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 babysitgit 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/babysit)<a href="https://agentmods.dev/skills/thedotmack/claude-mem/babysit"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/babysit/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/babysit"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/babysit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00044 | $0.01084 |
| Opus 5.5 | $0.00018 | $0.00434 |
| Sonnet 5.5 | $0.00009 | $0.00217 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
babysit 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Babysit PR
Stay with the PR until it is actually clean. Do not stop after one check pass if comments or review threads are still unresolved.
Workflow
- Identify the PR number, branch, and base branch.
- Confirm the PR is not draft and inspect mergeability, checks, review decision, comments, and review threads.
- Watch pending checks until they finish. Poll at a practical interval, usually 30-60 seconds unless the user asks for a different cadence.
- Read new comments and unresolved review threads. Treat bot summaries as useful, but verify actionable findings against the code.
- Fix real issues in focused commits, run relevant tests/builds, push, and return to step 2.
- Resolve stale review threads only after verifying the code or generated artifact now addresses the comment.
- Stop only when checks are passing or intentionally skipped, review decision is acceptable, no actionable comments remain, and no unresolved review threads remain.
GitHub CLI Checks
Use gh pr view for the coarse status:
gh pr view <number> --json \
number,state,isDraft,mergeable,mergeStateStatus,reviewDecision,headRefOid,statusCheckRollup,url
Resolve the repository owner/name before using GraphQL:
repo_json=$(gh repo view --json owner,name)
owner=$(jq -r '.owner.login // .owner.name' <<<"$repo_json")
repo=$(jq -r '.name' <<<"$repo_json")
Use GraphQL for unresolved review threads. Include pageInfo; omit cursor on the first page, then pass the previous endCursor with -f cursor="$cursor" while hasNextPage is true.
gh api graphql \
-f query='query($owner:String!,$repo:String!,$number:Int!,$cursor:String){repository(owner:$owner,name:$repo){pullRequest(number:$number){reviewThreads(first:100,after:$cursor){pageInfo{hasNextPage endCursor}nodes{id,isResolved,isOutdated,path,line,comments(last:1){nodes{author{login},body,createdAt,url}}}}}}}' \
-f owner="$owner" -f repo="$repo" -F number=<number>
Use this loop when a PR may have many review threads:
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 · 88 lines · 44 tokens per session scan A e322b0c36fa1
babysit is a skill published in the GitHub repository thedotmack/claude-mem (102,887 stars, last pushed today), licensed Apache-2.0. It adds 44 tokens to every session and 1,084 once invoked, about $0.0002 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.
Other skills, from other repositories
github
GitHub via gh CLI: PRs, issues, reviews, repos, auth.
merge-batch
Prepare and merge an owner-selected PR batch with contributor credit and exact-candidate admission. Use for a pasted PR list; combined focused validation is diagnostic, and every actual landing still requires its applicable checks and authorization.
babysit
Watch an open PR — fix failing CI, handle the straightforward review comments, and drive it to a mergeable state. Claude Code analog of Cursor's built-in /babysit. Use after opening a PR when the user wants the agent to shepherd it without re-prompting.
make-pr-easy-to-review
Prepare PRs for review by cleaning noisy history, improving PR descriptions, and adding reviewer guidance without changing code behavior. Use for "make this easy to review", "tidy this PR", "clean up commits", or "annotate the diff".
hivemind-goals
Create, track and update team goals via the Deeplake virtual filesystem at memory/goal/. Use whenever the user mentions a goal, objective, 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", or any actionable work item…
hivemind-memory
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.