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 gongyijie85/dsh-ecc --skill ml-adoption-playbookgit clone --depth 1 https://github.com/gongyijie85/dsh-eccWrote 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/gongyijie85/dsh-ecc/ml-adoption-playbook)<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/ml-adoption-playbook"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/ml-adoption-playbook/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/gongyijie85/dsh-ecc/ml-adoption-playbook"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/ml-adoption-playbook.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 | $0.00069 | $0.00839 |
| Opus 5 | $0.00034 | $0.00419 |
| Sonnet 5 | $0.00014 | $0.00168 |
| Haiku 4.5 | $0.00007 | $0.00084 |
Grade A, and why
ml-adoption-playbook 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
92% identical to ml-adoption-playbook — 2 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Adoption Playbook
This skill provides an adaptive methodology for implementing machine learning models into existing software engineering projects. It bridges the gap between traditional SWE and MLOps by structuring how ML should be researched, decoupled, trained, and integrated.
When to Activate
- A user asks to "add ML" or "add an algorithm" to their existing codebase.
- Planning the integration of a new model (e.g., recommendation, classification, forecasting) into a non-ML application.
- Structuring a workflow for an agent to build, train, and deploy an ML component adaptively.
Phase 1: Problem Framing & Feasibility
Before writing model code, establish the "why" and "how".
- Heuristic Check: Ask the user if a simple heuristic (e.g., regex, rule-based sorting) could solve the problem faster. If yes, start there.
- Metric Definition: Define what business metric the ML model is trying to improve (e.g., click-through rate, reduced latency).
- Mistake Budget: Define what a "bad" prediction looks like and how the system should handle it.
Phase 2: Data Readiness
ML is useless without clean, accessible data.
- Audit Data Sources: Identify where the training data lives. Is it a live database, a static CSV, or an API?
- Data Contract: Establish a schema for the input data. What features are required? What happens if a feature is missing?
- Leakage Prevention: Ensure the user's proposed data split does not accidentally leak future information into the training set (e.g., chronological splitting for time-series data).
Phase 3: Architectural Integration & Decoupling
Do not tightly couple model inference to core business logic.
- API Boundary: Suggest placing the model behind an API endpoint (e.g., using
fastapi-patternsordjango-patterns) or a dedicated service class. - Fallback Mechanisms: Design a default state. If the model takes too long to respond or throws an error, the system must gracefully fall back to a hardcoded rule.
- Feature Flags: Wrap the new ML inference call in a feature flag so it can be rolled out (or rolled back) safely.
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 · 58 lines · 69 tokens per session scan A 488f83a5c376
ml-adoption-playbook is a skill published in the GitHub repository gongyijie85/dsh-ecc (7 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 839 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to ml-adoption-playbook, differing in 2 lines, and is treated as a copy.
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