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 agentmods add skills/vudovn/ag-kit/memory-systemnpx skills add vudovn/ag-kit --skill memory-systemgit clone --depth 1 https://github.com/vudovn/ag-kitWhat 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 | $0.00035 | $0.01468 |
| Opus 5 | $0.00017 | $0.00734 |
| Sonnet 5 | $0.00007 | $0.00294 |
| Haiku 4.5 | $0.00003 | $0.00147 |
Grade A, and why
memory-system 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 2d 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory System — Persistent Cross-Session Memory
Enables agents to remember across sessions. Never re-discover what was already learned.
Overview
The Memory System provides persistent, searchable memory that survives across sessions. Instead of re-explaining preferences, conventions, and past decisions every time, agents read a structured MEMORY.md index and topic files.
Token Impact: +1,000 tokens to load index, but saves 3,000-10,000 tokens by eliminating re-discovery.
Architecture
.agents/memory/
├── MEMORY.md ← Lightweight index (max 200 lines)
├── user-preferences.md ← Topic file: user role, style, tools
├── project-conventions.md ← Topic file: coding standards, patterns
├── tech-decisions.md ← Topic file: past architectural decisions
├── feedback-history.md ← Topic file: what user liked/disliked
└── [topic-name].md ← Additional topic files as needed
MEMORY.md Index Format
The index is a lightweight pointer file — short entries that reference topic files for details.
Rules:
- Maximum 200 lines total
- Each entry: ~150 characters max
- Format:
- [type] summary → topic-file.md - Types:
[user][feedback][project][reference]
Example:
# Memory Index
## User
- [user] Prefers dark mode, uses Windows 11, PowerShell → user-preferences.md
- [user] Senior DevOps engineer, 8 years experience → user-preferences.md
- [user] Primary language: English, sometimes Turkish → user-preferences.md
## Project
- [project] Always use bun instead of npm → project-conventions.md
- [project] Tailwind v4 preferred, no v3 → tech-decisions.md
- [project] No purple/violet colors in UI → project-conventions.md
## Feedback
- [feedback] User likes concise responses, no filler → feedback-history.md
- [feedback] User dislikes verbose explanations → feedback-history.md
- [feedback] User prefers tables over bullet lists → feedback-history.md
## Reference
- [reference] Squid proxy runs on port 3128 → infrastructure-notes.md
- [reference] Git workflow: feature branches → main → project-conventions.md
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.
- 2d ago First seen · 183 lines · 35 tokens per session scan A 40ffe215156b
memory-system is a skill published in the GitHub repository vudovn/ag-kit (8,162 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 1,468 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…