| name | hr-pipeline-reviewer |
| description | Pipeline review and architectural validation skill for the Contoso HR Agent project.
Covers LangGraph + CrewAI + FastMCP 2 + Pydantic v2 patterns and the rules specific
to this codebase. Use when reviewing any change to src/contoso_hr/ or when adding new
pipeline components.
|
HR Pipeline Reviewer
Expert review skill for the Contoso HR Agent — a LangGraph + CrewAI + FastMCP 2
pipeline for evaluating Microsoft Certified Trainer (MCT) candidates against HR policy.
When to Activate
| Trigger | Action |
|---|
New or modified file in src/contoso_hr/ | Full review pass |
| New CrewAI agent or LangGraph node | Run checklists/new-agent.md |
| New MCP tool, resource, or prompt | Run checklists/new-mcp-tool.md |
| Unexpected pipeline disposition | Trace HRState through all nodes |
| Pre-commit | Run checklists/pre-commit.md |
| Demo prep | Run demo-readiness checks in agent definition |
Reference Guides
Load the relevant guide before reviewing — do not rely on memory alone:
Checklists
Priority Order
Always review in this sequence — stop and fix CRITICAL issues before proceeding:
- CRITICAL — Architecture — LangGraph/CrewAI coupling rules, MCP primitive rules
- HIGH — Correctness — State corruption, data loss, SQL injection, model chain drift
- HIGH — Demo Safety — Anything that would break a live teaching demo
- MEDIUM — Integration — Pydantic drift, tool coupling, transport parity
- LOW — Style — Naming, ruff compliance, docstrings
The Five Non-Negotiables
These are the five rules most commonly violated in this codebase. Check these first:
- Parallel nodes return partial state only — No
{**state, ...} in policy_expert or resume_analyst
- One Crew per LangGraph node — No multi-agent crews inside a single node function
- LLM injected at Agent construction — Never called inside task factory functions
- All four dispositions are Literals — Never use plain
str for disposition fields
- MCP tools work in both transports — No SSE-only or stdio-only code paths
Quick Diagnosis: Wrong Disposition
When a candidate gets an unexpected disposition, trace this path:
1. Check intake node: was ResumeSubmission parsed correctly?
→ src/contoso_hr/pipeline/graph.py: intake()
2. Check policy_expert output: what policy_context_summary was produced?
→ src/contoso_hr/pipeline/tasks.py: PolicyExpertTask
3. Check resume_analyst output: what skills_match_score was assigned?
→ src/contoso_hr/pipeline/tasks.py: ResumeAnalystTask
→ Did brave_web_search fire? Check tool call logs.
4. Check decision_maker input: did it receive both policy + analyst outputs?
→ Verify LangGraph merged partial state correctly (graph.py fan-in edges)
5. Check disposition thresholds in prompts.py:
→ Strong Match: 80+, Possible Match: 55-79, Needs Review: 35-54, Not Qualified: <35
Key File Map
src/contoso_hr/
├── pipeline/
│ ├── graph.py ← LangGraph StateGraph, HRState, 5 node functions
│ ├── agents.py ← 4 CrewAI Agent class definitions
│ ├── tasks.py ← CrewAI Task factories (inject state into descriptions)
│ ├── tools.py ← @tool query_hr_policy + brave_web_search
│ └── prompts.py ← System prompts + disposition thresholds
├── models.py ← Full Pydantic v2 model chain
├── config.py ← Azure AI Foundry factories
├── mcp_server/
│ └── server.py ← FastMCP 2, all 5 primitives
├── knowledge/
│ ├── retriever.py ← ChromaDB query → PolicyContext
│ └── vectorizer.py ← Doc ingestion → embeddings → ChromaDB
├── memory/
│ ├── sqlite_store.py ← HRSQLiteStore (candidates + evaluations)
│ └── checkpoints.py ← SqliteSaver factory
└── engine.py ← FastAPI endpoints + chat session management
