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 vahidkaargar/it-department-skills --skill remembergit clone --depth 1 https://github.com/vahidkaargar/it-department-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/vahidkaargar/it-department-skills/remember)<a href="https://agentmods.dev/skills/vahidkaargar/it-department-skills/remember"><img src="https://agentmods.dev/badge/skills/vahidkaargar/it-department-skills/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/vahidkaargar/it-department-skills/remember"><img src="https://agentmods.dev/badge/skills/vahidkaargar/it-department-skills/remember.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 | $0.00029 | $0.00801 |
| Opus 5.5 | $0.00012 | $0.00320 |
| Sonnet 5.5 | $0.00006 | $0.00160 |
| Haiku 4.5 | $0.00003 | $0.00080 |
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 2mo 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.
This is a copy
92% identical to remember — 31 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
remember — Save Knowledge Explicitly
Writes an explicit entry to auto-memory when something is important enough that you don't want to rely on Claude noticing it automatically.
Usage
/remember <what to remember>
/remember "This project's CI requires Node 20 LTS — v22 breaks the build"
/remember "The /api/auth endpoint uses a custom JWT library, not passport"
/remember "Reza prefers explicit error handling over try-catch-all patterns"
When to Use
| Situation | Example |
|---|---|
| Hard-won debugging insight | "CORS errors on /api/upload are caused by the CDN, not the backend" |
| Project convention not in CLAUDE.md | "We use barrel exports in src/components/" |
| Tool-specific gotcha | "Jest needs --forceExit flag or it hangs on DB tests" |
| Architecture decision | "We chose Drizzle over Prisma for type-safe SQL" |
| Preference you want Claude to learn | "Don't add comments explaining obvious code" |
Workflow
Step 1: Parse the knowledge
Extract from the user's input:
- What: The concrete fact or pattern
- Why it matters: Context (if provided)
- Scope: Project-specific or global?
Step 2: Check for duplicates
Use the auto-memory directory Claude Code announces in its system prompt (the "Memory" section names the exact path). Fallback if unknown:
MEMORY_DIR="$HOME/.claude/projects/$(pwd | tr '/.' '--')/memory"
grep -ni "<keywords>" "$MEMORY_DIR/MEMORY.md" 2>/dev/null
If a similar entry exists:
- Show it to the user
- Ask: "Update the existing entry or add a new one?"
Step 3: Write to MEMORY.md
Append to the end of MEMORY.md:
- {{concise fact or pattern}}
Keep entries concise — one line when possible. Auto-memory entries don't need timestamps, IDs, or metadata. They're notes, not database records.
If MEMORY.md is over 180 lines, warn the user:
⚠️ MEMORY.md is at {{n}}/200 lines. Consider running /si:review to free space.
Step 4: Suggest promotion
If the knowledge sounds like a rule (imperative, always/never, convention):
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.
- 2mo ago First seen · 102 lines · 29 tokens per session scan A a6c88158a9d7
remember is a skill published in the GitHub repository vahidkaargar/it-department-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 29 tokens to every session and 801 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to remember, differing in 31 lines, and is treated as a copy.
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.
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.
nano-memory
Guidelines and workflows for the agent to maintain persistent memory using native file tools (readfile, writefile, editfile) and standard OS commands for searching.
handoff
Compact the current conversation into a handoff document for a fresh agent.