Borrowing it
Nothing to install: this file belongs to terrene-foundation/metis. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/terrene-foundation/metis/main/.claude/skills/34-kailash-ml/SKILL.mdgit clone --depth 1 https://github.com/terrene-foundation/metisWrote 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/terrene-foundation/metis/34-kailash-ml)<a href="https://agentmods.dev/skills/terrene-foundation/metis/34-kailash-ml"><img src="https://agentmods.dev/badge/skills/terrene-foundation/metis/34-kailash-ml/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/terrene-foundation/metis/34-kailash-ml"><img src="https://agentmods.dev/badge/skills/terrene-foundation/metis/34-kailash-ml.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 | $0.00092 | $0.05837 |
| Opus 5 | $0.00046 | $0.02919 |
| Sonnet 5 | $0.00018 | $0.01167 |
| Haiku 4.5 | $0.00009 | $0.00584 |
Grade A, and why
kailash-ml 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.
This is a copy
97% identical to kailash-ml — 82 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kailash ML 1.0.0 — Classical / Deep Learning / RL Lifecycle
Production ML lifecycle framework built on Kailash Core SDK — engine-first km.* verb surface, 18-engine discovery registry, polars-native, ONNX-default serialisation, Agent Tool Discovery for Kaizen integration, wave-released with 6 sibling packages.
1.0.0 Engine-First Surface (Canonical)
Single entry: import kailash_ml as km. Zero-arg construction. 14 lifecycle verbs + 2 discovery verbs grouped in __all__:
import kailash_ml as km
async with km.track("demo") as run: # Group 1 lifecycle
result = await km.train(df, target="y") # Group 1 lifecycle
registered = await km.register(result, name="demo") # Group 1 lifecycle
server = await km.serve("demo@production") # Group 1 lifecycle
# $ kailash-ml-dashboard (separate shell) # Group 1 lifecycle
km.diagnose(model) # Group 1 — DLDiagnostics / RAGDiagnostics / RLDiagnostics
km.watch(model, reference_df) # Group 1 — DriftMonitor
km.seed(42); await km.reproduce(run_id) # Group 1 — reproducibility
await km.resume(run_id) # Group 1 — checkpoint resume
graph = await km.lineage("demo@v1", tenant_id=None) # Group 1 — LineageGraph; ambient tenant via get_current_tenant_id()
await km.rl_train(env, policy) # Group 1 — RL
km.autolog() # Group 1 — sklearn/lgb/Lightning/torch auto-logging
info = km.engine_info("TrainingPipeline") # Group 6 Engine Discovery (agents MUST use this, not imports)
engines = km.list_engines() # Group 6 — 18-engine catalog per §E1.1
Quick Start fingerprint (pinned, regression-tested via ml-engines-v2 §16.3):
c962060cf467cc732df355ec9e1212cfb0d7534a3eed4480b511adad5a9ceb00
21 Canonical Specs
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 428 lines · 92 tokens per session scan A bdb3f5854ab9
kailash-ml is a skill published in the GitHub repository terrene-foundation/metis (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 92 tokens to every session and 5,837 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to kailash-ml, differing in 82 lines, and is treated as a copy.
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