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 Leeroo-AI/superml --skill ml-researchgit clone --depth 1 https://github.com/Leeroo-AI/supermlWrote 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/leeroo-ai/superml/ml-research)<a href="https://agentmods.dev/skills/leeroo-ai/superml/ml-research"><img src="https://agentmods.dev/badge/skills/leeroo-ai/superml/ml-research.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 | $0.00037 | $0.06769 |
| Opus 5 | $0.00018 | $0.03385 |
| Sonnet 5 | $0.00007 | $0.01354 |
| Haiku 4.5 | $0.00004 | $0.00677 |
Grade A, and why
ml-research 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Include a **validation/test snippet** (curl test, benchmark script, or smoke test) so the user can verify their setup works How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Research
Deep-dive into ML topics using verified framework documentation, not stale training data.
Grounding
Detect mode: On your first grounding call, check if Leeroopedia KB tools are available. If they return results, use KB mode. If unavailable or auth fails, use Web mode.
CRITICAL SELF-CHECK before Phase 1: If KB auth fails, your FIRST action must be WebFetch — not text output. Do NOT write any prose, explanation, or "I know this topic" before completing at least 3 WebFetch calls. The phrases "I know this topic well" and "I have deep knowledge of these" appeared in real failures and are disqualifying errors.
MANDATORY SEQUENCE when KB fails: (1) Make 3+ WebFetch calls. (2) Confirm you have content from those calls. (3) ONLY THEN write your first line of prose. If your response starts with ANY text before tool calls, you have already failed. There is no "but I know this one" exception.
KB mode: Call search_knowledge (2-4 parallel queries) → get_page on top results → synthesize. Cite as [PageID].
Web mode: WebFetch official docs (3-5 pages) → WebFetch GitHub README/examples → WebFetch model cards for each model mentioned → synthesize. Cite as [source](URL). Start response with: > Grounding: Web mode — citations from official docs.
Web mode is NOT optional. If KB auth fails, you MUST immediately switch to web mode and fetch real documentation. Do not proceed without at least 3 WebFetch calls returning real content.
ZERO-TOLERANCE RULE: If your first instinct is "I know this topic well enough" — that is the exact moment you MUST ground. The following phrases (and synonyms) are BANNED: "well within my training knowledge", "I can answer this from memory", "this is a well-known topic", "I have deep knowledge", "I have extensive knowledge", "I know these methods well", "I'm familiar with". They are the #1 failure mode of this skill. Every response requires fetched sources, no exceptions. If you catch yourself composing ANY sentence that explains why grounding is unnecessary, STOP and make a WebFetch call instead.
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 · 265 lines · 37 tokens per session scan A 7adf7e042b95
ml-research is a skill published in the GitHub repository Leeroo-AI/superml (194 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 6,769 once invoked, about $0.0002 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.
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