review-recipes

name	review-recipes
description	Use this skill to quality-check CultureMech growth-media and solution recipes — verify schema/structure, MediaIngredientMech ingredient linkages and CHEBI mappings, solution references, data quality (placeholders, units, concentrations), and categorization. Use after creating/editing a recipe, before a KG export, or for periodic maintenance. Issues are graded P1 (blocking) → P4 (optional); P3/P4 can be auto-fixed.
version	1.1.0
tags	["validation","quality-assurance","recipes","media","solutions","ingredients","linkage"]
author	CultureMech Team
created	"2026-03-16T00:00:00.000Z"

Review Recipes Skill

Overview

The Review Recipes skill provides quality assurance for growth-media and solution recipes in CultureMech. It verifies that:

1. Recipe structure is valid — schema compliance, required fields present
2. Ingredient linkages are correct — MediaIngredientMech IDs exist, CHEBI mappings valid
3. Solution references are valid — solutions exist, proper composition
4. Data quality is high — no placeholders, concentrations/units specified
5. Preparation steps are logical — proper sequencing, sterilization appropriate
6. Categorization is correct — medium type, physical state, category match content

Technology stack: LinkML schema validation, MediaIngredientMech integration (ID linkages), recipe fingerprinting (duplicate detection), an auto-fix data-quality pipeline, and cross-reference (solution → ingredient) validation.

Dataset: ~15,450 records — ~15,360 media recipes across 5 categories (bacterial, algae, archaea, fungal, specialized) + ~90 solutions; ~118k ingredient instances; ~1,048 MediaIngredientMech linkages (84.1% solution coverage).

---

When to Use This Skill

Scenario	Workflow	Priority
Post-creation QA	Validate newly created recipes before committing	High
Batch validation	Review all recipes in a category	High
Pre-export check	Ensure KG export quality before release	Critical
Periodic maintenance	Monthly validation after updates	Medium
Ingredient enrichment	Check MediaIngredientMech coverage	Medium
Duplicate detection	Find potential merge candidates	Low
Data quality cleanup	Fix placeholders and missing data	High

IF newly created recipe   → interactive review
IF full category check    → batch review
IF data quality issues    → auto-fix pipeline
IF critical errors        → batch review with P1 filter
IF ingredient linkage     → validate_ingredients.py
IF duplicate detection    → recipe fingerprinting

---

Review Workflows

1. Interactive Review (single recipe)

PYTHONPATH=src python scripts/review_recipe.py "LB_Broth"                                  # by name
PYTHONPATH=src python scripts/review_recipe.py data/normalized_yaml/bacterial/LB_Broth.yaml # by path
PYTHONPATH=src python scripts/review_recipe.py --id CultureMech:015432                      # by ID
PYTHONPATH=src python scripts/review_recipe.py "TAP_Medium" --suggest-fixes                 # with fixes

Output: Rich panel with recipe structure, P1–P4 results, suggested corrections, ingredient linkage % (MediaIngredientMech coverage), and solution-reference validation.

2. Batch Review (category or all)

PYTHONPATH=src python scripts/batch_review_recipes.py \
  --category bacterial --output reports/validation_bacterial_$(date +%Y%m%d) --format md,json,html

PYTHONPATH=src python scripts/batch_review_recipes.py --category solutions --priority P1,P2
PYTHONPATH=src python scripts/batch_review_recipes.py --output reports/validation_all --threads 8
PYTHONPATH=src python scripts/batch_review_recipes.py --medium-type DEFINED --limit 100 --dry-run

Output: validation_report.md, validation_data.json, dashboard.html, plus category-level statistics.

3. Automated Data Quality Fixes

PYTHONPATH=src python scripts/fix_data_quality.py --dry-run                         # preview
PYTHONPATH=src python scripts/fix_data_quality.py --apply                           # all safe (P3/P4)
PYTHONPATH=src python scripts/fix_data_quality.py --apply --types concentration_units
PYTHONPATH=src python scripts/fix_data_quality.py --apply --types placeholders
PYTHONPATH=src python scripts/fix_data_quality.py --apply --types ingredient_names

Safe (auto-applied): standardize concentration units (g/L → G_PER_L), remove placeholder text, normalize ingredient names to MIM preferred terms, fix whitespace/capitalization, add obvious missing water. Unsafe (manual review): change medium_type, merge duplicates, modify preparation steps, change categorization.

4. Claude Code-Assisted Review

/review-recipes                       # interactive
/review-recipes "LB_Broth"            # specific recipe
/review-recipes "DAS_Vitamin_Cocktail" # a solution

Claude loads the YAML, runs validation via RecipeReviewer, explains issues, checks MIM linkages and solution references, proposes corrections with rationale, applies on approval, and updates curation_history.

---

Validation Rules

Issues are graded by priority. Full definitions (checks, impact, fixes) are in reference/validation-rules.md.

Level	Meaning	Action	Target
P1	Critical errors blocking KG export	Fix immediately	0
P2	High-priority warnings needing review	Manual review	< 1%
P3	Medium-priority data quality issues	Auto-correct when possible	< 5%
P4	Low-priority info/suggestions	Optional	Any

Rule	Summary
P1.1	Schema validation failure
P1.2	Invalid CultureMech ID
P1.3	Missing required fields
P1.4	Invalid enum values
P1.5	Broken solution reference
P2.1	Invalid MediaIngredientMech ID
P2.2	Invalid CHEBI ID
P2.3	Concentration mismatch
P2.4	Category mismatch
P2.5	Duplicate recipe
P3.1	Placeholder text
P3.2	Missing MediaIngredientMech linkage
P3.3	Non-standard ingredient name
P3.4	Missing preparation steps
P3.5	Sterilization not specified
P3.6	pH not specified
P4.1	Low MediaIngredientMech coverage
P4.2	Missing target organisms
P4.3	Missing references
P4.4	Incomplete curation history
P4.5	Solution could be extracted

---

Related Skills

• create-recipe — create new media/solution YAML records
• manage-identifiers — CultureMech ID assignment
• manage-ingredient-hierarchy — MediaIngredientMech integration

---

Script Support

scripts/review_recipe.py (interactive), batch_review_recipes.py (batch + reports), fix_data_quality.py (auto-fix), detect_duplicate_recipes.py (fingerprinting), generate_coverage_report.py (MIM coverage), validate_ingredients.py (linkage), enrich_with_mediaingredientmech.py (add MIM IDs).

---

Quick Reference

/review-recipes "recipe_name"                                              # single review
PYTHONPATH=src python scripts/batch_review_recipes.py --category {category} # batch
just fix-all-data-quality                                                  # auto-fix
PYTHONPATH=src python scripts/generate_coverage_report.py                  # coverage
PYTHONPATH=src python scripts/detect_duplicate_recipes.py                  # duplicates
just validate-recipes && just validate-schema && just generate-indexes     # full workflow

Remember: validate before committing, keep solution MediaIngredientMech coverage above 80%, and keep P1 errors at zero.

---

Reference Files

File	Contents
reference/validation-rules.md	Full definitions for all 21 P1–P4 rules: checks, impact, fixes
reference/linkage-and-duplicates.md	MediaIngredientMech coverage checks (single + batch report), cross-reference validation, and recipe fingerprinting / duplicate detection
reference/pipeline-and-patterns.md	Data-quality pipeline (justfile) integration, the four validation patterns (new recipe, solution, batch category, pre-export), and MediaIngredientMech enrichment/sync workflows
reference/operations.md	Error handling, the validation checklist, and interactive/batch output examples