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-debuggit 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-debug)<a href="https://agentmods.dev/skills/leeroo-ai/superml/ml-debug"><img src="https://agentmods.dev/badge/skills/leeroo-ai/superml/ml-debug/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/leeroo-ai/superml/ml-debug"><img src="https://agentmods.dev/badge/skills/leeroo-ai/superml/ml-debug.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 | $0.00032 | $0.09132 |
| Opus 5.5 | $0.00013 | $0.03653 |
| Sonnet 5.5 | $0.00006 | $0.01826 |
| Haiku 4.5 | $0.00003 | $0.00913 |
Grade A, and why
ml-debug 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 1mo 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.
| **Verification script that doesn't match the failure mode** | Serving fix verified with single curl; OOM fix verified with "run and see if it crashes" | Match verification to the failure: serving → concurrent load test How it starts
The opening of the file, as written. The whole thing — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Debugging
Systematically diagnose ML failures using framework-specific knowledge, not guesswork.
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.
HARD RULE: You MUST ground before writing analysis. If KB fails, you MUST WebFetch at least 2 URLs before writing ANY diagnosis. Writing from memory without fetching is the #1 failure mode of this skill — it produces zero-citation responses that score 0/3 on grounding. "I know X well" is NOT a substitute for fetching documentation.
KB mode: Call diagnose_failure → query_hyperparameter_priors → search_knowledge. Cite as [PageID].
KB mode grounding supplement (MANDATORY): After KB calls, you MUST WebFetch at least 2 public URLs (official docs, PyPI, GitHub issues/source) and cite them as [source](URL) alongside [PageID] citations. KB-only responses score 2/3 max on grounding because reviewers cannot verify proprietary page IDs. Pattern: KB call for diagnosis → WebFetch PyPI for version → WebFetch GitHub/docs for config verification → cite BOTH KB and public URLs in every section. Self-test: if your response has zero [source](URL) citations, you will lose a grounding point regardless of KB citation count.
Web mode: WebFetch GitHub issues for the error message → WebFetch framework troubleshooting docs → WebFetch config references. Cite as [source](URL). Start response with: > Grounding: Web mode — citations from official docs and GitHub issues.
Web mode grounding targets by response section (aim for these counts):
- Diagnosis root cause: 1+ citation (to a specific doc section or GitHub source line, NOT a top-level page)
- Each "Why it matters" explanation: 1+ citation or
[no KB] - Each fix step: 1+ citation for the specific API/config being changed
- Each quantitative claim ("X× faster"): 1 citation or
[no KB] - Prevention items: 1+ citation for the metric/tool referenced
Target: 5+ total citations in web mode, each linking to a specific doc section or source line (not top-level domain pages). Below 3 is a grounding failure. Generic page links (e.g.,
huggingface.co/docs/transformers) score lower than specific section links (e.g.,huggingface.co/docs/transformers/model_doc/mixtral#MixtralConfig).
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.
- 1mo ago First seen · 348 lines · 32 tokens per session scan A b6bf218fccb1
ml-debug is a skill published in the GitHub repository Leeroo-AI/superml (196 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 9,132 once invoked, about $0.0001 per session on Opus 5.5. 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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