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/lets7512/rlm-skill/statsnpx skills add Lets7512/rlm-skill --skill statsgit clone --depth 1 https://github.com/Lets7512/rlm-skillWhat 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 | $0.00038 | $0.00271 |
| Opus 5 | $0.00019 | $0.00135 |
| Sonnet 5 | $0.00008 | $0.00054 |
| Haiku 4.5 | $0.00004 | $0.00027 |
Grade A, and why
stats scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
RLM Token Savings Dashboard
Show how much context window space RLM patterns have saved.
Instructions
-
Run the stats script using Bash:
python "${CLAUDE_PLUGIN_ROOT}/src/stats.py"If
${CLAUDE_PLUGIN_ROOT}is not set (e.g., during local dev), use the plugin directory path directly. -
CRITICAL: Copy-paste the ENTIRE output as markdown into your response. Do NOT summarize or collapse it. The user must see the full dashboard.
-
After the dashboard, add a one-line highlight, e.g.:
- "RLM saved 45,000 tokens (~97% reduction) by keeping raw data out of context."
- If no events yet: "No RLM events logged yet. The PreToolUse hook will start tracking when it detects large file operations (>500KB)."
-
If the user says "reset", run:
python "${CLAUDE_PLUGIN_ROOT}/src/stats.py" reset
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.
- 2d ago First seen · 32 lines · 38 tokens per session scan A 15aa882b5da2
stats is a skill published in the GitHub repository Lets7512/rlm-skill (24 stars, last pushed 6mo ago), licensed MIT. It adds 38 tokens to every session and 271 once invoked, about $0.0002 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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