lean-profile-agent

Chat with my professional profile, grounded in my own documents and cited on every answer, plus the same knowledge base exposed as a read-only MCP server so a recruiter or client can ask from Claude, ChatGPT or any MCP client.

I am Leandro F. Juárez: Splunk Core Certified Consultant, Splunk Enterprise Certified Architect and Splunk Certified Instructor (Power User Fast Start, authorized by Splunk), working on SIEM, SOAR, detection engineering and AI engineering for security teams. This repository is both my "living CV" and a small, honest example of the AI/MCP work I do for clients.

Answers come only from my documented experience, with sources. When something is not in the documents, the assistant says so and points you to me. This is not a promise of perfection: the retrieval is deterministic and the model is instructed and tested to cite, but every answer still shows its sources and the CV is one click away.

What is inside

Related MCP server: portfolio-mcp

Run it locally

Requires Python 3.11+.

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
cp /path/to/Leandro_Juarez_CV_EN.pdf cv/   # optional, see cv/README.md
export ANTHROPIC_API_KEY=sk-ant-...        # only the chat needs it; MCP and the page work without
profile-agent                              # http://localhost:8080

Tests and lint:

pytest -q
ruff check src tests

Chat backend: PROFILE_PROVIDER=anthropic (default, ANTHROPIC_API_KEY, PROFILE_MODEL), gemini (GEMINI_API_KEY, GEMINI_MODEL, default gemini-flash-lite-latest) or openrouter (OPENROUTER_API_KEY, OPENROUTER_MODEL). eval/compare_models.py runs the same twelve questions through any of them and writes a side-by-side report with automatic checks.

Other environment variables: ANTHROPIC_API_KEY (chat), PROFILE_MODEL (default claude-sonnet-5-5), CHAT_DAILY_LIMIT (default 300 model calls per day), PROFILE_KB_DIR, PROFILE_CV_DIR, PORT.

Use the MCP server

Endpoint: https://<your-service>/mcp (locally http://localhost:8080/mcp). See examples/clients.md for Claude Code, Claude Desktop, ChatGPT and a Python client, and examples/claude_desktop_config.json.

claude mcp add --transport http lean-profile https://<your-service>/mcp

Tools: profile_summary(lang), search_experience(query, lang, k), list_case_studies(lang), get_case_study(id, lang), availability_and_rates(lang), contact().

How the chat stays grounded

1. The last user message is tokenized and matched against the sections of the knowledge base in the requested language (lang, or detected from the question).

2. If the best match is weak or covers too few of the question's terms, the server answers with a fixed "not in my documented experience" message and never calls the model.

3. Otherwise the top sections are sent as <source n="…" doc="…" section="…"> blocks with the question. The system prompt allows only those sources, requires [n] citations, and treats the question text as data, not instructions.

4. The server streams the text, then emits the sources that were actually cited and three follow-up questions taken from the documents just used.

tests/test_hard_rules.py keeps the public copy honest: certifications always in the agreed order, no certification claims that do not exist, and a few names that must never appear.

Deploy to Cloud Run

deploy/cloud-run.sh holds the recipe (Secret Manager for the API key, gcloud run deploy --source ., region southamerica-east1, scale to zero). It does not run by itself; read it, set PROJECT (and PROVIDER=gemini or anthropic, default gemini), and run it when you are ready. The Dockerfile copies knowledge/ and cv/ into the image; the CV PDFs are git-ignored on purpose.

Design notes

The full design is in docs/superpowers/specs/2026-11-03-profile-agent-design.md. In short: Python to match my other public repositories, one process for page + API + MCP so one small container serves everything, lexical retrieval because the corpus is tiny and must be auditable, and knowledge-base-driven follow-ups because suggestions should never be invented.

License

MIT. Case study figures are synthetic; the method, design decisions and deliverables are the real ones, as every case study states.