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 memseekai/memseek --skill memseek-remembergit clone --depth 1 https://github.com/memseekai/memseekWrote 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/memseekai/memseek/memseek-remember)<a href="https://agentmods.dev/skills/memseekai/memseek/memseek-remember"><img src="https://agentmods.dev/badge/skills/memseekai/memseek/memseek-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/memseekai/memseek/memseek-remember"><img src="https://agentmods.dev/badge/skills/memseekai/memseek/memseek-remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00024 | $0.00382 |
| Opus 5.5 | $0.00010 | $0.00153 |
| Sonnet 5.5 | $0.00005 | $0.00076 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
memseek-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.
What it actually says
Remember with Memseek
Persist $ARGUMENTS only when the user explicitly asks you to remember it or clearly
confirms it is durable. Never infer a preference, policy, deadline, or decision from
silence. Never store secrets, credentials, access tokens, private keys, or raw tool output.
The SessionStart context gives you the exact project entity and conversation session.
If its memory write policy is off, do not write. Explain that the user must change
MEMSEEK_CAPTURE_MODE to explicit or conversation and start a new session.
- If automatic capture mode is
conversationand$ARGUMENTSis already stated in the current user prompt, explain that the exact prompt is being captured; do not duplicate it. - Otherwise, call the Memseek MCP
remembertool with:entity: the exact project memory entity from SessionStarttype:messagetext: the user's exact words, without interpretationcontent.text: the same exact wordscontent.role:usercontent.session_id: the exact conversation session from SessionStartcontent.ordinal: a current Unix timestamp in millisecondsdedupe_key: a stableclaude-explicit:key derived from the entity and exact text
- Report that the append was accepted. Do not claim that derived memories already exist; the Memseek worker builds L1–L3 state asynchronously.
If the write tool is unavailable, run /memseek-memory:memseek-status and report the
specific configuration or tool-contract failure.
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 · 34 lines · 24 tokens per session scan A 831be729968d
memseek-remember is a skill published in the GitHub repository memseekai/memseek (11 stars, last pushed 25d ago), licensed Apache-2.0. It adds 24 tokens to every session and 382 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
atomicmemory
Persistent semantic memory across Claude Code sessions — user preferences, project context, prior decisions, codebase facts. Call memorysearch before answering questions that reference past work. Call memoryingest after the user shares durable facts.
atomicmemory-cli
Use the installed AtomicMemory CLI for memory search, ingestion, packaging, diagnostics, and agent-safe JSON output.
agent-memory-discipline
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
context-compression
A guide to shortening the information an agent carries between steps. It explains how to replace large tool outputs and old conversation details with compact, useful summaries while preserving important decisions and constraints.