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 agentmods add skills/skynetcmd/m3-memory/m3-statusnpx skills add skynetcmd/m3-memory --skill m3-statusgit clone --depth 1 https://github.com/skynetcmd/m3-memoryWrote 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/skynetcmd/m3-memory/m3-status)<a href="https://agentmods.dev/skills/skynetcmd/m3-memory/m3-status"><img src="https://agentmods.dev/badge/skills/skynetcmd/m3-memory/m3-status.svg" alt="Measured on agentmods" 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 | $0.00023 | $0.00230 |
| Opus 5 | $0.00012 | $0.00115 |
| Sonnet 5 | $0.00005 | $0.00046 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
m3-status 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 6d 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.
What it actually says
M3 Status
When to Use
Use this skill when you want to view the status, row counts, queue depth, spill size, and hook health of the background chatlog subsystem.
Instructions
Step 1 — run via the Bash tool, trying these resolvers in order. Stop at the first that returns exit 0:
mcp-memory chatlog status # 1. plain CLI
python -m m3_memory.cli chatlog status # 2. module form (Windows --user case)
.venv/Scripts/python.exe -m m3_memory.cli chatlog status # 3. repo venv (Windows)
.venv/bin/python -m m3_memory.cli chatlog status # 3. repo venv (macOS/Linux)
Step 2 — print the table verbatim.
Step 3 — append exactly ONE line of interpretation: capture rate, hook health, or any explicit warning the table reported.
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.
- 6d ago First seen · 23 lines · 23 tokens per session scan A 179c7001a0e6
m3-status is a skill published in the GitHub repository skynetcmd/m3-memory (23 stars, last pushed 6d ago), licensed Apache-2.0. It adds 23 tokens to every session and 230 once invoked, about $0.0001 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.
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