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/llaa33219/casabot/memorynpx skills add llaa33219/casabot --skill memorygit clone --depth 1 https://github.com/llaa33219/casabotWrote 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/llaa33219/casabot/memory)<a href="https://agentmods.dev/skills/llaa33219/casabot/memory"><img src="https://agentmods.dev/badge/skills/llaa33219/casabot/memory.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.01455 |
| Opus 5 | $0.00007 | $0.00727 |
| Sonnet 5 | $0.00003 | $0.00291 |
| Haiku 4.5 | $0.00001 | $0.00145 |
Grade A, and why
Memory 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 3d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory
This manual explains the difference between History and Memory, and guides agents on how to write and query memos.
1. Difference between History and Memory
CasAbot has two storage systems. Make sure to use them correctly.
History — Conversation Logs
| Item | Description |
|---|---|
| Path | ~/casabot/history/ |
| Format | JSON |
| Author | System (auto-saved) |
| Editable | ❌ Read-only |
| Purpose | Raw logs of entire conversations |
| Contents | User input, agent responses, tool call results, etc. |
Memory — Agent Memos
| Item | Description |
|---|---|
| Path | ~/casabot/memory/ |
| Format | Markdown (.md) |
| Author | Agents (written directly) |
| Editable | ✅ Freely editable |
| Purpose | Record important information, learning outcomes, analysis results, etc. |
| Contents | Things agents need to remember |
Core Principles
- Never modify History. It is preserved as the original conversation log.
- Memory is freely written, modified, and deleted by agents. It is a space for organizing important information.
- When important information comes up in conversation, record it in Memory.
2. Memory File Location
~/casabot/memory/
├── 2024-01-15-project-analysis.md
├── 2024-01-16-server-setup.md
├── user-preferences.md
├── frequently-used-commands.md
└── ...
3. Writing Rules
Filename Rules
- With date (recommended):
YYYY-MM-DD-topic.md— Record at a specific point in time - Without date:
topic.md— Continuously updated record - Use alphanumeric characters and hyphens (
-) - Use hyphens instead of spaces
File Format
All memory files are written in Markdown (.md) format.
Recommended Content Structure
# Title
- **Author**: agent name
- **Date**: YYYY-MM-DD
- **Tags**: #keyword1 #keyword2
## Summary
One or two line summary of key content
## Details
Detailed content...
## Related Items
- Related memory filenames
- Related conversation IDs
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.
- 3d ago First seen · 246 lines · 14 tokens per session scan A ca89f6ab6d59
Memory is a skill published in the GitHub repository llaa33219/casabot (5 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 1,455 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 2026-08-31.
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…