remember
Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.
Best use case
remember is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.
Teams using remember should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/remember/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How remember Compares
| Feature / Agent | remember | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# /si: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
```
/si:remember <what to remember>
/si:remember "This project's CI requires Node 20 LTS — v22 breaks the build"
/si:remember "The /api/auth endpoint uses a custom JWT library, not passport"
/si: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
```bash
MEMORY_DIR="$HOME/.claude/projects/$(pwd | sed 's|/|%2F|g; s|%2F|/|; s|^/||')/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`:
```markdown
- {{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):
```
💡 This sounds like it could be a CLAUDE.md rule rather than a memory entry.
Rules are enforced with higher priority. Want to /si:promote it instead?
```
### Step 5: Confirm
```
✅ Saved to auto-memory
"{{entry}}"
MEMORY.md: {{n}}/200 lines
Claude will see this at the start of every session in this project.
```
## What NOT to use /si:remember for
- **Temporary context**: Use session memory or just tell Claude in conversation
- **Enforced rules**: Use `/si:promote` to write directly to CLAUDE.md
- **Cross-project knowledge**: Use `~/.claude/CLAUDE.md` for global rules
- **Sensitive data**: Never store credentials, tokens, or secrets in memory files
## Tips
- Be concise — one line beats a paragraph
- Include the concrete command or value, not just the concept
- ✅ "Build with `pnpm build`, tests with `pnpm test:e2e`"
- ❌ "The project uses pnpm for building and testing"
- If you're remembering the same thing twice, promote it to CLAUDE.mdRelated Skills
wiki-query
Query the LLM Wiki — reads index.md first, drills into 3-10 relevant pages, synthesizes an answer with inline [[wikilink]] citations, and offers to file the answer back as a new comparison or synthesis page. Usage /wiki-query "<question>"
wiki-log
Show recent entries from the LLM Wiki log (wiki/log.md). Uses the standardized
wiki-lint
Run a health check on the LLM Wiki vault — mechanical checks (orphans, broken links, stale pages, missing frontmatter, log gap, duplicates) plus semantic checks (contradictions, cross-reference gaps, concepts missing their own page). Outputs a markdown report with suggested actions. Usage /wiki-lint [--stale-days N] [--log-gap-days N]
wiki-init
Bootstrap a fresh LLM Wiki vault with the three-layer structure, schema files, and starter templates. Usage /wiki-init <path> --topic "<topic>" [--tool all|claude-code|codex|cursor|antigravity]
wiki-ingest
Ingest a source file from raw/ into the LLM Wiki — read, discuss, write summary page, update cross-references across 5-15 pages, regenerate index, append to log. Usage /wiki-ingest <path-to-source>
tc
Track technical changes with structured records, a state machine, and session handoff. Usage: /tc <init|create|update|status|resume|close|export|dashboard> [args]
tc-tracker
Use when the user asks to track technical changes, create change records, manage TC lifecycles, or hand off work between AI sessions. Covers init/create/update/status/resume/close/export workflows for structured code change documentation.
llm-wiki
Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
karpathy-coder
Use when writing, reviewing, or committing code to enforce Karpathy's 4 coding principles — surface assumptions before coding, keep it simple, make surgical changes, define verifiable goals. Triggers on "review my diff", "check complexity", "am I overcomplicating this", "karpathy check", "before I commit", or any code quality concern where the LLM might be overcoding.
karpathy-check
Run Karpathy's 4-principle review on staged changes or the last commit. Checks complexity, diff noise, hidden assumptions, and goal verification. Usage /karpathy-check [--last-commit]
cs-wiki-linter
Dispatched sub-agent that runs a periodic health check on an LLM Wiki vault. Runs mechanical checks via scripts (orphans, broken links, stale pages, missing frontmatter, duplicate titles, log gaps), does semantic checks (contradictions, stale claims, cross-reference gaps, concepts missing their own page), and produces a markdown report with suggested actions. Spawn weekly, after batch ingests, or when the user says "check the wiki" / "lint my wiki" / "audit the vault".
cs-wiki-librarian
Dispatched sub-agent that answers queries against an LLM Wiki vault. Reads index.md first, drills into 3-10 relevant pages across categories, synthesizes an answer with inline [[wikilink]] citations, and offers to file the answer back into the wiki as a new comparison or synthesis page. Spawn when the user asks a substantive question the wiki might answer, says "what does the wiki say about X", "compare A and B across my sources", or wants to explore a topic.