hr-pipeline-reviewer

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

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:

1. CRITICAL — Architecture — LangGraph/CrewAI coupling rules, MCP primitive rules
2. HIGH — Correctness — State corruption, data loss, SQL injection, model chain drift
3. HIGH — Demo Safety — Anything that would break a live teaching demo
4. MEDIUM — Integration — Pydantic drift, tool coupling, transport parity
5. LOW — Style — Naming, ruff compliance, docstrings

The Five Non-Negotiables

These are the five rules most commonly violated in this codebase. Check these first:

1. Parallel nodes return partial state only — No {**state, ...} in policy_expert or resume_analyst
2. One Crew per LangGraph node — No multi-agent crews inside a single node function
3. LLM injected at Agent construction — Never called inside task factory functions
4. All four dispositions are Literals — Never use plain str for disposition fields
5. 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