research-best-practices

name	research-best-practices
description	Research current best practices, latest package versions, and official guidance before writing implementation code. Uses MCP tools (Microsoft Learn, Context7, DeepWiki) and available Copilot skills to ground decisions in up-to-date, first-party documentation rather than stale training data.

Research Best Practices

Ground every implementation decision in current, authoritative sources before writing code.

When to Use

• Always at the start of Step 3 (Implementation) — before the first line of code
• When adding a new Azure service, SDK, or infrastructure resource not covered by specs/tech-stack.md
• When choosing between libraries, patterns, or architectural approaches
• When a package version may have breaking changes since last known state
• When the task involves an area you haven't recently verified (auth, storage, AI, etc.)
• Note: Phase 1d (Tech Stack Resolution) performs comprehensive upfront research. This skill handles targeted, increment-specific research that builds on those resolved decisions.

Inputs

• specs/tech-stack.md — Pre-resolved technology decisions from Phase 1d (check this FIRST)
• Feature contracts from Step 2 (API specs, shared types, infra contract)
• The project's current package.json dependencies and versions
• The specific technologies and services the feature requires

Research Tools

Use these MCP tools in priority order:

Tool	Use For	Example
Microsoft Learn MCP (microsoft_docs_search, microsoft_code_sample_search, microsoft_docs_fetch)	Azure SDKs, Azure best practices, .NET Aspire, Azure Container Apps, Entra ID, any Microsoft/Azure technology	"Azure Container Apps health probes", "MSAL Node.js token caching"
Context7	Latest docs and usage examples for any open-source library or framework — npm packages, Next.js, Express, Tailwind, Playwright, etc.	"next.js app router server actions", "express middleware error handling"
DeepWiki	Deep architectural understanding of open-source repos — how a library works internally, patterns used, extension points	"How does next-auth handle session rotation?", "Playwright test isolation model"
Azure Best Practices (get_azure_bestpractices)	Azure-specific code generation and deployment best practices — call before writing any Azure infra or SDK code	"Container Apps deployment", "Cosmos DB SDK usage"
Web Search (web_search)	Recent releases, changelogs, migration guides, community consensus on emerging patterns	"Express 5 migration guide", "Next.js 15 breaking changes"

Also check local resources

• .github/skills/ — Scan for an existing skill that covers the task
• specs/contracts/ — Re-read the API and infra contracts to confirm scope
• package.json — Check current dependency versions before assuming APIs

Steps

1. Consult tech stack — Read specs/tech-stack.md first. Most technology decisions, versions, and patterns should already be resolved from Phase 1d. Only research further if the current increment needs something not covered.
2. Inventory — List the technologies, SDKs, and services needed for the current feature/slice that are NOT already covered by specs/tech-stack.md
3. Check skills — Scan .github/skills/ for existing skills that cover any of these technologies
4. Research each technology — For each item in the inventory: a. Query Microsoft Learn MCP for Azure/Microsoft technologies b. Query Context7 for latest framework/library docs and examples c. Query DeepWiki if you need to understand library internals d. Query Azure Best Practices if Azure resources are involved e. Use Web Search for recent changelogs or migration guides
5. Check versions — Verify that the package versions in package.json are current; note any that need updating
6. Summarize findings — Produce a concise research summary:
   • Recommended patterns and APIs (with source links)
   • Package versions to use or update
   • Anti-patterns or deprecations to avoid
   • Any relevant skills found in .github/skills/
7. Record in state — Save key findings in state.json under the current feature's metadata so future sessions don't repeat the research

Output Format

## Research Summary: <feature-name>

### Technologies Researched
| Technology | Version | Source | Key Finding |
|------------|---------|--------|-------------|
| @azure/cosmos | 4.2.0 | MS Learn | Use `iterateAll()` instead of `fetchAll()` for large datasets |
| next.js | 15.1.0 | Context7 | Server Actions stable; use `revalidatePath` for cache invalidation |

### Patterns to Follow
- <pattern description> (source: <link>)

### Anti-patterns / Deprecations
- <what to avoid> (source: <link>)

### Package Updates Needed
- <package>: <current> → <recommended> (reason)

### Skills Available
- <skill-name>: <how it applies>

Notes

• Do not skip this step. Stale knowledge causes subtle bugs, deprecated API usage, and security vulnerabilities.
• Research is scoped to the current feature — don't boil the ocean.
• If a technology is well-established and unchanged (e.g., basic Express routing), a quick verification is sufficient.
• Cache-friendly: if you researched a technology for Feature A, reuse those findings for Feature B unless the context differs.