NanoClaw is an AI assistant that runs agents inside separate Linux containers, isolating their files and execution environments. People use it to connect agents to messaging services and run assistants with memory and scheduled jobs. The catalogue contains skills and instructions for extending or operating NanoClaw.
Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/nanocoai/nanoclaw/add-mnemon)<a href="https://agentmods.dev/skills/nanocoai/nanoclaw/add-mnemon"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/add-mnemon/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/nanocoai/nanoclaw/add-mnemon"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/add-mnemon.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 | $0.00027 | $0.01690 |
| Opus 5 | $0.00014 | $0.00845 |
| Sonnet 5 | $0.00005 | $0.00338 |
| Haiku 4.5 | $0.00003 | $0.00169 |
Grade B, and why
add-mnemon scanned grade B with 2 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
cat /home/node/.claude/settings.json | grep -A5 mnemon Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://api.github.com/repos/mnemon-dev/mnemon/releases/latest | grep '"tag_name"' How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Mnemon — Persistent Memory
Installs mnemon in the agent container image. On each container start, mnemon setup registers Claude Code hooks that surface relevant memory before the agent responds and store new insights after each turn. Memory is written to the per-agent-group .claude/ mount and survives container restarts.
Provider Compatibility
mnemon hooks fire only under --target claude-code. Use this skill on agent groups that run the default Claude provider. The provider is the materialized provider key in each group's container.json (absent or claude = default Claude provider). Confirm it before applying:
grep -H '"provider"' groups/*/container.json 2>/dev/null # no match, or "provider": "claude" = Claude
If a group sets a different provider (e.g. "provider": "opencode"), it spawns its own process and never invokes the claude CLI, so the hooks registered by mnemon setup do not run for that group.
Phase 1: Pre-flight
Check if already applied
grep -q 'MNEMON_VERSION' container/Dockerfile && echo "Already applied" || echo "Not applied"
If already applied, re-run Phase 2 anyway — every step is idempotent and skips work that is already in place — then continue to Phase 3 (Verify).
Check latest mnemon version
curl -fsSL https://api.github.com/repos/mnemon-dev/mnemon/releases/latest | grep '"tag_name"'
Note the version (e.g. v0.1.1) — use it as MNEMON_VERSION in the next step.
Phase 2: Apply Changes
1. Dockerfile — install mnemon binary
Insert the mnemon block immediately above the # ---- Bun runtime section of container/Dockerfile (skip if grep -q 'MNEMON_VERSION' container/Dockerfile already matches):
# ---- mnemon — persistent agent memory ----------------------------------------
ARG MNEMON_VERSION=0.1.1
RUN ARCH=$(dpkg --print-architecture) && \
curl -fsSL "https://github.com/mnemon-dev/mnemon/releases/download/v${MNEMON_VERSION}/mnemon_${MNEMON_VERSION}_linux_${ARCH}.tar.gz" \
| tar -xz -C /usr/local/bin mnemon && \
chmod +x /usr/local/bin/mnemon
ENV MNEMON_DATA_DIR=/home/node/.claude/mnemon
What ships with it
3 files 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.
- 9d ago First seen · 178 lines · 27 tokens per session scan B 2109353129e0
add-mnemon is a skill published in the GitHub repository nanocoai/nanoclaw (30,720 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 1,690 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 2 findings (reads agent configuration directories, 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
data-sync
Sync and archive data from messaging platforms (WhatsApp, Discord, Slack, Twitter/X, Google) into Moltis memory as daily digest summaries. Orchestrates crawl tools and writes structured markdown to the memory system.
self-reflection
Daily loop that reviews the bot's own recent outbound behavior and any pending lessons in self/learnings.md, stress-tests candidate rules against 10-20 hypothetical scenarios, and on explicit owner approval routes each to the right home among three sinks — prompts/project.md (durable behavioral rules), a memory file…
Pynchy Development
Use when running pynchy locally — running the app, tests, linting, formatting, prek hooks, or rebuilding the agent container. Also use when determining whether you're on the live Pynchy host or a local machine, and for debugging agent behavior-- session transcript branching, inspecting message history and agent traces…
Pynchy Plugin Authoring
Use when creating, scaffolding, or updating a pynchy plugin, including channels, MCP servers, skills, agent cores, workspace specs, and container runtime plugins. Also use when users ask how to register plugins via config.toml, add entry points, or validate plugin hook wiring.
x-integration
Post tweets, like, reply, retweet, and quote on X (Twitter) using browser automation. Use when the user asks you to interact with X/Twitter.
slack-token-extractor
Refresh expired Slack browser tokens (xoxc/xoxd) using persistent browser sessions. Use when Slack MCP tools fail with authentication errors.