A memory tool that saves important decisions, facts, and project context from a conversation into ByteRover's searchable context tree. ByteRover is a system for storing conversation knowledge for later retrieval.

Use it after project decisions, technical investigations, debugging sessions, or other discussions worth preserving for later.

It reduces the need to repeat important project background, technical decisions, and conclusions in future conversations.

remember — Skill for Claude Code and Codex by ahkedia: A memory tool that saves important decisions, facts, and…

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 skills/ahkedia/lyra-ai/remember

Any agent

npx skills add ahkedia/lyra-ai --skill remember

Clone the repo

git clone --depth 1 https://github.com/ahkedia/lyra-ai

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/ahkedia/lyra-ai/remember.svg)](https://agentmods.dev/skills/ahkedia/lyra-ai/remember)

Your own site

<a href="https://agentmods.dev/skills/ahkedia/lyra-ai/remember"><img src="https://agentmods.dev/badge/skills/ahkedia/lyra-ai/remember.svg" alt="Measured on agentmods" height="20"></a>

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 704 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.

Model	Per session	Once invoked
Fable 5.1	$0.00032	$0.00704
Opus 5	$0.00016	$0.00352
Sonnet 5	$0.00006	$0.00141
Haiku 4.5	$0.00003	$0.00070

Measured 5d ago against content hash 31dfeefae20c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, 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 5d 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.

skills/remember/SKILL.md · 71 lines

How it starts

The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Remember

Saves meaningful context from the current conversation into ByteRover's long-term memory (.brv/context-tree/). Useful after technical decisions, project planning sessions, debugging sessions, or any conversation worth recalling later.

When to Use

After discussing a project decision (e.g., "we decided to use X because Y")
After a technical deep-dive (architecture, debugging session, config changes)
When Akash asks: "remember this" or "save this"
Self-invoke after completing a complex multi-step task — ask "Want me to remember this?" before curating

Operations

Curate current conversation context

TIMESTAMP=$(date +%s)
TMPFILE="/tmp/lyra-memory-${TIMESTAMP}.md"

# Write a structured summary of what's worth remembering
cat > "$TMPFILE" << 'SUMMARY'
[SUMMARY_CONTENT]
SUMMARY

brv curate "$TMPFILE"
rm -f "$TMPFILE"

Decision Logic

Identify what's worth remembering: decisions made, facts learned, preferences expressed, project context, technical outcomes
Skip: greetings, trivial acknowledgments, transient task outputs (e.g., "what's on my shopping list")
Write a structured summary covering:
- What was decided or done (concrete outcome)
- Why (reasoning, if stated)
- Key details (commands, file paths, config changes, names)
- What to remember for next time (carry-forward context)
Run brv curate on the temp file
Report what was saved in one sentence

Examples

User says: "/remember" Action: Summarize the conversation, write to /tmp/lyra-memory-{ts}.md, run brv curate, clean up Response: "Saved to memory: ByteRover installed on Lyra, Context Tree bootstrapped from MEMORY.md and SOUL.md, /remember skill created."

User says: "remember that we use paise not rupees in Reap Capital" Action: Write a targeted fact-note, curate it Response: "Saved to memory: Reap Capital uses paise (not rupees) for all monetary values."

User says: "save the decisions from this session" Action: Same as /remember — summarize the session's key decisions and curate them Response: One-sentence summary of what was saved.

Read the full file on GitHub · 71 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.

5d ago First seen · 71 lines · 32 tokens per session scan A 31dfeefae20c

Subscribe to this mod's changes

remember is a skill published in the GitHub repository ahkedia/lyra-ai (5 stars, last pushed 16d ago), licensed MIT. It adds 32 tokens to every session and 704 once invoked, about $0.0002 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.

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remember

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

Remember

When to Use

Operations

Curate current conversation context

Decision Logic

Examples

What this file has done since we first saw it

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