Zurück zu Fähigkeiten

setup-gxp-r-project

pjt222
Aktualisiert Yesterday
3 Ansichten
17
2
17
Auf GitHub ansehen
Metaworddesign

Über

Diese Fähigkeit erstellt eine R-Projektstruktur, die mit GxP-Vorschriften wie 21 CFR Part 11 konform ist, und automatisiert die Einrichtung für validierte Umgebungen, Qualifizierungsdokumente und elektronische Aufzeichnungen. Verwenden Sie diese Funktion bei der Initiierung von R-Analyseprojekten in regulierten Pharma- oder Biotech-Umgebungen, um die Prüfbereitschaft sicherzustellen. Sie behandelt Anforderungen für klinische Studien, regulatorische Einreichungen und Änderungskontrolle.

Schnellinstallation

Claude Code

Empfohlen
Primär
npx skills add pjt222/agent-almanac -a claude-code
Plugin-BefehlAlternativ
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternativ
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/setup-gxp-r-project

Kopieren Sie diesen Befehl und fügen Sie ihn in Claude Code ein, um diese Fähigkeit zu installieren

Dokumentation

Set Up GxP R Project

Make R project structure that meets GxP regulatory requirements for validated computing.

When Use

  • Start R analysis project in regulated env (pharma, biotech, medical devices)
  • Set up R for clinical trial analysis
  • Make validated computing env for regulatory submissions
  • Implement 21 CFR Part 11 or EU Annex 11 requirements

Inputs

  • Required: Project scope + regulatory framework (FDA, EMA, both)
  • Required: R version + package versions to validate
  • Required: Validation strategy (risk-based)
  • Optional: Existing SOPs for computerized systems
  • Optional: QMS integration requirements

Steps

Step 1: Create Validated Project Structure

gxp-project/
├── R/                          # Analysis scripts
│   ├── 01_data_import.R
│   ├── 02_data_processing.R
│   └── 03_analysis.R
├── validation/                 # Validation documentation
│   ├── validation_plan.md      # VP: scope, strategy, roles
│   ├── risk_assessment.md      # Risk categorization
│   ├── iq/                     # Installation Qualification
│   │   ├── iq_protocol.md
│   │   └── iq_report.md
│   ├── oq/                     # Operational Qualification
│   │   ├── oq_protocol.md
│   │   └── oq_report.md
│   ├── pq/                     # Performance Qualification
│   │   ├── pq_protocol.md
│   │   └── pq_report.md
│   └── traceability_matrix.md  # Requirements to tests mapping
├── tests/                      # Automated test suite
│   ├── testthat.R
│   └── testthat/
│       ├── test-data_import.R
│       └── test-analysis.R
├── data/                       # Input data (controlled)
│   ├── raw/                    # Immutable raw data
│   └── derived/                # Processed datasets
├── output/                     # Analysis outputs
├── docs/                       # Supporting documentation
│   ├── sop_references.md       # Links to relevant SOPs
│   └── change_log.md           # Manual change documentation
├── renv.lock                   # Locked dependencies
├── DESCRIPTION                 # Project metadata
├── .Rprofile                   # Session configuration
└── CLAUDE.md                   # AI assistant instructions

Got: Complete dir structure exists with R/, validation/ (including iq/, oq/, pq/ subdirs), tests/testthat/, data/raw/, data/derived/, output/, docs/ dirs.

If fail: Dirs missing? Create with mkdir -p. Verify in correct project root. For existing projects, create only missing dirs rather than overwriting.

Step 2: Create Validation Plan

Create validation/validation_plan.md.

# Validation Plan

## 1. Purpose
This plan defines the validation strategy for [Project Name] using R [version].

## 2. Scope
- R version: 4.5.0
- Packages: [list with versions]
- Analysis: [description]
- Regulatory framework: 21 CFR Part 11 / EU Annex 11

## 3. Risk Assessment Approach
Using GAMP 5 risk-based categories:
- Category 3: Non-configured products (R base)
- Category 4: Configured products (R packages with default settings)
- Category 5: Custom applications (custom R scripts)

## 4. Validation Activities
| Activity | Category 3 | Category 4 | Category 5 |
|----------|-----------|-----------|-----------|
| IQ | Required | Required | Required |
| OQ | Reduced | Standard | Enhanced |
| PQ | N/A | Standard | Enhanced |

## 5. Roles and Responsibilities
- Validation Lead: [Name]
- Developer: [Name]
- QA Reviewer: [Name]
- Approver: [Name]

## 6. Acceptance Criteria
All tests must pass with documented evidence.

Got: validation/validation_plan.md complete with scope, GAMP 5 risk categories, validation activities matrix, roles + responsibilities, acceptance criteria. Plan references specific R version + regulatory framework.

If fail: Regulatory framework unclear? Consult org's QA dept for applicable SOPs. Do not proceed with validation activities until plan reviewed + approved.

Step 3: Lock Dependencies with renv

# Initialize renv with exact versions
renv::init()

# Install specific validated versions
renv::install("[email protected]")
renv::install("[email protected]")

# Snapshot
renv::snapshot()

renv.lock file serves as controlled package inventory.

Got: renv.lock exists with exact version numbers for all required packages. renv::status() reports no issues. Every package version pinned (e.g., [email protected]), not floating.

If fail: renv::install() fails for specific version? Check version exists on CRAN archives. Use renv::install("package@version", repos = "https://packagemanager.posit.co/cran/latest") for archived versions.

Step 4: Implement Version Control

git init
git add .
git commit -m "Initial validated project structure"

# Use signed commits for traceability
git config user.signingkey YOUR_GPG_KEY
git config commit.gpgsign true

Got: Project under git version control with signed commits enabled. Initial commit contains validated project structure + renv.lock.

If fail: GPG signing fails? Verify GPG key configured with gpg --list-secret-keys. For envs without GPG, document deviation, use unsigned commits with manual audit trail entries in docs/change_log.md.

Step 5: Create IQ Protocol

validation/iq/iq_protocol.md.

# Installation Qualification Protocol

## Objective
Verify that R and required packages are correctly installed.

## Test Cases

### IQ-001: R Version Verification
- **Requirement**: R 4.5.0 installed
- **Procedure**: Execute `R.version.string`
- **Expected:** "R version 4.5.0 (date)"
- **Result**: [ PASS / FAIL ]

### IQ-002: Package Installation Verification
- **Requirement**: All packages in renv.lock installed
- **Procedure**: Execute `renv::status()`
- **Expected:** "No issues found"
- **Result**: [ PASS / FAIL ]

### IQ-003: Package Version Verification
- **Procedure**: Execute `installed.packages()[, c("Package", "Version")]`
- **Expected:** Versions match renv.lock exactly
- **Result**: [ PASS / FAIL ]

Got: validation/iq/iq_protocol.md contains test cases for R version verification, package install verification, package version verification, each with clear expected results + pass/fail fields.

If fail: IQ protocol template does not match org SOP requirements? Adapt format while keeping required fields (requirement, procedure, expected, actual, pass/fail). Consult QA for approved templates.

Step 6: Write Automated OQ/PQ Tests

# tests/testthat/test-analysis.R
test_that("primary analysis produces validated results", {
  # Known input -> known output (double programming validation)
  test_data <- read.csv(test_path("fixtures", "validation_dataset.csv"))

  result <- primary_analysis(test_data)

  # Compare against independently calculated expected values
  expect_equal(result$estimate, 2.345, tolerance = 1e-3)
  expect_equal(result$p_value, 0.012, tolerance = 1e-3)
  expect_equal(result$ci_lower, 1.234, tolerance = 1e-3)
})

Got: Automated test files in tests/testthat/ covering OQ (operational verification of each function) + PQ (end-to-end validation against independent reference values). Tests use explicit numeric tolerances.

If fail: Reference values not yet available from independent calc (e.g., SAS)? Create placeholder tests with skip("Awaiting independent reference values"), document in traceability matrix.

Step 7: Create Traceability Matrix

# Traceability Matrix

| Req ID | Requirement | Test ID | Test Description | Status |
|--------|-------------|---------|------------------|--------|
| REQ-001 | Import CSV data correctly | OQ-001 | Verify data dimensions and types | PASS |
| REQ-002 | Calculate primary endpoint | PQ-001 | Compare against reference results | PASS |
| REQ-003 | Generate report output | PQ-002 | Verify report contains all sections | PASS |

Got: validation/traceability_matrix.md links every requirement to at least one test case, every test linked to a requirement. No orphaned requirements or tests.

If fail: Requirements untested? Create test cases or document risk-based justification for exclusion. Tests with no linked requirement? Either link to existing requirement or remove as out-of-scope.

Checks

  • Project structure follows documented template
  • renv.lock contains all deps with exact versions
  • Validation plan complete + approved
  • IQ protocol executes successfully
  • OQ test cases cover all configured functionality
  • PQ tests validate against independently computed results
  • Traceability matrix links requirements to tests
  • Change control process documented

Pitfalls

  • Use install.packages() without version pinning: Always use renv with locked versions
  • Missing audit trail: Every change must be documented. Use git signed commits.
  • Over-validating: Apply risk-based approach. Not every CRAN package needs Category 5 validation.
  • Forget system-level qualification: OS + R installation need IQ too
  • No independent verification: PQ should compare against results computed independently (SAS, manual calc)

See Also

  • write-validation-documentation - detailed validation document creation
  • implement-audit-trail - electronic records + audit trails
  • validate-statistical-output - double programming + output validation
  • manage-renv-dependencies - dep locking for validated envs

GitHub Repository

pjt222/agent-almanac
Pfad: i18n/caveman/skills/setup-gxp-r-project
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

Verwandte Skills

content-collections

Meta

Diese Skill bietet eine produktionsgetestete Einrichtung für Content Collections – ein TypeScript-first-Tool, das Markdown/MDX-Dateien in typsichere Datensammlungen mit Zod-Validierung umwandelt. Verwenden Sie ihn beim Erstellen von Blogs, Dokumentationsseiten oder inhaltsstarken Vite + React-Anwendungen, um Typsicherheit und automatische Inhaltsvalidierung zu gewährleisten. Er behandelt alles von der Vite-Plugin-Konfiguration und MDX-Kompilierung bis hin zur Deployment-Optimierung und Schema-Validierung.

Skill ansehen

polymarket

Meta

Diese Fähigkeit ermöglicht es Entwicklern, Anwendungen mit der Polymarket-Prognosemärkte-Plattform zu erstellen, einschließlich API-Integration für Handel und Marktdaten. Sie bietet außerdem Echtzeit-Datenstreaming über WebSocket, um Live-Trades und Marktaktivitäten zu überwachen. Nutzen Sie sie zur Implementierung von Handelsstrategien oder zur Erstellung von Tools, die Live-Marktaktualisierungen verarbeiten.

Skill ansehen

creating-opencode-plugins

Meta

Diese Fähigkeit unterstützt Entwickler dabei, OpenCode-Plugins zu erstellen, die in über 25 Ereignistypen wie Befehle, Dateien und LSP-Operationen eingreifen. Sie bietet die Plugin-Struktur, Event-API-Spezifikationen und Implementierungsmuster für JavaScript/TypeScript-Module. Nutzen Sie sie, wenn Sie den Lebenszyklus des OpenCode KI-Assistenten mit benutzerdefinierter ereignisgesteuerter Logik abfangen, überwachen oder erweitern müssen.

Skill ansehen

sglang

Meta

SGLang ist ein hochperformantes LLM-Serving-Framework, das sich auf schnelle, strukturierte Generierung für JSON, Regex und agentenbasierte Workflows unter Verwendung seines RadixAttention-Prefix-Cachings spezialisiert. Es bietet deutlich schnellere Inferenz, insbesondere für Aufgaben mit wiederholten Präfixen, was es ideal für komplexe, strukturierte Ausgaben und Mehrfachdialoge macht. Wählen Sie SGLang gegenüber Alternativen wie vLLM, wenn Sie constrained decoding benötigen oder Anwendungen mit umfangreicher Präfix-Weitergabe entwickeln.

Skill ansehen