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 weekly-digestsgit 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/weekly-digests)<a href="https://agentmods.dev/skills/thedotmack/claude-mem/weekly-digests"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/weekly-digests/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/weekly-digests"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/weekly-digests.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 | $0.00093 | $0.03503 |
| Opus 5 | $0.00046 | $0.01751 |
| Sonnet 5 | $0.00019 | $0.00701 |
| Haiku 4.5 | $0.00009 | $0.00350 |
Grade A, and why
weekly-digests scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "http://localhost:${WORKER_PORT}/api/context/inject?project=PROJECT_NAME&full=true" \ How it starts
The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weekly Digests
Produce a serial, multi-chapter narrative digest of a project's complete claude-mem history. Differs from timeline-report (one long report) — this generates one digest per ISO week, with each subagent reading the prior week's carry-forward block so the story stays coherent.
The chapter count equals the number of ISO weeks the timeline covers. A project with 2 weeks of data produces 2 chapters; one with 30 weeks produces 30. There is no fixed length — count the weeks first, then drive the pipeline off that count.
When to Use
Trigger when the user asks for:
- "Weekly digests"
- "Week-by-week story"
- "Serial timeline"
- "Story chapters of [project]"
- "Run a digest for each week"
- "Continue the story week by week"
If the user wants a single sweeping report, use timeline-report instead. This skill is for serial chapter format.
Prerequisites
- claude-mem worker running
- Project has at least one ISO week of observations (the pipeline degenerates gracefully — even N=1 works)
- A clean output directory the user is comfortable writing into
Resolve the worker port (do this once, reuse $WORKER_PORT):
WORKER_PORT="${CLAUDE_MEM_WORKER_PORT:-$(node -e "const fs=require('fs'),p=require('path'),os=require('os');const uid=(typeof process.getuid==='function'?process.getuid():77);const fallback=String(37700+(uid%100));try{const s=JSON.parse(fs.readFileSync(p.join(os.homedir(),'.claude-mem','settings.json'),'utf-8'));process.stdout.write(String(s.CLAUDE_MEM_WORKER_PORT||fallback));}catch{process.stdout.write(fallback);}" 2>/dev/null)}"
Workflow
Step 1: Determine the Project Name
Same worktree-detection pattern as timeline-report. In a worktree, the data source is the parent project:
git_dir=$(git rev-parse --git-dir 2>/dev/null)
git_common_dir=$(git rev-parse --git-common-dir 2>/dev/null)
if [ "$git_dir" != "$git_common_dir" ]; then
parent_project=$(basename "$(dirname "$git_common_dir")")
else
parent_project=$(basename "$PWD")
fi
echo "$parent_project"
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 · 263 lines · 93 tokens per session scan A e3f711e8b8e1
weekly-digests is a skill published in the GitHub repository thedotmack/claude-mem (93,434 stars, last pushed yesterday), licensed Apache-2.0. It adds 93 tokens to every session and 3,503 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). 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-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
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.
dejavu
Review and manage learned antipattern rules. Use when Claude should review its past mistakes, apply learned rules to CLAUDE.md, or check rule effectiveness. Triggers on "/dejavu", "review rules", "what did you learn".
dejavu-report
Generate a weekly report of detected patterns and rule effectiveness. Use when user asks "dejavu report", "what did you learn this week", "dejavu weekly".