MPC working#136
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Fix/gloscope_py
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…entation_tutorial - Add the tutorials for the GloScope reimplementation - Fix R implementation of GloScope
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Fix/negative distances
Adds a markdown skill library (Claude Code per-folder SKILL.md layout) that mirrors the public API surface (pp / tl / pl / datasets), so coding agents can use patpy without rediscovering the API from source each session. Pure additive change: no module behavior is altered. Skills ship with the wheel via the default hatchling include for files under src/patpy/. Co-Authored-By: Claude Opus 4.7 <[email protected]>
Adds a markdown skill library (Claude Code per-folder SKILL.md layout) that mirrors the public API surface (pp / tl / pl / datasets), so coding agents can use patpy without rediscovering the API from source each session. Pure additive change: no module behavior is altered. Skills ship with the wheel via the default hatchling include for files under src/patpy/. Co-Authored-By: Claude Opus 4.7 <[email protected]>
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Fix KNN score
Feature/agent skills
Adds a BioContextAI Registry-compatible MCP server that exposes dataset search and download from CellxGene Discover so any MCP-capable agent (Claude Desktop, Cursor, mcp-cli + Ollama, ...) can fetch single-cell datasets by disease, tissue, or ontology term. The server complements (instead of duplicating) the existing MaxMLang/cxg-census-mcp and biocontext-ai/anndata-mcp registry servers. - New src/patpy/mcp/ package with a multi-source plugin architecture (DataSource protocol) and a CellxGene Discover REST source with pagination, retries, 24 h on-disk index cache, and streaming download with SHA-256 verification. - New mcp/ directory with a schema-validated meta.yaml ready to be PR'd into biocontext-ai/registry, plus a Dockerfile and .dockerignore. - Documentation in docs/mcp.md (linked from docs/index.md and a new section in README.md) covering install, agent config snippets, and a chaining recipe with cxg-census-mcp and anndata-mcp. - Test suite under tests/: server tool registration check, twelve discover-client unit tests with a hand-rolled fake session, and registry-schema validation against the upstream schema.json (committed offline as a fixture). Co-authored-by: Cursor <[email protected]>
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Thanks, looks interesting, I'll review the content soon. But the main question is how to keep it relevant? Could you add prompts that you used to generate it, or come up with a way to update the skills on new code updates? It would be cool to have a GitHub action, backed by Claude, that updates skills on pull requests. For more effective prompting, point the agent to CHANGELOG.md |
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Will do it today, thanks Vlad |
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Sounds good, I am trying to finish fetching datasets from cellXgene |
Grpinto branch
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…caches Aligns the mcp/ subproject with biocontext-ai/mcp-server-cookiecutter and keeps test caches out of git going forward. mcp/ subproject (cookiecutter alignment): - Add mcp/mcp.json (BioContextAI Registry client snippet, mirrors biocontext-ai-anndata-mcp and MaxMLang-cxg-census-mcp siblings). - Add .github/workflows/test-patpy-mcp.yaml and build-patpy-mcp.yaml so mcp/tests/ runs on every PR (was previously only at release-tag time) and a broken mcp/pyproject.toml is caught before tagging. Both are path-filtered to mcp/** so they don't run on parent-only changes. - Enrich mcp/README.md with the cookiecutter's four canonical install patterns (uvx PyPI / uvx git / uvx local / pip) plus copyable mcp.json snippets, and document the two-file registry submission. Skills (correct API divergences surfaced by an end-to-end run): - sample_representation/SKILL.md: Pseudobulk's cell_group_key is a required positional arg (not optional); document that explicitly with an inline TypeError example, and update the minimal example to pass it. - supervised_methods/SKILL.md: PULSAR is zero-shot inference using a pretrained HuggingFace model on UCE foundation-model embeddings, not a trainable classifier on PCA features. Split the "common signature" into trainable (MixMIL/PaSCient) vs zero-shot (PULSAR), document layer="X_uce", device="cuda", and the HF-model download. Add the mixmil + torch-scatter install gotcha. Repo hygiene: - Tighten .gitignore: ignore .cache-*/, .venv-*/, /outputs/, /run_*.py, /.cellxgene-test-target.json, and /mcp/dist/. Prevents the patpy-mcp test cache and any future scratch from being committed. - Remove already-committed scratch from HEAD: 62.7 MB Slide-seq h5ad, 9.5 MB CellxGene catalog dump, the test-target sidecar, and the /.cellxgene-test-target.json probe file. Historical blobs will be evicted from .git/objects via a follow-up git filter-repo run. AGENTS.md: document the new mcp.json + workflows so future agents see them in the layout tree. Co-authored-by: Cursor <[email protected]>
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- Pack patpy skill modules (datasets, preprocessing, sample_representation, supervised_methods, evaluation, plotting) for export to Claude Code, Codex, and BioContext layouts via patpy-export-skills / patpy-export-biocontext - Add patpy.mcp FastMCP server (patpy-skills-mcp console script) exposing dataset_summary, preprocess_dataset, build_representation, evaluate_representation, run_supervised_prediction, generate_plot, simulate_dataset as MCP tools - Add example client configs under examples/mcp/ - Add tests for skill export bundles and MCP tool wrappers - Add [mcp] optional dependency group
# Conflicts: # .gitignore # CHANGELOG.md # README.md # docs/index.md # pyproject.toml # src/patpy/skills/SKILL.md # src/patpy/skills/sample_representation/SKILL.md # src/patpy/skills/supervised_methods/SKILL.md # src/patpy/tl/evaluation.py # tests/test_evaluation.py # tests/test_skills.py
… into feature/agent-skills
Feature/agent skills
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Is this ready for review? |
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@VladimirShitov I am not sure yet @benjaminfreyuu did you finish what you wanted to do ? I have not refactored the code. It is something that still needs to be done, reorganize stuff and test like on a large scale. |
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The skills part is working, MCP claude code integration and biocontextAI are added as well. |

Gonçalo Pinto :