Is deploy-ai-search-portal safe to install?

SaferSkills independently audited deploy-ai-search-portal (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

solution-plays/09-ai-search-portal/.github/skills/deploy-ai-search-portal/SKILL.md · 1 file

solution-plays/09-ai-search-portal/.github/skills/deploy-ai-search-portal/SKILL.md4.6 KB · Markdown

name: deploy-ai-search-portal

description: "Deploy AI Search Portal — configure index schema, hybrid search, semantic ranking, facets, autocomplete, scoring profiles, indexers. Use when: deploy, provision."

Deploy AI Search Portal

When to Use

Deploy Azure AI Search index with hybrid search capabilities
Configure semantic ranking, facets, and autocomplete
Set up indexers for data ingestion from multiple sources
Configure scoring profiles for relevance tuning
Deploy search portal frontend

Prerequisites

Azure CLI authenticated: az account show
Bicep CLI: az bicep version
Azure AI Search resource (Standard tier for semantic ranking)
Data sources prepared (Blob Storage, SQL, Cosmos DB)
Embedding model deployed (text-embedding-3-large)

Step 1: Validate Infrastructure

az bicep lint -f infra/main.bicep
az bicep build -f infra/main.bicep

Verify resources:

Azure AI Search (Standard S1+ for semantic ranking)
Azure OpenAI (embedding model deployment)
Azure Storage (document source, skillset cache)
Azure App Service / Static Web Apps (portal frontend)
Azure Monitor (search analytics, query logs)

Step 2: Deploy Azure Resources

az deployment group create \
  --resource-group $RESOURCE_GROUP \
  --template-file infra/main.bicep \
  --parameters infra/parameters.json

Step 3: Create Index Schema

Define index fields with appropriate types and attributes:

Field	Type	Searchable	Filterable	Facetable	Sortable
id	Edm.String	—	—	—	—
title	Edm.String	✅	—	—	✅
content	Edm.String	✅	—	—	—
category	Edm.String	—	✅	✅	—
date	Edm.DateTimeOffset	—	✅	—	✅
author	Edm.String	✅	✅	✅	—
contentVector	Collection(Edm.Single)	—	—	—	—

Vector configuration: HNSW algorithm, cosine metric, dimensions matching embedding model (3072 for text-embedding-3-large).

Step 4: Configure Hybrid Search

{
  "search": {
    "queryType": "semantic",
    "semanticConfiguration": "default-semantic",
    "vectorQueries": [{ "kind": "vector", "fields": "contentVector", "k": 5 }],
    "searchFields": "title,content",
    "select": "id,title,content,category,date"
  }
}

Enable all three search modes: BM25 keyword + vector similarity + semantic reranking.

Step 5: Configure Indexers

Source	Indexer Type	Schedule	Delta Detection
Blob Storage	blob	Every 5 min	Change detection
SQL Database	sql	Every 15 min	High watermark
Cosmos DB	cosmosdb	Real-time	Change feed
SharePoint	sharepoint	Every 1 hour	Last modified

Skillset: Add cognitive skills for OCR, language detection, entity extraction, and vectorization.

Step 6: Configure Autocomplete & Suggestions

Enable suggester on title and category fields
Configure autocomplete mode: twoTerms (best balance)
Set minimum prefix length: 2 characters
Add fuzzy matching for typo tolerance

Step 7: Deploy Portal Frontend

# Build and deploy static web app
cd portal/
npm run build
az staticwebapp deploy --app-name $APP_NAME --output-location build/

Step 8: Smoke Test

# Test keyword search
curl "$SEARCH_ENDPOINT/indexes/portal-index/docs?search=azure+pricing&api-version=2024-07-01"

# Test vector search
python scripts/test_vector_search.py --query "How to deploy containers"

# Test hybrid search
python scripts/test_hybrid_search.py --query "kubernetes scaling" --top 5

# Test autocomplete
curl "$SEARCH_ENDPOINT/indexes/portal-index/docs/autocomplete?search=kub&suggesterName=sg&api-version=2024-07-01"

Post-Deployment Verification

[ ] Hybrid search returning relevant results
[ ] Semantic ranking enabled and working
[ ] Facets populating correctly
[ ] Autocomplete responding within 100ms
[ ] Indexer running on schedule without errors
[ ] Portal frontend loading and querying successfully
[ ] Search analytics logging queries

Troubleshooting

Issue	Cause	Fix
Zero results	Index empty	Check indexer status, verify data source
Vector search returns irrelevant	Wrong embedding model	Match query embedding to index embedding model
Semantic ranking not working	SKU too low	Requires Standard S1 or higher
Slow autocomplete	Too many suggestable fields	Limit suggester to 2-3 fields
Indexer fails	Schema mismatch	Compare source schema to index fields
Facet counts wrong	Filter not applied	Verify filterable attribute on facet fields

deploy-ai-search-portal— agent skill

Is deploy-ai-search-portal safe to install?

Deploy AI Search Portal

When to Use

Prerequisites

Step 1: Validate Infrastructure

Step 2: Deploy Azure Resources

Step 3: Create Index Schema

Step 4: Configure Hybrid Search

Step 5: Configure Indexers

Step 6: Configure Autocomplete & Suggestions

Step 7: Deploy Portal Frontend

Step 8: Smoke Test

Post-Deployment Verification

Troubleshooting

Audit the pieces. Scan the whole. Decide.