MCP HubMCP Hub
Retour aux compétences

setup-gxp-r-project

pjt222
Mis à jour Yesterday
2 vues
17
2
17
Voir sur GitHub
Métaworddesign

À propos

Cette compétence crée une structure de projet R conforme aux réglementations GxP telles que le 21 CFR Partie 11, automatisant la configuration pour les environnements validés, les documents de qualification et les enregistrements électroniques. Utilisez-la lors du démarrage de projets d'analyse R dans des environnements pharmaceutiques ou biotechnologiques réglementés pour garantir la préparation aux audits. Elle gère les exigences pour les essais cliniques, les soumissions réglementaires et le contrôle des changements.

Installation rapide

Claude Code

Recommandé
Principal
npx skills add pjt222/agent-almanac -a claude-code
Commande PluginAlternatif
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternatif
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/setup-gxp-r-project

Copiez et collez cette commande dans Claude Code pour installer cette compétence

Documentation

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

Dépôt GitHub

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

Compétences associées

content-collections

Méta

Cette compétence propose une configuration éprouvée en production pour Content Collections, un outil axé sur TypeScript qui transforme des fichiers Markdown/MDX en collections de données typées de manière sûre avec une validation Zod. Utilisez-la lors de la création de blogs, de sites de documentation ou d'applications Vite + React riches en contenu pour garantir la sécurité de typage et la validation automatique du contenu. Elle couvre tout, de la configuration du plugin Vite et de la compilation MDX à l'optimisation des déploiements et la validation des schémas.

Voir la compétence

polymarket

Méta

Cette compétence permet aux développeurs de créer des applications avec la plateforme de marchés prédictifs Polymarket, incluant l'intégration d'API pour le trading et les données de marché. Elle fournit également une diffusion de données en temps réel via WebSocket pour surveiller les transactions en direct et l'activité du marché. Utilisez-la pour mettre en œuvre des stratégies de trading ou pour créer des outils traitant les mises à jour de marché en direct.

Voir la compétence

creating-opencode-plugins

Méta

Cette compétence aide les développeurs à créer des plugins OpenCode qui s'interconnectent avec plus de 25 types d'événements tels que les commandes, les fichiers et les opérations LSP. Elle fournit la structure du plugin, les spécifications de l'API événementielle et les modèles d'implémentation pour les modules JavaScript/TypeScript. Utilisez-la lorsque vous avez besoin d'intercepter, de surveiller ou d'étendre le cycle de vie de l'assistant IA OpenCode avec une logique personnalisée pilotée par les événements.

Voir la compétence

sglang

Méta

SGLang est un framework de service LLM haute performance spécialisé dans la génération rapide et structurée pour les workflows JSON, regex et agentiques grâce à son cache de préfixe RadixAttention. Il offre une inférence nettement plus rapide, particulièrement pour les tâches avec des préfixes répétés, ce qui le rend idéal pour les sorties complexes et structurées ainsi que les conversations multi-tours. Choisissez SGLang plutôt que des alternatives comme vLLM lorsque vous avez besoin d'un décodage contraint ou que vous construisez des applications avec un partage étendu de préfixes.

Voir la compétence