Merge pull request #343 from scverse/feat/claude-agent-skill

Add bundled Claude Code Agent Skill and installer

Author: PauBadiaM

CHANGELOG.md

@@ -8,6 +8,12 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## 2.2.0
+
+### Added
+- Bundled a Claude Code [Agent Skill](https://docs.claude.com/en/docs/claude-code/skills) that teaches coding agents to use decoupler correctly (method selection, calling conventions, output locations, priors, datasets, and benchmarking)
+- Added the `decoupler-install-skills` console script to install the skill into `~/.claude/skills/decoupler/` (use `--force` to refresh after upgrading, or `--print-path` to point `CLAUDE_SKILLS_PATH` at the bundled copy in place)
+
 ## 2.1.6
 
 ### Bugfixes

README.md

@@ -79,6 +79,20 @@ mamba create -n=dcp conda-forge::decoupler-py
 pip install git+https://github.com/scverse/decoupler.git@main
 ```
 
+## Claude Code Skill
+
+decoupler ships an [Agent Skill](https://docs.claude.com/en/docs/claude-code/skills) for [Claude Code](https://claude.com/claude-code) that teaches the agent to use the library correctly. Install it once into your personal skills directory:
+
+```bash
+decoupler-install-skills
+```
+
+This copies the skill to `~/.claude/skills/decoupler/`, making it available in **every** project (re-run with `--force` after upgrading). Once installed, just ask Claude Code to do decoupler tasks ("find the most active TFs in this cell cluster", "score hallmark gene sets with ora", "build a consensus across methods") and the skill is consulted automatically. To avoid copying into your home directory, point Claude Code at the bundled copy instead:
+
+```bash
+export CLAUDE_SKILLS_PATH="$(decoupler-install-skills --print-path)"
+```
+
 ## Release notes
 
 See the [changelog][].

pyproject.toml

@@ -4,7 +4,7 @@ requires = [ "hatchling" ]
 
 [project]
 name = "decoupler"
-version = "2.1.6"
+version = "2.2.0"
 description = "Python package to perform enrichment analysis from omics data."
 readme = "README.md"
 license = { file = "LICENSE" }
@@ -38,6 +38,7 @@ dependencies = [
 urls.Documentation = "https://decoupler.readthedocs.io/"
 urls.Homepage = "https://github.com/scverse/decoupler"
 urls.Source = "https://github.com/scverse/decoupler"
+scripts.decoupler-install-skills = "decoupler._skills.install:main"
 
 [dependency-groups]
 dev = [

src/decoupler/_skills/__init__.py

@@ -0,0 +1,6 @@
+"""Bundled Claude Code Agent Skill and its installer.
+
+This subpackage keeps the decoupler Agent Skill (the ``data/`` directory) and the
+command that installs it (``install.py``) together and out of the main package
+namespace. See ``install.py`` for usage.
+"""

src/decoupler/_skills/data/SKILL.md

@@ -0,0 +1,85 @@
+---
+name: decoupler
+description: Use for any task involving the decoupler library — inferring biological activity/enrichment scores from omics data (bulk, single-cell, spatial). Triggers on estimating transcription factor (TF) activity, pathway activity, or gene-set enrichment from an AnnData/DataFrame; running ulm, mlm, ora, gsea, gsva, aucell, viper, zscore, waggr, mdt, udt; multi-method consensus; fetching prior knowledge (CollecTRI, DoRothEA, PROGENy, MSigDB hallmarks, OmniPath); pseudobulk and expression filtering; using bundled example datasets (pbmc3k, covid5k, erygast1k, hsctgfb, msvisium, knocktf, toy) and whether they are raw counts or normalized; ranking/plotting activity scores; or benchmarking methods against ground truth. This is a router skill — read the relevant file under references/ before writing decoupler code, because outputs land in different places and several defaults are non-obvious.
+---
+
+# decoupler
+
+`decoupler` estimates biological activities (transcription factors, pathways, gene
+sets) from omics data. You give it a **data matrix** (observations × features, e.g.
+cells × genes) and a **prior-knowledge network** (which features belong to which
+biological program), and it returns an **enrichment score per observation per
+program** — all methods sharing one unified interface. Part of scverse; works
+directly on `AnnData`, `pandas.DataFrame`, or raw matrices.
+
+This skill is a **router**. Each topic below has a detailed reference file with exact
+signatures and footguns. **Read the relevant reference before writing code** — do not
+rely on memory of the API, because results are written to different places depending
+on the input type and several methods have non-obvious defaults.
+
+## Two cross-cutting concepts (read these first if unsure)
+
+- **[The unified method call](references/calling-convention.md)** — every method is
+  called the same way: `dc.mt.<method>(data, net, tmin=5, ...)`. Covers the accepted
+  input types (AnnData / DataFrame / `[matrix, obs, var]` **list**), the shared
+  arguments (`tmin`, `raw`, `layer`, `empty`, `bsize`), and the big footguns: input
+  must be **normalized (e.g. log1p), not raw counts**, and `net` must be a
+  **long-format** `source`/`target`/`weight` table.
+
+- **[Where the results go](references/io-and-outputs.md)** — the return type depends
+  on the input type. **AnnData → written in place** into `.obsm["score_<method>"]`
+  (and `.obsm["padj_<method>"]` if the method tests), returns `None`. **DataFrame /
+  list → returns an `(es, pv)` tuple** (`pv` is `None` for non-testing methods). Read
+  scores back out of an AnnData with `dc.pp.get_obsm`. **This is the #1 thing agents
+  get wrong.**
+
+## Task → reference file
+
+| If the task is… | Read |
+|---|---|
+| **starting from scratch** — set up an end-to-end run (load data → get a net → score → rank → plot) | [references/getting-started.md](references/getting-started.md) |
+| how to **call a method**, what data/net formats are accepted, `tmin`/`raw`/`layer` semantics | [references/calling-convention.md](references/calling-convention.md) |
+| **where scores land** and how to read them back (`.obsm` vs `(es, pv)` tuple, `get_obsm`) | [references/io-and-outputs.md](references/io-and-outputs.md) |
+| **choosing and configuring a method** — ulm/mlm/ora/gsea/gsva/aucell/viper/zscore/waggr/mdt/udt + their kwargs | [references/methods.md](references/methods.md) |
+| getting **prior knowledge** — TF regulons (CollecTRI, DoRothEA), pathways (PROGENy), gene sets (MSigDB hallmarks), OmniPath, `.gmt`, organism translation | [references/priors.md](references/priors.md) |
+| **preprocessing** — pseudobulk from single cell, expression/sample filtering, layers, spatial kNN | [references/preprocessing.md](references/preprocessing.md) |
+| running **many methods at once** and combining them into a **consensus** | [references/multi-method.md](references/multi-method.md) |
+| **ranking** activity scores by group/ordering and **plotting** them (barplot, dotplot, volcano, network) or overlaying them on a **UMAP / spatial H&E** via scanpy | [references/ranking-and-plotting.md](references/ranking-and-plotting.md) |
+| **benchmarking** methods/nets against a perturbation ground truth | [references/benchmarking.md](references/benchmarking.md) |
+| using a **bundled example dataset** (`dc.ds.*`) — and whether its `.X` is raw counts or already normalized | [references/datasets.md](references/datasets.md) |
+
+## Quick orientation (submodule map)
+
+decoupler exposes everything through namespaced submodules — canonical import is
+`import decoupler as dc`:
+
+- `dc.mt` — **methods**: the enrichment/activity algorithms (`ulm`, `mlm`, `ora`,
+  `gsea`, `gsva`, `aucell`, `viper`, `zscore`, `waggr`, `mdt`, `udt`) plus
+  `dc.mt.decouple` (run several) and `dc.mt.consensus`. `dc.mt.show()` lists them.
+- `dc.op` — **prior knowledge** (OmniPath): `collectri`, `dorothea`, `progeny`,
+  `hallmark`, `resource`, `translate`.
+- `dc.pp` — **preprocessing**: `pseudobulk`, `filter_by_expr`, `filter_by_prop`,
+  `filter_samples`, `get_obsm`, `extract`, `swap_layer`, `knn`, `read_gmt`.
+- `dc.ds` — **datasets**: `toy` (used in every example: `adata, net = dc.ds.toy()`),
+  `pbmc3k`, `covid5k`, and other real examples. **They differ in normalization state**
+  (some raw counts, some already log-normalized) — see
+  [datasets.md](references/datasets.md) before scoring one.
+- `dc.tl` — **tools**: `rankby_group`, `rankby_obsm`, `rankby_order`.
+- `dc.pl` — **plotting**: `barplot`, `dotplot`, `volcano`, `network`, `obsm`, etc.
+- `dc.bm` — **benchmarking**: `benchmark`, `metric`, `pl`.
+
+## Conventions used throughout
+
+- **Data** = observations × features (cells/samples × genes), **normalized** (log1p),
+  not raw counts. **Net** = long-format DataFrame: `source` (program), `target`
+  (feature), optional signed `weight`.
+- Output score matrices are observations × sources. AnnData results live in `.obsm`
+  under `score_<method>` / `padj_<method>`.
+- `tmin` (default 5) silently drops sources with fewer than `tmin` targets present in
+  the data — lower it for small/toy nets (examples use `tmin=3`).
+- `verbose=True` on any method prints the pruning/run log — use it to see how many
+  sources survived `tmin`.
+- **Plotting**: use `dc.pl.*` for summary plots and scanpy `sc.pl.umap` / `sc.pl.spatial`
+  (on a `dc.pp.get_obsm` score AnnData) for embedding/tissue overlays. Never reimplement
+  a plot from matplotlib primitives — see
+  [ranking-and-plotting.md](references/ranking-and-plotting.md).

src/decoupler/_skills/data/references/benchmarking.md

@@ -0,0 +1,59 @@
+# Benchmarking methods and nets
+
+`dc.bm` evaluates how well a method + net recover a known ground truth — typically a
+perturbation dataset where you know which program was up/down in each sample. Use it
+to **choose a method** or **validate a net**, not for routine scoring.
+
+## `dc.bm.benchmark`
+
+```python
+dc.bm.benchmark(
+    adata,                 # AnnData with a perturbation ground truth in .obs
+    net,                   # a net, or a dict of {name: net} to compare several
+    metrics=None,          # e.g. ["auroc", "auprc"] (None = defaults)
+    groupby=None,          # obs column(s) to stratify the evaluation
+    runby="expr",          # evaluate per-observation ("expr") or per-source
+    sfilt=False,           # filter sources to those with a ground-truth label
+    thr=0.1, emin=5,
+    kws_decouple=None,     # kwargs forwarded to dc.mt.decouple (methods, args, tmin, ...)
+    verbose=False,
+)
+```
+
+- Internally runs `dc.mt.decouple`, so pass method selection and per-method args via
+  `kws_decouple` (e.g. `kws_decouple={"methods": ["ulm", "mlm"], "tmin": 3}`).
+- Pass a **dict of nets** to benchmark several priors head to head.
+- The ground truth (which source is perturbed, and its sign) must be encoded in
+  `adata.obs` — see the decoupler benchmarking tutorial for the expected columns.
+
+## Metrics and plots
+
+- `dc.bm.metric` — the scoring functions: `auc`, `fscore`, `qrank`, `hmean`,
+  `dict_metric`.
+- `dc.bm.pl` — plots of benchmark results: `auc`, `bar`, `fscore`, `qrank`, `summary`.
+
+```python
+res = dc.bm.benchmark(adata, {"collectri": net_a, "dorothea": net_b},
+                      kws_decouple={"methods": ["ulm", "mlm"]})
+dc.bm.pl.summary(res)   # compare methods × nets
+```
+
+## When to use
+
+- Deciding **which method** to trust for your data/organism.
+- Comparing **candidate nets** (e.g. CollecTRI vs DoRothEA levels).
+- Sanity-checking a **custom net** against a perturbation benchmark
+  (`dc.ds.knocktf` and similar provide ground-truth datasets).
+
+## Footguns
+
+- Benchmarking is only meaningful with a **real ground truth** — a toy net won't tell
+  you anything.
+- It re-runs scoring internally; keep `kws_decouple` consistent with how you intend to
+  score for real, or the comparison won't transfer.
+
+## Related references
+
+[multi-method.md](multi-method.md) (`decouple`, which `benchmark` wraps),
+[methods.md](methods.md) (candidate methods),
+[priors.md](priors.md) (candidate nets).

src/decoupler/_skills/data/references/calling-convention.md

@@ -0,0 +1,91 @@
+# The unified method call
+
+Every enrichment method in `dc.mt` is a callable object with the **same signature**.
+Learn it once and it applies to `ulm`, `mlm`, `ora`, `gsea`, `gsva`, `aucell`,
+`viper`, `zscore`, `waggr`, `mdt`, `udt` (and `dc.mt.decouple`).
+
+```python
+import decoupler as dc
+
+adata, net = dc.ds.toy()          # AnnData (obs × var) + long-format net
+dc.mt.ulm(adata, net, tmin=3)     # writes adata.obsm["score_ulm"], ["padj_ulm"]
+```
+
+## Shared signature
+
+```python
+dc.mt.<method>(
+    data,              # AnnData | pandas.DataFrame | [matrix, obs_names, var_names]
+    net,               # long-format DataFrame: source, target, [weight]
+    tmin=5,            # drop sources with < tmin targets present in the data
+    raw=False,         # use adata.raw instead of adata.X
+    layer=None,        # use adata.layers[layer] instead of adata.X (passed via kwargs)
+    empty=True,        # drop all-zero observations/features before scoring
+    bsize=250_000,     # batch size (rows) for sparse / backed matrices
+    verbose=False,
+    **kwargs,          # method-specific args — see references/methods.md
+)
+```
+
+`layer=` is accepted even though it is not in the explicit signature — it is forwarded
+through `**kwargs` to the runner. Use `layer=` **or** `raw=`, not both.
+
+## Accepted input types (`data`)
+
+| Form | What it is | Returns |
+|---|---|---|
+| `AnnData` | scored on `.X` (or `.raw`/`layer`) | **`None`** — results written to `.obsm` in place |
+| `pandas.DataFrame` | observations (rows) × features (cols) | `(es, pv)` tuple of DataFrames |
+| `list` `[matrix, obs_names, var_names]` | numpy/sparse matrix + name arrays | `(es, pv)` tuple of DataFrames |
+
+See [io-and-outputs.md](io-and-outputs.md) for the return details.
+
+### Footgun — the matrix form must be a `list`, not a `tuple`
+
+```python
+# WRONG — a tuple raises "mat must be a list of [matrix, samples, features]"
+es, pv = dc.mt.ulm((X, obs, var), net)
+
+# RIGHT — wrap it in a list
+es, pv = dc.mt.ulm([X, obs, var], net)
+```
+
+## The three footguns that silently give wrong results
+
+1. **Input must be normalized, not raw counts.** decoupler expects
+   log-normalized expression — the same matrix you would use for differential
+   expression. **When normalizing, use `sc.pp.normalize_total(adata, target_sum=1e4)`**
+   followed by `sc.pp.log1p(adata)`. Passing raw counts runs without error but produces
+   meaningless scores. If your normalized data is in a layer, pass
+   `layer="lognorm"`; if in `.raw`, pass `raw=True`. **Check before normalizing:** if
+   `.X` is integer-valued with a large max it is raw counts; if it is small floats
+   (often with `adata.uns["log1p"]`) it is already normalized — do not normalize twice.
+   The bundled datasets differ; see [datasets.md](datasets.md) (e.g. `pbmc3k` is already
+   log-normalized, `covid5k` is raw counts).
+
+2. **`net` must be long-format** with columns `source`, `target`, and (for weighted
+   methods) `weight` — one row per (program, feature) edge. A wide/matrix net or wrong
+   column names will fail or misbehave. See [priors.md](priors.md).
+
+3. **`tmin` silently prunes.** Sources with fewer than `tmin` (default 5) targets
+   present in `data` are dropped *before* scoring — they simply won't appear in the
+   output. On small or toy nets this can drop everything; the examples use `tmin=3`.
+   Run with `verbose=True` to see `<method> - X sources with < tmin targets` and how
+   many survived.
+
+## Listing and introspecting methods
+
+```python
+dc.mt.show()          # DataFrame: name, desc, stype, weight, test, limits, reference
+```
+
+- `weight` — whether the method uses signed edge weights (`True` for ulm/mlm/viper/…).
+- `test` — whether it produces p-values (`padj`). `False` for aucell/gsva/mdt/udt.
+- `stype` / `limits` — value type and output range (e.g. aucell `(0, 1)`).
+
+## Related references
+
+[io-and-outputs.md](io-and-outputs.md) (return types, reading `.obsm` back),
+[methods.md](methods.md) (per-method kwargs and how to choose one),
+[priors.md](priors.md) (building/fetching `net`),
+[preprocessing.md](preprocessing.md) (getting a normalized matrix / layers).