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 seb1n/awesome-ai-agent-skills --skill ml-pipeline-creationgit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skillsWrote 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/seb1n/awesome-ai-agent-skills/ml-pipeline-creation)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/ml-pipeline-creation"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/ml-pipeline-creation/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/seb1n/awesome-ai-agent-skills/ml-pipeline-creation"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/ml-pipeline-creation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00061 | $0.01153 |
| Opus 5 | $0.00030 | $0.00576 |
| Sonnet 5 | $0.00012 | $0.00231 |
| Haiku 4.5 | $0.00006 | $0.00115 |
Grade A, and why
ml-pipeline-creation 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 13d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Pipeline Creation
Build reproducible ML workflows whose inputs, outputs, lineage, and promotion criteria are explicit. Prefer the project's existing orchestrator and conventions; do not introduce a platform merely to demonstrate one.
Required Inputs
- Business objective and measurable model acceptance criteria
- Data sources, ownership, sensitivity, and expected refresh cadence
- Existing preprocessing, training, evaluation, and serving code
- Target environments and available orchestration or CI system
- Compute, cost, latency, reproducibility, and compliance constraints
If critical details are missing, state assumptions and design a platform-neutral pipeline before selecting an implementation.
Output Contract
Produce:
- A dependency graph of pipeline stages and artifacts
- A versioned pipeline definition or implementation
- Explicit schemas for every stage input and output
- Data, model, and environment versioning rules
- Evaluation and promotion gates with failure behavior
- Observability, retry, backfill, and rollback procedures
- A verification record showing how the pipeline was tested
Workflow
- Inspect the environment. Identify the repository language, dependency manager, existing orchestration system, model framework, artifact store, and deployment path. Reuse established tools where possible.
- Define the contract. Record the objective, data snapshot rules, target metric, baseline, acceptance threshold, resource budget, and deployment constraints. Separate offline evaluation from production health metrics.
- Model the DAG. Represent ingestion, validation, splitting, transformation, training, evaluation, registration, and deployment as idempotent stages. Declare every artifact rather than relying on undeclared files or mutable global state.
- Implement reproducibility. Pin dependencies, seed stochastic operations where appropriate, version code and data, capture parameters, and store immutable artifacts with provenance. Prevent train/validation leakage by fitting transformations only on training data.
- Add quality gates. Validate schemas before training, compare metrics with a baseline, fail closed on missing or invalid artifacts, and require explicit approval before production promotion when consequences are material.
- Design operations. Define retries only for transient failures, make reruns idempotent, specify backfill boundaries, emit structured logs and metrics, and document rollback to the last known-good model.
- Test incrementally. Run unit tests for components, a small deterministic end-to-end fixture, and a staging or dry-run execution. Confirm that a failed stage cannot silently publish a model.
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.
- 13d ago First seen · 103 lines · 61 tokens per session scan A e91890f50de8
ml-pipeline-creation is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 61 tokens to every session and 1,153 once invoked, about $0.0003 per session on Opus 5. 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 2026-08-30.
Other skills, from other repositories
shipping-reproducible-results
Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.
diagnosing-ml-failures
Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance…
validating-models-and-claims
Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.
auditing-data-and-ground-truth
Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.
designing-leakage-safe-experiments
Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.
using-data-analysis
Route data analysis and machine learning work to the right skill in this suite. Use when starting any analysis, modeling, validation, or reproducibility task and the matching specialized skill is not yet clear; not needed when one specific skill already clearly applies.