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 request-progressgit 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/request-progress)<a href="https://agentmods.dev/skills/thedotmack/claude-mem/request-progress"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/request-progress/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/request-progress"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/request-progress.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 | $0.00088 | $0.03168 |
| Opus 5.5 | $0.00035 | $0.01267 |
| Sonnet 5.5 | $0.00018 | $0.00634 |
| Haiku 4.5 | $0.00009 | $0.00317 |
Grade A, and why
request-progress 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 today.
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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Request Progress
My Work shows one card per human request, built from the human's own prompts: a conversation's first substantive ask owns its follow-ups, and agent work, activity and progress sit beneath it, even before any work is recorded. People rarely finish a goal in one conversation, though. They /clear, switch worktrees, hand off to Codex, come back the next day. This skill checks each new conversation's opening ask against the open requests in its project to join those conversations into one overall request, then reviews each request from memory and writes the build story.
${CLAUDE_SKILL_DIR} is the folder holding this file. Every step runs one CLI:
CLI="bun ${CLAUDE_SKILL_DIR}/scripts/request-groups.mjs"
It opens ~/.claude-mem/claude-mem.db read-only and writes only to ~/.claude-mem/work-view/. My Work picks up changes within a second: grouped conversations show as one card that starts at the earliest ask, with a progress meter; the request page shows the build story, how released work landed (usage, value, regressions, why each change was made) and its work product; each card shows the owner goal it serves; the build story's first paragraph is My Work's recap, and the story sheet shows shipping velocity, the narrative and preferences. A review never changes a request's recorded status.
Default window: 14 days. Use the window the user asks for.
1. Group conversations into overall requests
A human request is the top level of My Work: every conversation's opening ask either starts a request or continues one. Grouping decides once per conversation, from its opening substantive ask, and decisions already in the ledger are kept. Requests stay inside one project root (the project key before its first /, so worktrees of one repo share requests); a conversation never joins a request from another root, and apply refuses one that does.
$CLI grouping-input --days 14
It returns one batch: up to 20 conversations the ledger has not decided yet, earliest first, all from one project root, each with its openingAsk and work lists; that root's openRequests (title, anchor conversation, anchorAsk, recentAsks; shipped and abandoned requests are left out); and proposedLinks, conversations code already linked because they wrote to the same work-state list or recorded the same request_id (keep these unless the list is obviously a generic bucket). remaining counts the undecided conversations left for later batches. Apply each batch, then run grouping-input again until conversations is empty.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today Changed 4fba9fbc2e8f
- yesterday First seen · 126 lines · 88 tokens per session scan A 5e8cff2bfd91
request-progress is a skill published in the GitHub repository thedotmack/claude-mem (103,125 stars, last pushed today), licensed Apache-2.0. It adds 88 tokens to every session and 3,168 once invoked, about $0.0004 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 2026-10-25.
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Onboarding doc for a new contributor or agent — project state, conventions, priorities relevant to the specified role.
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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…
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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.