remember

A command for saving a useful fix, reusable approach, decision, or warning to persistent project memory. It asks for the relevant details and adds searchable tags.

In plain words
What is it for?
Use it to record solved errors, proven patterns, architecture choices, and pitfalls for future work.
Why use it?
It keeps lessons and decisions available after the current conversation instead of requiring the same discovery again.

Command

Part of the claude-code-starter-system plugin — 1 skill, 4 commands shipped together

Install

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.

agentmods
npx agentmods add commands/primeline-ai/claude-code-starter-system/remember
Clone the repo
git clone --depth 1 https://github.com/primeline-ai/claude-code-starter-system

Or install claude-code-starter-system, the plugin that ships this one along with the rest of its 1 skill, 4 commands.

Per session 11 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 656 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00011 $0.00656
Opus 5 $0.00005 $0.00328
Sonnet 5 $0.00002 $0.00131
Haiku 4.5 $0.00001 $0.00066

Measured 3d ago against content hash 0d8f2207ecab, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

commands/remember.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/remember [type]

Save something worth keeping - a fix that worked, a pattern you want to reuse, a decision you made, or a pitfall to avoid.

Argument

  • type (optional): solution, pattern, decision, gotcha

Workflow

Step 1: Determine Type

If no type was provided, ask:

What do you want to save?
1. solution - An error you fixed (problem + root cause + fix)
2. pattern  - An approach that worked well
3. decision - An architecture or design choice you made
4. gotcha   - A pitfall or trap to avoid next time

Step 2: Ask Type-Specific Questions

Keep this conversational. Ask all questions for the type at once, not one by one.

Solution:

  • What was the problem or error?
  • What was the root cause?
  • What fixed it?

Pattern:

  • What is this pattern called?
  • When should it be used?
  • How is it applied?

Decision:

  • What question was decided?
  • What was decided, and why?
  • What alternatives were rejected?

Gotcha:

  • What is the pitfall?
  • Why is it a problem?
  • What is the correct approach?

Step 3: Generate Tags

Generate 3-5 tags based on:

  • The active project (if known from .claude/memory/index.json)
  • Technology or tool mentioned
  • Keywords from the description

Format tags as lowercase slugs: next-js, api-auth, caching

Step 4: Save the Learning

Create the file at:

.claude/memory/learnings/{type}-{YYYY-MM-DD}-{short-slug}.md

Where {short-slug} is a 2-3 word kebab-case summary of the content.

File format:

---
date: {YYYY-MM-DD}
type: {type}
tags: [{tag1}, {tag2}, {tag3}]
project: {active project name, or "general" if unknown}
---

# {Title summarizing what was learned}

## Context

{Brief description of where/when this came up}

## {Type-specific content}

{The actual content based on what was answered in Step 2}

For each type, use these section headers inside the file:

  • solution: ## Problem, ## Root Cause, ## Fix
  • pattern: ## When to Use, ## How to Apply
  • decision: ## Decision, ## Reasoning, ## Rejected Alternatives
  • gotcha: ## The Pitfall, ## Why It's a Problem, ## Correct Approach

Read the full file on GitHub · 112 lines

Changes

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.

  1. 3d ago First seen · 112 lines · 11 tokens per session scan A 0d8f2207ecab

Subscribe to this mod's changes

remember is a command published in the GitHub repository primeline-ai/claude-code-starter-system (5 stars, last pushed 18d ago), licensed MIT. It adds 11 tokens to every session and 656 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.