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mlops-validation

Add the validation layers that gate a merge — ty typing, Ruff linting, pytest coverage, structured logging, and the trivy, pip-audit, and gitleaks scans. Use when hardening code quality or wiring the mise run check task.

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Preis unbestätigt★ 22 GitHub-StarsVerzeichnis aktualisiert · 13. Sept. 2026agent-skill

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MLOps Validation

Goal

To ensure software quality, reliability, and security through automated validation layers. This skill enforces Strict Typing (ty), Unified Linting (ruff), Comprehensive Testing (pytest), Structured Logging, and Supply-Chain Scanning (pip-audit, gitleaks, trivy).

Prerequisites

Language: Python 3.14
Manager: uv; Tasks: mise
Context: Ensuring code quality before merge/deploy.

Instructions

1. The Task Vocabulary

Every check below is a mise task, so the same command runs locally, in the git hook, and in CI. Never invoke the underlying tool by hand in a hook or a workflow — the task is the single definition.

Task	Tool	What it proves
`check:format`	`dprint`, `validate-pyproject`, `ruff format --check`, `uv lock --check`	Files and manifests are canonical and the lockfile is current.
`check:lint`	`ruff check`	No lint violation, including the `S` (security) rules.
`check:types`	`ty check`	Type annotations are consistent.
`check:vuln`	`pip-audit`	No known CVE in the resolved Python dependencies.
`check:leaks`	`gitleaks`	No secret in the staged change or the recent history.
`check:scan`	`trivy`	No misconfiguration, leaked secret, or forbidden license in the tree.
`check:actions`	`actionlint`, `zizmor`	Workflows are valid and not vulnerable.
`test`	`pytest`	Behavior is covered and correct.

mise run check runs the check:* tasks in parallel; mise run all chains format -> check -> test -> build and is the only gate anyone needs to remember.

2. Static Analysis (Typing & Linting)

Catch errors before they run.

Typing:
- Tool: ty (Astral type checker; pre-1.0, pin a compatible range such as ty>=0.0.69,<0.1). The mandated checker — do not use mypy.
- Rule: No Any (unless absolutely necessary). Fully typed function signatures.
- Pragmatism: ty does not yet model every dynamic library (MLflow, pandera, Pydantic). Silence the specific rule categories they trigger in [tool.ty.rules], with a comment saying why — never disable the checker wholesale.
- DataFrames: Use pandera schemas to validate DataFrame structures/types.
- Classes: Use pydantic for data modeling and runtime validation.
Linting & Formatting:
- Tool: ruff 0.16+ (replaces black, isort, pylint, flake8, bandit).
- Rule: Zero tolerance for linter errors. Use noqa sparingly and with justification.
- Config: Centralize in pyproject.toml, with an explicit [tool.ruff.lint] select list. Ruff 0.16 expanded the default rule set from 59 to 413 rules, so a project that relies on the default set changes behavior on upgrade while an explicit select list does not.
- Markdown: Ruff 0.16 also formats Python code blocks inside .md files. Expect a one-time reformat of your documentation on the first run, and commit it.

3. Testing Strategy

Verify behavior and prevent regressions.

Tool: pytest (9.x).
Structure: Mirror src/ in tests/.
```
src/pkg/mod.py -> tests/test_mod.py
```
Fixtures: Use tests/conftest.py for shared setup (mock data, temp paths).
Coverage: Measure with pytest-cov and set --cov-fail-under to the level the suite actually reaches, so any drop is a visible regression rather than slack under a round number.

Pattern: Use Given-When-Then in comments.

def test_pipeline_execution(input_data):
    # Given: Valid input data
    # When: The pipeline processes the data
    # Then: The output content matches expectations

MLflow in tests: Point the tests at a SQLite tracking store, not the deprecated file store — but build it once. Creating a fresh MLflow SQLite database runs the full Alembic migration chain (measured at roughly 7 seconds per database), so a session-scoped fixture should migrate one template database and each test should shutil.copyfile it into its own tmp_path (roughly 0.07 seconds). Migrating per test turned a 34-second suite into a 339-second one.

4. Structured Logging

Enable observability and debugging.

Tool: loguru, configured through a small logging service so sinks and levels stay configurable. The wider house standard for long-lived services is structlog; both emit structured records, so choose one per project and stay with it — running both splits the log stream and doubles the configuration surface.
Format: Use structured logging (JSON) in production for queryability.
Levels:
- DEBUG: Low-level tracing (payloads, internal state).
- INFO: Key business events (Job started, Model saved).
- ERROR: Actionable failures (with stack traces).
Context: Include context (Job ID, Model Version) in logs.
Discipline: No bare print in library code — Ruff's T20 rules enforce it.

5. Security

Protect the supply chain and runtime.

Code Scanning: Enable Ruff S (flake8-bandit) rules to detect unsafe patterns (e.g., eval, yaml.load) — this replaces standalone bandit, and runs inside check:lint.
Dependencies: check:vuln runs pip-audit --skip-editable against the resolved environment; Dependabot opens the update pull requests on a weekly schedule.
Secret Scanning: check:leaks runs gitleaks. Scan the staged change in the pre-commit hook (--staged) and the recent history in CI (--log-opts="--max-count=100"); a scheduled workflow rescans the full history weekly, because a secret committed and later removed is invisible to a shallow scan forever.
Filesystem Scanning: check:scan runs trivy --config trivy.yaml fs . — one pass covering vulnerabilities, misconfigurations (Dockerfile, IaC), secrets, and license compliance. Two details matter: pass --config explicitly, or a TRIVY_CONFIG exported in a developer's shell silently overrides the committed policy; and use the fs subcommand, because trivy config only runs the misconfiguration scanner and quietly skips the rest.
Workflow Scanning: check:actions runs actionlint (syntax, shell) and zizmor (workflow security: injection, over-broad permissions, credential persistence).
Secrets: NEVER log secrets. Sanitize outputs.

Self-Correction Checklist

Type Safety: Does mise run check:types pass?
Lint Cleanliness: Does mise run check:lint pass with an explicit select list?
Test Discovery: Does pytest successfully find modules in src/?
Test Speed: Is the MLflow store built once and copied, not migrated per test?
Log Format: Are production logs serializing to JSON, from one logging library?
Security: Do check:lint (Ruff S), check:vuln, check:leaks, check:scan, and check:actions all pass?
Gate: Does mise run all pass end to end?

Quelle ansehen ↗

Dateimetadaten

name: mlops-validation
description: Add the validation layers that gate a merge — ty typing, Ruff linting, pytest coverage, structured logging, and the trivy, pip-audit, and gitleaks scans. Use when hardening code quality or wiring the mise run check task.
license: MIT
metadata:
  author: Médéric HURIER (Fmind)
  source: github.com/MLOps-Courses/mlops-coding-skills/tree/main/mlops-validation
  created: 2026-01-25
  updated: 2026-08-10

Originaltext anzeigen

---
name: mlops-validation
description: Add the validation layers that gate a merge — ty typing, Ruff linting, pytest coverage, structured logging, and the trivy, pip-audit, and gitleaks scans. Use when hardening code quality or wiring the mise run check task.
license: MIT
metadata:
  author: Médéric HURIER (Fmind)
  source: github.com/MLOps-Courses/mlops-coding-skills/tree/main/mlops-validation
  created: 2026-01-25
  updated: 2026-08-10
---

# MLOps Validation

## Goal

To ensure software quality, reliability, and security through automated validation layers. This skill enforces **Strict Typing** (`ty`), **Unified Linting** (`ruff`), **Comprehensive Testing** (`pytest`), **Structured Logging**, and **Supply-Chain Scanning** (`pip-audit`, `gitleaks`, `trivy`).

## Prerequisites

- **Language**: Python 3.14
- **Manager**: `uv`; **Tasks**: `mise`
- **Context**: Ensuring code quality before merge/deploy.

## Instructions

### 1. The Task Vocabulary

Every check below is a `mise` task, so the same command runs locally, in the git hook, and in CI. Never invoke the underlying tool by hand in a hook or a workflow — the task is the single definition.

| Task            | Tool                                                                     | What it proves                                                        |
| --------------- | ------------------------------------------------------------------------ | --------------------------------------------------------------------- |
| `check:format`  | `dprint`, `validate-pyproject`, `ruff format --check`, `uv lock --check` | Files and manifests are canonical and the lockfile is current.        |
| `check:lint`    | `ruff check`                                                             | No lint violation, including the `S` (security) rules.                |
| `check:types`   | `ty check`                                                               | Type annotations are consistent.                                      |
| `check:vuln`    | `pip-audit`                                                              | No known CVE in the resolved Python dependencies.                     |
| `check:leaks`   | `gitleaks`                                                               | No secret in the staged change or the recent history.                 |
| `check:scan`    | `trivy`                                                                  | No misconfiguration, leaked secret, or forbidden license in the tree. |
| `check:actions` | `actionlint`, `zizmor`                                                   | Workflows are valid and not vulnerable.                               |
| `test`          | `pytest`                                                                 | Behavior is covered and correct.                                      |

`mise run check` runs the `check:*` tasks in parallel; `mise run all` chains `format` -> `check` -> `test` -> `build` and is the only gate anyone needs to remember.

### 2. Static Analysis (Typing & Linting)

Catch errors before they run.

1. **Typing**:
   - **Tool**: `ty` (Astral type checker; pre-1.0, pin a compatible range such as `ty>=0.0.69,<0.1`). The mandated checker — do not use `mypy`.
   - **Rule**: No `Any` (unless absolutely necessary). Fully typed function signatures.
   - **Pragmatism**: `ty` does not yet model every dynamic library (MLflow, pandera, Pydantic). Silence the specific rule categories they trigger in `[tool.ty.rules]`, with a comment saying why — never disable the checker wholesale.
   - **DataFrames**: Use `pandera` schemas to validate DataFrame structures/types.
   - **Classes**: Use `pydantic` for data modeling and runtime validation.
1. **Linting & Formatting**:
   - **Tool**: `ruff` 0.16+ (replaces black, isort, pylint, flake8, bandit).
   - **Rule**: Zero tolerance for linter errors. Use `noqa` sparingly and with justification.
   - **Config**: Centralize in `pyproject.toml`, with an explicit `[tool.ruff.lint] select` list. Ruff 0.16 expanded the _default_ rule set from 59 to 413 rules, so a project that relies on the default set changes behavior on upgrade while an explicit `select` list does not.
   - **Markdown**: Ruff 0.16 also formats Python code blocks inside `.md` files. Expect a one-time reformat of your documentation on the first run, and commit it.

### 3. Testing Strategy

Verify behavior and prevent regressions.

1. **Tool**: `pytest` (9.x).
1. **Structure**: Mirror `src/` in `tests/`.

   ```text
   src/pkg/mod.py -> tests/test_mod.py
   ```

1. **Fixtures**: Use `tests/conftest.py` for shared setup (mock data, temp paths).
1. **Coverage**: Measure with `pytest-cov` and set `--cov-fail-under` to the level the suite actually reaches, so any drop is a visible regression rather than slack under a round number.
1. **Pattern**: Use **Given-When-Then** in comments.

   ```python
   def test_pipeline_execution(input_data):
       # Given: Valid input data
       # When: The pipeline processes the data
       # Then: The output content matches expectations
   ```

1. **MLflow in tests**: Point the tests at a SQLite tracking store, not the deprecated file store — but build it once. Creating a fresh MLflow SQLite database runs the full Alembic migration chain (measured at roughly 7 seconds per database), so a session-scoped fixture should migrate one template database and each test should `shutil.copyfile` it into its own `tmp_path` (roughly 0.07 seconds). Migrating per test turned a 34-second suite into a 339-second one.

### 4. Structured Logging

Enable observability and debugging.

1. **Tool**: `loguru`, configured through a small logging service so sinks and levels stay configurable. The wider house standard for long-lived services is `structlog`; both emit structured records, so choose one per project and stay with it — running both splits the log stream and doubles the configuration surface.
1. **Format**: Use structured logging (JSON) in production for queryability.
1. **Levels**:
   - `DEBUG`: Low-level tracing (payloads, internal state).
   - `INFO`: Key business events (Job started, Model saved).
   - `ERROR`: Actionable failures (with stack traces).
1. **Context**: Include context (Job ID, Model Version) in logs.
1. **Discipline**: No bare `print` in library code — Ruff's `T20` rules enforce it.

### 5. Security

Protect the supply chain and runtime.

1. **Code Scanning**: Enable Ruff `S` (flake8-bandit) rules to detect unsafe patterns (e.g., `eval`, `yaml.load`) — this replaces standalone `bandit`, and runs inside `check:lint`.
1. **Dependencies**: `check:vuln` runs `pip-audit --skip-editable` against the resolved environment; **Dependabot** opens the update pull requests on a weekly schedule.
1. **Secret Scanning**: `check:leaks` runs `gitleaks`. Scan the staged change in the pre-commit hook (`--staged`) and the recent history in CI (`--log-opts="--max-count=100"`); a scheduled workflow rescans the **full** history weekly, because a secret committed and later removed is invisible to a shallow scan forever.
1. **Filesystem Scanning**: `check:scan` runs `trivy --config trivy.yaml fs .` — one pass covering vulnerabilities, misconfigurations (Dockerfile, IaC), secrets, and license compliance. Two details matter: pass `--config` explicitly, or a `TRIVY_CONFIG` exported in a developer's shell silently overrides the committed policy; and use the `fs` subcommand, because `trivy config` only runs the misconfiguration scanner and quietly skips the rest.
1. **Workflow Scanning**: `check:actions` runs `actionlint` (syntax, shell) and `zizmor` (workflow security: injection, over-broad permissions, credential persistence).
1. **Secrets**: **NEVER** log secrets. Sanitize outputs.

## Self-Correction Checklist

- [ ] **Type Safety**: Does `mise run check:types` pass?
- [ ] **Lint Cleanliness**: Does `mise run check:lint` pass with an explicit `select` list?
- [ ] **Test Discovery**: Does `pytest` successfully find modules in `src/`?
- [ ] **Test Speed**: Is the MLflow store built once and copied, not migrated per test?
- [ ] **Log Format**: Are production logs serializing to JSON, from one logging library?
- [ ] **Security**: Do `check:lint` (Ruff `S`), `check:vuln`, `check:leaks`, `check:scan`, and `check:actions` all pass?
- [ ] **Gate**: Does `mise run all` pass end to end?

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: MIT
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstStatisch geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: MLOps-Courses/mlops-coding-skills
Lizenz: MIT
Version: Unknown
Letzter GitHub-Push: 10. Aug. 2026
Verzeichnis aktualisiert: 13. Sept. 2026
Anleitungspfad: mlops-validation/SKILL.md @ 4a146e6c4d47

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

49/100

Prüfung nötig

Vertrauen

55/100

Do not auto-install

Audit

67/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

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Weitere Details

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      "documentation": "Usable metadata, review docs",
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      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "security",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 22 GitHub stars",
      "Stars/forks activity: 22 stars, 4 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 67,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 22 GitHub stars",
      "Stars/forks activity: 22 stars, 4 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 49,
    "label": "Needs review"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use mlops-validation in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 63/100 Manual review",
      "Audit: 67/100 Needs review",
      "Safety: 23/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "mlops-courses-mlops-validation (mlops-validation)",
      "install_command": "npx skills add MLOps-Courses/mlops-coding-skills --skill mlops-validation",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "mlops-courses-mlops-validation",
      "task": "Use mlops-validation in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/mlops-courses-mlops-validation",
    "api": "https://www.openagentskill.com/api/agent/skills/mlops-courses-mlops-validation",
    "audit": "https://www.openagentskill.com/skills/mlops-courses-mlops-validation/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=mlops-courses-mlops-validation&task=Use%20mlops-validation%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20mlops-validation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20mlops-validation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/mlops-courses-mlops-validation/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/mlops-courses-mlops-validation"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: Médéric HURIER (Fmind)
Quelle: MLOps-Courses/mlops-coding-skills
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

Diesen Skill beanspruchen

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Diesen Skill-Eintrag beanspruchen

Dieser Registry-indexiert-Eintrag wird Médéric HURIER (Fmind) zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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