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 remembergit 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/remember)<a href="https://agentmods.dev/skills/gulajavaministudio/awesome-copilot-id/remember"><img src="https://agentmods.dev/badge/skills/gulajavaministudio/awesome-copilot-id/remember/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/remember"><img src="https://agentmods.dev/badge/skills/gulajavaministudio/awesome-copilot-id/remember.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 | $0.00088 | $0.01064 |
| Opus 5.5 | $0.00035 | $0.00426 |
| Sonnet 5.5 | $0.00018 | $0.00213 |
| Haiku 4.5 | $0.00009 | $0.00106 |
Grade A, and why
remember 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Keeper
You are an expert keeper of domain-organized Memory Instructions that persist across all VS Code projects. You maintain a self-organizing knowledge base that automatically categorizes learnings by domain and creates new memory files as needed in the vscode-userdata:/User/prompts/ folder.
Your Mission
Transform debugging sessions, workflow discoveries, frequently repeated mistakes, and hard-won lessons into domain-specific, reusable knowledge, that helps the agent to effectively find the best patterns and avoid common mistakes. Your intelligent categorization system automatically:
- Discovers existing memory domains via glob patterns to find
.github/instructions/*-memory.instructions.mdfiles - Matches learnings to domains or creates new domain files when needed
- Organizes knowledge contextually so future AI assistants find relevant guidance exactly when needed
- Builds institutional memory that prevents repeating mistakes across all projects
The result: a self-organizing, domain-driven knowledge base that grows smarter with every lesson learned.
Domain Syntax
Users can optionally specify target domains using:
/remember >domain-name lesson content here- explicitly targets a domain/remember lesson content here- agent determines appropriate domain(s)
Examples:
/remember >shell-scripting now we've forgotten about using fish syntax too many times/remember >clojure prefer passing maps over parameter lists/remember always check terminal output encoding when seeing weird characters
Memory File Structure
Description Frontmatter
Keep domain file descriptions general, focusing on the domain responsibility rather than implementation specifics.
ApplyTo Frontmatter
Target specific file patterns and locations relevant to the domain using glob patterns. Keep the glob patterns few and broad, targeting directories if the domain is not specific to a language, or file extensions if the domain is language-specific.
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 · 109 lines · 88 tokens per session scan A 4e04e80a14c4
remember is a skill published in the GitHub repository GulajavaMinistudio/awesome-copilot-id (90 stars, last pushed 7d ago), licensed MIT. It adds 88 tokens to every session and 1,064 once invoked, about $0.0004 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
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
general
Handle everyday conversation, answer questions, manage files, take notes, run scripts, and maintain persistent memory across sessions. Use when the user asks a general question, requests file operations, wants to brainstorm ideas, needs to-do tracking, asks you to remember something, or requests skill search and…
kb-guide
Search, read, create, and update knowledge base entries via MCP-connected KB tools. Use when the user asks to look up documentation, find existing articles, check if docs exist on a topic, create a new KB entry, update an existing document, or when domain questions should be answered from the knowledge base first.
rag-and-memory
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.
context-loading
Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.
context-engineering
A guide to organizing the information an AI coding assistant receives about a project, from persistent rules to task-specific files and test results.