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 monte-carlo-data/mc-agent-toolkit --skill reinforce-agentgit clone --depth 1 https://github.com/monte-carlo-data/mc-agent-toolkitWrote 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/monte-carlo-data/mc-agent-toolkit/reinforce-agent)<a href="https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/reinforce-agent"><img src="https://agentmods.dev/badge/skills/monte-carlo-data/mc-agent-toolkit/reinforce-agent.svg" alt="Measured on agentmods" height="20"></a>- 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 | $0.00147 | $0.02359 |
| Opus 5 | $0.00073 | $0.01179 |
| Sonnet 5 | $0.00029 | $0.00472 |
| Haiku 4.5 | $0.00015 | $0.00236 |
Grade A, and why
monte-carlo-reinforce-agent 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monte Carlo Reinforce Agent Skill
This skill turns Monte Carlo's reinforcement loop diagnosis into landed code fixes. Monte Carlo runs a daily reinforcement loop pipeline that analyzes an agent's traces and produces, per workflow, a report of diagnosed issues — each with supporting evidence (trace deep-links, verifier checks) and recommended fixes. This skill reads that diagnosis, ranks it, proposes what to fix, and follows through with a pull request — pausing for the user's decision at each fan-out point.
Monte Carlo tool routing (required): Always call Monte Carlo MCP tools through this plugin's bundled server, whose fully-qualified tool names are
mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__<tool>(e.g.mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__get_reinforcement_loop_report). Bare tool names used in this skill (get_agent_metadata,get_reinforcement_loop_summaries,get_reinforcement_loop_report) refer to that bundled server. If the session also has a separately-configuredmonte-carlo-mcpserver, do not route to it — it may point at a different endpoint or credentials.
When to activate this skill
Activate when the user:
- Wants to fix or improve an AI agent based on its Monte Carlo reinforcement loop ("fix my agent", "reinforce my agent", "improve my agent's health").
- Asks what to fix in an agent ("what are my agent's top issues", "what should I fix in ").
- Wants a PR that addresses an agent's diagnosed problems.
When NOT to activate this skill
- Investigating one agent alert or trace (eval-score drop, latency/token spike, a specific
trace id) → use
monte-carlo-troubleshoot-agent-traces. That skill investigates a single incident; this one acts on the standing reinforcement loop diagnosis across a workflow and writes code. - Creating or tuning agent monitors →
monte-carlo-monitoring-advisor/tune-monitor. - Instrumenting a new agent to emit traces →
monte-carlo-instrument-agent.
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.
- 8d ago First seen · 165 lines · 147 tokens per session scan A df10086d23b4
monte-carlo-reinforce-agent is a skill published in the GitHub repository monte-carlo-data/mc-agent-toolkit (91 stars, last pushed 14d ago), licensed Apache-2.0. It adds 147 tokens to every session and 2,359 once invoked, about $0.0007 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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