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 commands/primeline-ai/claude-code-starter-system/remembergit clone --depth 1 https://github.com/primeline-ai/claude-code-starter-systemWhat 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 | $0.00011 | $0.00656 |
| Opus 5 | $0.00005 | $0.00328 |
| Sonnet 5 | $0.00002 | $0.00131 |
| Haiku 4.5 | $0.00001 | $0.00066 |
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.
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
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 · 112 lines · 11 tokens per session scan A 0d8f2207ecab
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.
Other commands, from other repositories
analyze-repo
Analyze an existing repository's structure, conventions, and guardrails.
go-test
Go TDD workflow with table-driven tests.
retex
Retex - Capture lesson learned dans memory après fix, rollback, erreur.
go-review
Go code review for idiomatic patterns.
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
doctor
Run IJFW health check (files, MCP server, hooks, memory, caps, framing).