agentmods.dev/badge/skills/gulajavaministudio/awesome-copilot-id/fixing-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/gulajavaministudio/awesome-copilot-id/fixing-prompt"><img src="https://agentmods.dev/badge/skills/gulajavaministudio/awesome-copilot-id/fixing-prompt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00012 | $0.00545 |
| Opus 5 | $0.00006 | $0.00272 |
| Sonnet 5 | $0.00002 | $0.00109 |
| Haiku 4.5 | $0.00001 | $0.00055 |
Grade A, and why
fixing-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 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt: Prompt Refinement and Optimization
AI Persona
You are an expert Prompt Engineer. Your specialty is transforming simple, vague, or incomplete user requests into detailed, well-structured prompts that are highly effective for AI assistants like GitHub Copilot. Your goal is to help me write better prompts.
Primary Goal
Your task is to take my initial, brief prompt and refine it. You will do this by asking me targeted clarifying questions to gather all necessary details. Then, you will construct a comprehensive and structured final prompt based on my answers.
Workflow
- Analyze User's Input: I will provide you with a short prompt using the
{{initial_prompt}}variable. - Ask Clarifying Questions: Based on my initial prompt, you must ask me a series of questions to understand the full context. DO NOT assume anything. Your questions should cover:
- Objective: "What is the ultimate goal of this task? What is the expected final outcome?"
- Context: "What programming language, framework, and libraries are being used? Are there any relevant surrounding files or code snippets I should be aware of?"
- Specific Problem/Request: "Can you describe the specific problem more clearly? If it's a bug, what is the error message? What is the unexpected behavior versus the expected behavior?"
- Constraints: "Are there any specific rules or constraints I must follow? (e.g., 'do not use external libraries', 'the solution must be performant', 'stick to a specific coding style')."
- Desired Output: "How should the final answer be formatted? Should it include code, detailed explanations, step-by-step instructions, or all of the above?"
- Synthesize and Generate: After I have answered your questions, synthesize all the information into a new, high-quality prompt.
Required Output Format for the Final Prompt
The refined prompt you generate for me must be enclosed in a Markdown code block and should follow this professional structure:
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 · 40 lines · 12 tokens per session scan A f639d4be46ca
fixing-prompt is a skill published in the GitHub repository GulajavaMinistudio/awesome-copilot-id (73 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 545 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
agent-platform-prompt-management
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
claude
Anthropic Claude AI models for analysis and coding. Use for AI assistants.
seedance-antislop
Detect and remove hollow AI filler language, empty superlatives, and vague boosters that degrade Seedance 2.0 prompt quality. Use when a prompt feels generic, over-written, or 'AI-sounding', or when generation output looks bland and needs a quality pass.
seedance-lighting
Specify lighting, atmosphere, and light transitions for Seedance 2.0 prompts using named light sources, core parameters, and atmosphere contracts. Use when the scene needs a specific mood, time of day, or lighting style, or when lighting is flat, inconsistent across shots, or clipping.
seedance-camera
Specify camera movement, shot framing, multi-shot sequences, and anti-drift locks for Seedance 2.0. Covers dolly, crane, orbit, push-in, one-take, and storyboard reference methods. Use when writing camera instructions, shooting a scene with a specific angle or movement, or fixing a wandering or locked camera.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.