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 DNYoussef/context-cascade --skill ml-expertgit clone --depth 1 https://github.com/DNYoussef/context-cascadeWrote 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/dnyoussef/context-cascade/ml-expert)<a href="https://agentmods.dev/skills/dnyoussef/context-cascade/ml-expert"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/ml-expert/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/dnyoussef/context-cascade/ml-expert"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/ml-expert.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 | $0.00000 | $0.01836 |
| Opus 5.5 | $0.00000 | $0.00734 |
| Sonnet 5.5 | $0.00000 | $0.00367 |
| Haiku 4.5 | $0.00000 | $0.00184 |
Grade A, and why
ml-expert 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 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.
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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/============================================================================/ /* ML-EXPERT SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/
name: ml-expert version: 1.0.0 description: | [assert|neutral] Version: 1.0.0 [ground:given] [conf:0.95] [state:confirmed] category: specialists tags:
- specialists
- domain-expert author: ruv cognitive_frame: primary: honorific goal_analysis: first_order: "Execute ml-expert workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic specialists processes"
/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/
[define|neutral] SKILL := { name: "ml-expert", category: "specialists", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/
[define|neutral] COGNITIVE_FRAME := { frame: "Honorific", source: "Japanese", force: "Who is the audience?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/
[define|neutral] TRIGGER_POSITIVE := { keywords: ["ml-expert", "specialists", "workflow"], context: "user needs ml-expert capability" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/
What ships with it
16 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.
- agents/ml-expert-specialist.prompt 18 KB
- examples/ensemble-methods-example.sh 14 KB runs code
- examples/feature-engineering-example.js 14 KB runs code
- examples/neural-architecture-example.py 14 KB runs code
- manifest.json 1.1 KB
- readme.md 5.1 KB
- resources/architecture-template.yaml 2.6 KB
- resources/ensemble-builder.sh 11 KB runs code
- resources/ensemble-strategy.yaml 3.4 KB
- resources/feature-config.json 3.0 KB
- resources/feature-engineer.js 11 KB runs code
- resources/ml-debugger.py 11 KB runs code
- resources/model-architect.py 12 KB runs code
- tests/test_ensemble_builder.py 6.0 KB runs code
- tests/test_feature_engineer.py 6.5 KB runs code
- tests/test_model_architect.py 8.6 KB runs code
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 · 221 lines · 0 tokens per session scan A ff5991045d70
ml-expert is a skill published in the GitHub repository DNYoussef/context-cascade (32 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,836 tokens. 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 2026-09-03.
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