Claude-Mem, now presented as Grok Mem, records an agent's work, compresses it with AI, and brings relevant notes into later sessions so the agent can remember decisions and next steps. It is intended for persistent context across agent conversations and supports multiple coding-agent environments. The catalogue add-ons provide the workflows and integrations used to operate this memory system.
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 thedotmack/claude-mem --skill smart-exploregit clone --depth 1 https://github.com/thedotmack/claude-memWrote 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/thedotmack/claude-mem/smart-explore)<a href="https://agentmods.dev/skills/thedotmack/claude-mem/smart-explore"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/smart-explore/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/thedotmack/claude-mem/smart-explore"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/smart-explore.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00041 | $0.02190 |
| Opus 5 | $0.00020 | $0.01095 |
| Sonnet 5 | $0.00008 | $0.00438 |
| Haiku 4.5 | $0.00004 | $0.00219 |
Grade A, and why
smart-explore 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 9d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Smart Explore
Structural code exploration using AST parsing. This skill overrides your default exploration behavior. While this skill is active, use smart_search/smart_outline/smart_unfold as your primary tools instead of Read, Grep, and Glob.
Core principle: Index first, fetch on demand. Give yourself a map of the code before loading implementation details. The question before every file read should be: "do I need to see all of this, or can I get a structural overview first?" The answer is almost always: get the map.
Your Next Tool Call
This skill only loads instructions. You must call the MCP tools yourself. Your next action should be one of:
smart_search(query="<topic>", path="./src") -- discover files + symbols across a directory
smart_outline(file_path="<file>") -- structural skeleton of one file
smart_unfold(file_path="<file>", symbol_name="<name>") -- full source of one symbol
Do NOT run Grep, Glob, Read, or find to discover files first. smart_search walks directories, parses all code files, and returns ranked symbols in one call. It replaces the Glob → Grep → Read discovery cycle.
3-Layer Workflow
Step 1: Search -- Discover Files and Symbols
smart_search(query="shutdown", path="./src", max_results=15)
Returns: Ranked symbols with signatures, line numbers, match reasons, plus folded file views (~2-6k tokens)
-- Matching Symbols --
function performGracefulShutdown (services/infrastructure/GracefulShutdown.ts:56)
function httpShutdown (services/infrastructure/HealthMonitor.ts:92)
method WorkerService.shutdown (services/worker-service.ts:846)
-- Folded File Views --
services/infrastructure/GracefulShutdown.ts (7 symbols)
services/worker-service.ts (12 symbols)
This is your discovery tool. It finds relevant files AND shows their structure. No Glob/find pre-scan needed.
Parameters:
query(string, required) -- What to search for (function name, concept, class name)path(string) -- Root directory to search (defaults to cwd)max_results(number) -- Max matching symbols, default 20, max 50file_pattern(string, optional) -- Filter to specific files/paths
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.
- 9d ago First seen · 194 lines · 41 tokens per session scan A 89c834b9ed88
smart-explore is a skill published in the GitHub repository thedotmack/claude-mem (93,434 stars, last pushed 2d ago), licensed Apache-2.0. It adds 41 tokens to every session and 2,190 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 2026-08-30.
Other skills, from other repositories
hivemind-graph
Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what's the architecture / which subsystems exist?", "what's the impact of…
dejavu-scan
Trigger a manual scan for antipatterns. Use when user asks "scan for patterns", "dejavu scan", "check for mistakes".
dejavu-status
Quick status check for dejavu. Use when user asks "dejavu status", "how's dejavu doing".
hivemind-goals
Create, track and update team goals + KPIs via the Deeplake virtual filesystem at memory/goal/ and memory/kpi/. Use whenever the user mentions a goal, objective, KPI, target, milestone, or asks to track progress on something measurable. ALSO use when the user says "task", "todo", "work item", "remind me to", "fix X"…
hivemind-memory
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
hivemind-memory
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.