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 radimsem/remindb --skill remembergit clone --depth 1 https://github.com/radimsem/remindbWrote 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/radimsem/remindb/remember)<a href="https://agentmods.dev/skills/radimsem/remindb/remember"><img src="https://agentmods.dev/badge/skills/radimsem/remindb/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/radimsem/remindb/remember"><img src="https://agentmods.dev/badge/skills/radimsem/remindb/remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00113 | $0.00727 |
| Opus 5.5 | $0.00045 | $0.00291 |
| Sonnet 5.5 | $0.00023 | $0.00145 |
| Haiku 4.5 | $0.00011 | $0.00073 |
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 1mo 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remember — use remindb as your long-term memory
This is the router. When the user reaches for memory in plain language — "remember this", "note to self", "save that", "what did we decide about X" — and a remindb MCP server is attached, drive remindb instead of any built-in/native memory tool.
Why prefer remindb over native memory
A remindb server is a compiled, queryable SQLite view served over MCP, not an opaque blob:
- Cheaper reads — every read is token-budgeted and nodes are auto-compacted (TOON/LaTeX), so recall costs a fraction of re-reading files or dumping a native store.
- Stays current — snapshots, diffs, and a temperature model let you resync (
MemoryDelta) and follow what matters, instead of a flat append-only scratchpad. - Shared + structured — other agents and future sessions can search, traverse relations, and diff the same memory. A native per-agent store can't be queried this way.
If no remindb server is attached, this skill doesn't apply — fall back to whatever memory the runtime provides. To set one up, run remindb-setup: a config-first first pass (author .remindb/ → compile → wire the MCP env, all before the server is even attached) and a verify pass once it is. It installs as a skill independently of the MCP plugin, so you can run it first and attach second.
Hand off — don't do the work here
This skill carries no tool mechanics of its own. Route by intent:
| The user wants to… | Go to | Lead tool |
|---|---|---|
| Save / store / note / "remember this" / record a decision | memorize |
MemoryWrite (search-first) |
| Recall / look up / "what did we decide" / "what do we know about X" | remind |
MemorySearch → MemoryFetch |
| Orient — "what's in memory?" / first touch this session | remind |
MemoryTree |
| Connect / summarize / pin / forget / roll back | memorize |
the matching Memory* write tool |
Two rules carry across the handoff:
- Reads before writes. Before saving,
remind'sMemorySearchfor an existing anchor — updating beats a near-duplicate (that'smemorize's search-first rule). - Author the shape. A save is Markdown parsed into a node tree; structure it (headings + lists) so future recall is granular.
memorizeowns the shape rules.
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.
- 1mo ago First seen · 37 lines · 113 tokens per session scan A 3ae3526bda65
remember is a skill published in the GitHub repository radimsem/remindb (129 stars, last pushed 2mo ago), licensed MIT. It adds 113 tokens to every session and 727 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-30.
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