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
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add thedotmack/claude-mem/plugin install 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/oh-my-issues)<a href="https://agentmods.dev/skills/thedotmack/claude-mem/oh-my-issues"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/oh-my-issues/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/oh-my-issues"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/oh-my-issues.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00097 | $0.02808 |
| Opus 5 | $0.00048 | $0.01404 |
| Sonnet 5 | $0.00019 | $0.00562 |
| Haiku 4.5 | $0.00010 | $0.00281 |
Grade A, and why
oh-my-issues 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 10d 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
oh-my-issues
Turn an issue backlog into a roadmap. Issues are symptom data, not units of work — the unit of work is the architectural defect that produces them. The end state is open issues == open plans, 1:1.
Core principle
Stop closing issues one at a time. Group symptoms that share a single architectural fix into a cluster, give the cluster one canonical home (a plan-master issue + a plans/0X-*.md design doc), close every child with a standardized redirect, and ship one PR per cluster that closes all children atomically. New incoming bugs get appended to the matching master as a "Round N" comment, not opened as new tracked issues.
This compounds three ways: architectural fixes retire whole symptom families, the plan's test matrix institutionalizes prevention in CI, and standardized triage makes residual inflow cheap.
When to use
- The repo has 20+ open issues and many feel like duplicates or platform-specific symptoms of the same defect.
- The user asks to "triage", "consolidate", "cluster", "dedupe", "group", or "make a plan from" the issue list.
- A new bug is filed and the user wants to know whether it belongs to existing work.
- The user wants to ship a focused PR that resolves a cluster of related issues.
When NOT to use
- Fewer than ~15 open issues: just close them.
- Issues are genuinely independent (no shared root causes): one fix per issue is correct.
- The repo lacks
plans/discipline and the user does not want to introduce one — propose first, do not impose.
Three modes
Mode 1: Cluster pass (initial reduction)
Use when the backlog has never been consolidated. Goal: go from N issues to N_plans masters in one operation.
- Read everything in full. Fetch every open issue's body and its comment thread — not just titles. Surface-level grouping fails without full text, and reproduction steps, linked duplicates, and diagnostic output often live in comments rather than the original body. See "GitHub CLI primitives" below for the correct paginated listing + per-issue comment fetch (a single
gh issue listcall does not return comment bodies). - Cluster by root cause, not by surface. The clustering question is would one architectural change retire all of these? — not do these mention the same word?. "Windows" is a surface; "spawn contract violated by host shells" is a root cause. Two issues with different surfaces can share a cluster (e.g. an env-var leak in two different code paths sharing one missing env-isolation boundary).
- Name each cluster as an architectural problem. Title format:
[plan-XX] <Architectural Defect> — <one-line scope>. Example:[plan-02] Spawn-Contract Templating — canonical ${CLAUDE_PLUGIN_ROOT} resolution across all hosts. The title must imply a fix, not a topic. - Open one master issue per cluster with a body that lists: the architectural defect, the children (by issue number), the fix sequence, and a required test matrix (host × IDE × shell, etc.) that prevents regression.
- Mirror each master as
plans/0X-<slug>.mdin the repo. The issue is the public tracker; the doc is the design. They reference each other. - Close every child with the standardized redirect comment (see below) and state
not planned. - Verify end state:
gh issue list --state openreturns exactly the masters and nothing else.
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.
- 10d ago First seen · 227 lines · 97 tokens per session scan A 292417dedb16
oh-my-issues is a skill published in the GitHub repository thedotmack/claude-mem (93,544 stars, last pushed yesterday), licensed Apache-2.0. It adds 97 tokens to every session and 2,808 once invoked, about $0.0005 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"…
batch
Research and plan a large-scale change, then execute it in parallel across 5–30 isolated worktree agents that each open a PR.
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.