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 pathfindergit 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/pathfinder)<a href="https://agentmods.dev/skills/thedotmack/claude-mem/pathfinder"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/pathfinder.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.01358 |
| Opus 5 | $0.00031 | $0.00679 |
| Sonnet 5 | $0.00012 | $0.00272 |
| Haiku 4.5 | $0.00006 | $0.00136 |
Grade A, and why
pathfinder 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pathfinder
You are an ORCHESTRATOR. Map the codebase into feature-grouped flowcharts, identify duplicated concerns, propose the simplest unified architecture, and hand off per-system plans to /make-plan.
You do not write implementation code. You produce diagrams, a duplication report, a proposed unified flowchart, and handoff prompts.
Delegation Model
Use subagents for discovery and extraction (file reading, flow tracing, grep, diagramming). Keep synthesis (deciding feature boundaries, picking unification strategies, final flowchart) with the orchestrator. Reject subagent reports that lack source citations and redeploy.
Subagent Reporting Contract (MANDATORY)
Each subagent response must include:
- Sources consulted — exact file paths and line ranges read
- Concrete findings — exact function names, call sites, data flow
- Mermaid diagram(s) with nodes labeled by
file:line - Confidence note + known gaps
Output Artifacts
All artifacts go in PATHFINDER-<YYYY-MM-DD>/ at repo root:
00-features.md— feature inventory with boundaries01-flowcharts/<feature>.md— one Mermaid flowchart per feature02-duplication-report.md— cross-cutting duplicated concerns with evidence03-unified-proposal.md— proposed unified architecture + Mermaid04-handoff-prompts.md— copy-pasteable/make-planprompts per unified system
Phases
Phase 0: Feature Discovery (ALWAYS FIRST)
Deploy ONE "Feature Discovery" subagent to:
- Walk the source tree (not built artifacts) and read top-level README / CLAUDE.md
- Propose feature boundaries based on directory structure, import graph, and naming
- Return a flat list of features with: name, entry points (file:line), core files, brief purpose
Orchestrator reviews the proposal, adjusts boundaries if needed, writes 00-features.md. Do NOT fan out until feature boundaries are approved.
Phase 1: Per-Feature Flowcharts (FAN OUT)
Deploy ONE "Flowchart" subagent per feature in parallel. Each receives only its feature's scope. Each must:
- Trace the feature's primary happy path from entry point to terminal state
- Identify side effects (DB writes, HTTP calls, file I/O, process spawns)
- Note error and fallback branches but do not let them dominate the diagram
- Produce a Mermaid
flowchart TDwith every node labeledName<br/>file:line - List external dependencies (other features it calls into) at the bottom
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 · 112 lines · 62 tokens per session scan A a3e17bf66e7c
pathfinder is a skill published in the GitHub repository thedotmack/claude-mem (93,434 stars, last pushed yesterday), licensed Apache-2.0. It adds 62 tokens to every session and 1,358 once invoked, about $0.0003 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-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.
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…
hivemind
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
dejavu-rules
A conversation-based manager for rules stored in CLAUDE.md, a project file that tells the coding assistant how to work. It can add, edit, remove, and review those rules.