[upstream-sync] skills/README.md from mims-harvard/ToolUniverse@bb632a34 [catalogue]

Reviewed-on: #13

upstream/mims-harvard-ToolUniverse/catalogue/skills/README.md

@@ -2,9 +2,9 @@
 title: "ToolUniverse Skills"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/README.md
-upstream_sha: e2520a96
-imported_at: 2026-06-26
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/bb632a34/skills/README.md
+upstream_sha: bb632a34
+imported_at: 2026-07-01
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -98,6 +98,7 @@ npx skills add mims-harvard/ToolUniverse
 | Skill | Description |
 |-------|-------------|
 | `setup-tooluniverse` | Install and configure ToolUniverse (MCP, CLI, or SDK) |
+| `tooluniverse-cs-setup` | Install/update ToolUniverse in **Claude Science** (conda env + pip package + native skill; not MCP) |
 | `create-tooluniverse-skill` | Create new skills with test-driven methodology |
 | `devtu-auto-discover-apis` | Discover life science APIs and create tools automatically |
 | `devtu-create-tool` | Create new scientific tools with proper structure and testing |

upstream/mims-harvard-ToolUniverse/catalogue/skills/tooluniverse-cs-setup/SKILL.md

@@ -0,0 +1,68 @@
+---
+lineage_type: import
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/bb632a34/skills/tooluniverse-cs-setup/SKILL.md
+upstream_sha: bb632a34
+imported_at: 2026-07-01
+prompt_class: unknown
+upstream_changes: accepted
+name: tooluniverse-cs-setup
+description: Install or update ToolUniverse in Claude Science — create the conda env, install the tooluniverse pip package, and (re)build the tooluniverse-research skill by fetching the current workflow library from GitHub. Use for first-time setup, upgrading the ToolUniverse version, refreshing the bundled workflows after an upstream release, or reinstalling on a new machine.
+---
+
+# Set up ToolUniverse for Claude Science
+
+The upstream ToolUniverse ships a Claude **Code** plugin (MCP server + `uvx` + slash commands). Claude **Science** loads capabilities differently, so this skill installs the equivalent natively: the `tooluniverse` **pip package** supplies the 2500+ tools, and the workflow library is packaged into a single dynamically-loaded skill, `tooluniverse-research`. No `uv`, no MCP server, no plugin marketplace.
+
+Loading this skill defines `tu_build_research_bundle()` in the kernel (run cells in the **`tooluniverse`** conda env).
+
+## Full install / update — four steps
+
+**1. Create the conda env** (skip if it already exists):
+```
+manage_environments(mode="create", name="tooluniverse", python_version="3.11", packages=["pip"])
+```
+
+**2. Install (or upgrade) the tools** — the pip package is the tool layer:
+```
+manage_packages(mode="install", environment="tooluniverse", packages=["tooluniverse"], use_pip=True)
+```
+Pin a version for reproducibility with `["tooluniverse==1.3.0"]`.
+
+**3. Stage the workflow bundle** — fetch the current repo and rebuild the file tree (run in a `python` cell, env `tooluniverse`):
+```python
+res = tu_build_research_bundle(staging="./tu_staging")
+res  # {out_dir, n_workflows, n_files, dropped, files_head}
+```
+This downloads the repo tarball, parses every `tooluniverse-*` workflow (dropping the plugin/installer entries), and writes `./tu_staging/out/` = `SKILL.md`, `kernel.py`, `index.json`, `workflows/*.md`.
+
+**4. Publish the skill** — push the staged tree into the catalog (run in the **`repl`** tool; `host.skills.*` lives there, not in `python`):
+```python
+import os
+SKILL = "tooluniverse-research"
+out = os.path.abspath("./tu_staging/out")
+if any(s["name"] == SKILL for s in host.skills.list()):
+    host.skills.delete(SKILL)                       # clean rebuild
+for root, _d, fs in os.walk(out):
+    for f in fs:
+        p = os.path.join(root, f)
+        rel = os.path.relpath(p, out)
+        host.skills.edit(SKILL, rel, open(p, encoding="utf-8").read())
+print(host.skills.publish(SKILL, overwrite=True))
+```
+(`host.skills.publish` refuses if `kernel.py` fails the sidecar gate — the `edit` result carries the verdict.)
+
+## Verify
+
+```python
+skill("tooluniverse-research")                      # loads router + injects helpers
+tu = get_tu()
+tu.run({"name": "PubChem_get_CID_by_compound_name", "arguments": {"name": "metformin"}})
+# -> {'status': 'success', 'data': {'IdentifierList': {'CID': [4091]}}}
+```
+
+## Notes
+
+- **Sandbox cache**: ToolUniverse defaults its cache to `~/.tooluniverse`, which is read-only here; `get_tu()` redirects it to the workspace via `TOOLUNIVERSE_CACHE_DIR`. Nothing to configure.
+- **API keys** (optional): most tools work without them. For NCBI / OncoKB / NVIDIA etc., add keys under Customize → Credentials, then expose them in the `tooluniverse` env.
+- **What is NOT ported**: the plugin's slash commands (`/tooluniverse:research`) and MCP server — replaced by natural-language routing (`search_skills` → `find_tu_workflow`). The two `*-plugin` installer docs are dropped as non-research entries.
+- **Updating**: rerun steps 2–4. Step 2 upgrades the tools; steps 3–4 refresh the workflow library from the latest GitHub state.

upstream/mims-harvard-ToolUniverse/catalogue/skills/tooluniverse-polygenic-risk-score/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-polygenic-risk-score/SKILL.md
-upstream_sha: e2520a96
-imported_at: 2026-06-26
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/3038dcbe/skills/tooluniverse-polygenic-risk-score/SKILL.md
+upstream_sha: 3038dcbe
+imported_at: 2026-06-30
 prompt_class: unknown
 upstream_changes: accepted
 name: tooluniverse-polygenic-risk-score
@@ -152,10 +152,19 @@ PRS can stratify individuals for:
 ### Research Applications
 
 - **Gene discovery**: PRS-based phenome-wide association studies (PheWAS)
-- **Genetic correlation**: Compare PRS across traits
+- **Genetic correlation**: Compare PRS across traits — but for a rigorous, GWAS-summary-statistics estimate of cross-trait genetic correlation (rg), use `run_ldsc_genetic_correlation` (LD Score regression), which needs only summary stats (no individual genotypes) and corrects for sample overlap. Far more principled than correlating PRS values.
 - **Causal inference**: Mendelian randomization using PRS as instruments
 - **Simulation studies**: Model polygenic architecture
 
+### SNP-heritability and genetic correlation (LDSC)
+
+Before or alongside building a PRS, quantify how much of the trait is captured by common SNPs and how traits relate — directly from GWAS summary statistics:
+
+- `run_ldsc_heritability` — SNP-based heritability (h²_SNP) from one GWAS's summary stats; the intercept also flags confounding/inflation vs. true polygenicity. This sets the ceiling a PRS can reach (the "heritability gap" below is exactly h²_SNP minus PRS R²).
+- `run_ldsc_genetic_correlation` — genetic correlation (rg) between two GWAS, for shared-aetiology and cross-trait PRS questions.
+
+Both are remote tools (LD Score regression engine + reference LD-score panels). Use them to ground heritability/rg claims in data rather than citing literature point estimates.
+
 ### Personal Genomics
 
 Consumer genetic testing (23andMe, Ancestry DNA) provides raw genotypes. Users can:

upstream/mims-harvard-ToolUniverse/catalogue/skills/tooluniverse-regulatory-genomics/SKILL.md

@@ -1,12 +1,12 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-regulatory-genomics/SKILL.md
-upstream_sha: e2520a96
-imported_at: 2026-06-26
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/3038dcbe/skills/tooluniverse-regulatory-genomics/SKILL.md
+upstream_sha: 3038dcbe
+imported_at: 2026-06-30
 prompt_class: catalogue
 upstream_changes: accepted
 name: tooluniverse-regulatory-genomics
-description: Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC. Use for regulatory element annotation, TF-binding-site prediction, and regulatory-region functional impact assessment.
+description: Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC — plus sequence-based deep-learning prediction of regulatory activity and non-coding variant effects (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2). Use for regulatory element annotation, TF-binding-site prediction, regulatory-region functional impact assessment, and predicting how a non-coding variant or a raw DNA sequence affects expression/chromatin/accessibility. Use this whenever a user asks what regulates a gene, whether a SNP hits a regulatory element, or to predict a non-coding variant's functional effect from sequence.
 disable-model-invocation: true
 ---
 
@@ -48,6 +48,9 @@ When analysis requires computation (statistics, data processing, scoring, enrich
 - "Is rs1234567 in a regulatory region?"
 - "What TF motifs overlap this genomic region?"
 - "Find ENCODE experiments for ATAC-seq in cancer cell lines"
+- "Predict the effect of this non-coding variant on expression / chromatin accessibility"
+- "Predict regulatory activity (expression, accessibility, TF binding) directly from a DNA sequence"
+- "Which of these enhancer variants is predicted to be most disruptive?"
 
 ---
 
@@ -71,6 +74,20 @@ When analysis requires computation (statistics, data processing, scoring, enrich
 | `RegulomeDB_query_variant` | Score regulatory impact of a variant | `rsid` (e.g., "rs4994") |
 | `ENCODE_search_biosamples` | Find available cell lines/tissues in ENCODE | `term_name`, `biosample_type`, `limit` |
 
+### Sequence-based deep-learning models (predict, don't just annotate)
+
+The tools above tell you what is *known* to be at a locus (databases). These models instead *predict* regulatory activity directly from the DNA sequence, and — by scoring a reference vs. alternate window — predict what a non-coding variant *does*. RegulomeDB ranks a variant by overlap with existing annotations; these give a quantitative, tissue-aware effect size even for novel variants with no annotation. Reach for them when annotation is silent or when the question is "how much does this allele change regulation".
+
+| Tool | Op | Predicts | Context | Access |
+|------|----|----------|---------|--------|
+| `AlphaGenome_predict_interval` / `AlphaGenome_score_variant` | profile region / score variant | RNA-seq, ATAC, CAGE, splice tracks (frontier accuracy, single-base) | up to 1 Mb | hosted API — `ALPHA_GENOME_API_KEY` |
+| `run_enformer_predict` / `run_enformer_variant_effect` | profile / score | 5,313 human (+1,643 mouse) tracks: expression, chromatin, TF binding | 196 kb | remote MCP server |
+| `run_borzoi_predict` / `run_borzoi_variant_effect` | profile / score | RNA-seq coverage (expression / polyA / splicing emphasis), 7,611 tracks | 524 kb | remote MCP server |
+| `run_chrombpnet_predict` / `run_chrombpnet_variant_effect` | profile / score | chromatin accessibility (ATAC / DNase), base-resolution profile + counts | ~2 kb | remote MCP server |
+| `Evo2_score_variant` | score | genome-foundation-model delta log-likelihood; coding **and** non-coding | up to 1 Mb | hosted NIM — `NVIDIA_API_KEY` |
+
+**Picking one:** `AlphaGenome_*` is the broadest readout + longest context when its key is set; `run_enformer_*` / `run_borzoi_*` are the published, self-hostable equivalents (Enformer for general regulation, Borzoi when expression/splicing is the question); `run_chrombpnet_*` when the question is specifically chromatin accessibility; `Evo2_score_variant` as a sequence-only check that also covers coding variants. Outputs are Δ (alt − ref) effect sizes, not calibrated probabilities — rank/calibrate against known variants. If no key/server is provisioned, fall back to the annotation tools above and say so.
+
 ---
 
 ## Workflow

upstream/mims-harvard-ToolUniverse/skills/devtu-code-optimization/SKILL.md

@@ -1,8 +1,8 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/devtu-code-optimization/SKILL.md
-upstream_sha: e2520a96
-imported_at: 2026-06-26
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/3038dcbe/skills/devtu-code-optimization/SKILL.md
+upstream_sha: 3038dcbe
+imported_at: 2026-06-30
 prompt_class: prompt
 upstream_changes: accepted
 name: devtu-code-optimization
@@ -40,6 +40,8 @@ Always run `Skill(skill="simplify")` after writing or modifying code.
 | Undisclosed normalization | Auto-transform hidden from user | [code-patterns.md](code-patterns.md) — Normalization Disclosure |
 | try/except indent | SyntaxError at runtime | [code-patterns.md](code-patterns.md) — try/except section |
 | Truncation buried | Data count hidden in notes | [code-patterns.md](code-patterns.md) — Truncation |
+| Hosted model API (NIM) | async 404 on poll, JSON-wrapped output, 200+inner-failure, "not found for account" | [code-patterns.md](code-patterns.md) — Hosted Model-API Tools |
+| R subprocess tool | `'\.' unrecognized escape` from `Rscript -e` | [code-patterns.md](code-patterns.md) — R-subprocess Tools |
 
 ## References
 

upstream/mims-harvard-ToolUniverse/skills/devtu-code-optimization/references/code-patterns.md

@@ -2,9 +2,9 @@
 title: "Code Patterns Reference"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/devtu-code-optimization/references/code-patterns.md
-upstream_sha: e2520a96
-imported_at: 2026-06-26
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/3038dcbe/skills/devtu-code-optimization/references/code-patterns.md
+upstream_sha: 3038dcbe
+imported_at: 2026-06-30
 prompt_class: prompt
 upstream_changes: accepted
 author: upstream
@@ -115,6 +115,73 @@ elif count == 0:
     result["hint"] = "No data available for this entity."
 ```
 
+## Hosted Model-API Tools (NVIDIA NIM-style)
+
+### Async poll host = the invocation host
+Poll a 202 job-status on the SAME gateway you POSTed to. NVCF biology NIMs invoke
+**and** poll on `health.api.nvidia.com`; `integrate.api.nvidia.com` serves only the
+OpenAI-compatible LLM endpoints and has **no** `/v1/status` route.
+
+```python
+host = urlparse(self.base_url).netloc          # e.g. health.api.nvidia.com
+poll_url = f"https://{host}/v1/status/{req_id}"
+```
+
+### Route-existence probe (find/verify hosted endpoints)
+Plain-text `404 page not found` = route does NOT exist; a structured
+`{"status":404,...}` (or 400/422/200) = route exists. Use the live API to confirm a
+model is hosted and to find the right slug before wrapping it.
+
+### Unwrap JSON envelopes around the "raw" payload
+Some endpoints return `{"pdbs": ["...ATOM..."]}` even when response_type is `pdb`.
+Unwrap to the inner value so the field matches the schema (real PDB, not a JSON blob).
+
+### HTTP 200 with an inner failure
+A 200 can carry `{"status": "failed", ...}` (e.g. DiffDock with an unreadable
+ligand). Surface it as an error — but only on explicit `failed/error/errored`; an
+inner `status:"success"` must stay a success (don't over-match).
+
+### 404 "not found for account" ≠ wrong path
+A gated/unprovisioned model returns a 404 whose body says "Not found for account".
+Report "model not available for your account" rather than "endpoint not found".
+
+### Retry a longer poll window before declaring "broken"
+A heavy async job can return 504 / `nvcf-status: errored` simply because
+`NVCF-POLL-SECONDS` was shorter than its runtime. Only a *persistent* 400
+`DEGRADED`/error across retries is a real outage. 5xx bodies are often empty —
+surface `nvcf-status` / `nvcf-reqid` from the headers instead.
+
+### Model-variant selection via templated endpoint
+Expose multiple hosted sizes through one tool: a `{placeholder}` in the endpoint +
+`fields.path_params` default, filled from the request arg (sanitized slug) and
+stripped from the request body.
+
+```python
+# endpoint "arc/{model}/generate", path_params {"model": "evo2-40b"}
+value = args.get(key) or default
+if not re.fullmatch(r"[A-Za-z0-9._-]+", str(value)):  # no path injection
+    value = default
+endpoint = endpoint.replace("{" + key + "}", value)
+```
+
+## R-subprocess Tools
+
+### Run a script file, not `Rscript -e <string>`
+`Rscript -e` collapses one backslash level before R parses it, so a regex literal
+like `sub("\\..*", ...)` becomes `sub("\..*", ...)` and R aborts with
+`'\.' is an unrecognized escape`. Write the script to a temp `.R` file and run
+`Rscript <file>` (parsed verbatim); always remove the temp file, incl. on timeout.
+
+```python
+tmp = tempfile.NamedTemporaryFile(mode="w", suffix=".R", delete=False)
+try:
+    tmp.write(r_script); tmp.close()
+    return subprocess.run(["Rscript", tmp.name], capture_output=True, text=True, timeout=t)
+finally:
+    try: os.unlink(tmp.name)
+    except OSError: pass
+```
+
 ## try/except Indentation (Critical)
 
 ```python

upstream/mims-harvard-ToolUniverse/skills/tooluniverse-cs-setup/templates/router_SKILL.md

@@ -0,0 +1,47 @@
+---
+lineage_type: import
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/bb632a34/skills/tooluniverse-cs-setup/templates/router_SKILL.md
+upstream_sha: bb632a34
+imported_at: 2026-07-01
+prompt_class: prompt
+upstream_changes: accepted
+name: tooluniverse-research
+description: Biomedical and scientific research via ToolUniverse's 2500+ tools across 133 structured workflows — drug research, disease research, gene and variant interpretation, cancer genomics, clinical trials, ADMET prediction, pharmacovigilance and adverse events, CRISPR screens, protein and structure analysis, epidemiology, and graded literature reviews. Use for "tell me about drug/gene/disease/variant X", multi-database investigations, cross-validating biomedical claims, ID translation, or any structured scientific lookup spanning FDA, ChEMBL, PubChem, ClinicalTrials.gov, UniProt, Ensembl, PubMed, and 200+ other databases.
+---
+
+# ToolUniverse Research
+
+Brings ToolUniverse's 2500+ scientific tools and 133 structured research workflows into Claude Science. The `tooluniverse` PyPI package supplies the tools; this skill bundles the workflows and a kernel sidecar that wires them up.
+
+## Setup
+
+Run all cells in the **`tooluniverse`** conda environment. Loading this skill auto-defines these helpers in the kernel:
+
+- `get_tu()` → a loaded `ToolUniverse` instance (cache redirected to the workspace, since `~/.tooluniverse` is read-only here).
+- `tu_workflows()` → list all 133 workflows (`name` + `description`).
+- `find_tu_workflow(query)` → rank workflows by relevance to a question.
+- `tu_workflow(name)` → the full step-by-step procedure for one workflow.
+- `tu_tool_info(tu, name)` → a tool's JSON spec, including its argument schema.
+
+## Answering a research question
+
+1. **Route** to a workflow: `find_tu_workflow("tell me about metformin")` returns ranked names. (Or browse `tu_workflows()`.)
+2. **Load** its procedure: `print(tu_workflow("tooluniverse-drug-research"))` and follow the steps.
+3. **Execute** the tools the workflow names. Every `ToolName(args)` reference maps to:
+   ```python
+   tu = get_tu()
+   tu.run({"name": "PubChem_get_CID_by_compound_name",
+           "arguments": {"name": "metformin"}})
+   ```
+4. **Confirm argument names** before a call if unsure — `tu_tool_info(tu, "PubChem_get_CID_by_compound_name")` shows the exact schema. Workflow prose abbreviates arguments; the schema is authoritative.
+5. **Discover tools** at runtime when no workflow fits:
+   ```python
+   tu.run({"name": "Tool_Finder_Keyword",
+           "arguments": {"description": "drug adverse events", "limit": 10}})
+   ```
+
+## Notes
+
+- Most tools work without API keys. A few (NCBI, OncoKB, NVIDIA, …) unlock enhanced access when keys are set in the env — add under Customize → Credentials, then expose them in the `tooluniverse` env.
+- Workflows are self-contained: report templates, checklists, and tool references are appended to each as appendices.
+- These workflows emphasize *looking things up* over recalling them — when a workflow says query a database, run the tool rather than answering from memory.

upstream/mims-harvard-ToolUniverse/skills/tooluniverse-variant-interpretation/SKILL.md

@@ -1,12 +1,12 @@
 ---
 lineage_type: import
-upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-variant-interpretation/SKILL.md
-upstream_sha: e2520a96
-imported_at: 2026-06-26
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/3038dcbe/skills/tooluniverse-variant-interpretation/SKILL.md
+upstream_sha: 3038dcbe
+imported_at: 2026-06-30
 prompt_class: prompt
 upstream_changes: accepted
 name: tooluniverse-variant-interpretation
-description: Clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Use for VUS classification, pathogenicity assessment with cited criteria, structure-based variant impact (AlphaFold/PDB), and producing clinical-grade variant reports for return of results or molecular tumor boards.
+description: Clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Use for VUS classification, pathogenicity assessment with cited criteria, structure-based variant impact (AlphaFold/PDB), non-coding/regulatory variant effect prediction with sequence deep-learning models (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2), and producing clinical-grade variant reports for return of results or molecular tumor boards. Use this whenever a user asks about a variant's significance, an intronic/promoter/enhancer/UTR non-coding variant's functional impact, or needs ACMG classification — even if they don't say "ACMG".
 disable-model-invocation: true
 ---
 
@@ -43,7 +43,7 @@ When asked about a variant's significance, query ClinVar/gnomAD/CIViC FIRST. Nev
 ```
 Phase 1: VARIANT IDENTITY        → Normalize HGVS, map gene/transcript/consequence
 Phase 2: CLINICAL DATABASES       → ClinVar, gnomAD, OMIM, ClinGen, COSMIC, SpliceAI
-Phase 2.5: REGULATORY CONTEXT     → ChIPAtlas, ENCODE (non-coding variants only)
+Phase 2.5: REGULATORY CONTEXT     → ChIPAtlas/ENCODE annotation + DL variant-effect (AlphaGenome/Enformer/Borzoi/ChromBPNet/Evo2) (non-coding only)
 Phase 3: COMPUTATIONAL PREDICTIONS → CADD, AlphaMissense, EVE, SIFT/PolyPhen
 Phase 4: STRUCTURAL ANALYSIS      → PDB/AlphaFold2, domains, functional sites (VUS/novel)
 Phase 4.5: EXPRESSION CONTEXT     → CELLxGENE, GTEx tissue expression
@@ -88,7 +88,23 @@ See `CODE_PATTERNS.md` for implementation details.
 
 Apply for intronic (non-splice), promoter, UTR, or intergenic variants near disease genes.
 
-Tools: `ChIPAtlas_enrichment_analysis`, `ChIPAtlas_get_peak_data`, `ENCODE_search_experiments`, `ENCODE_get_experiment`
+**Annotation — what regulatory element is here:** `ChIPAtlas_enrichment_analysis`, `ChIPAtlas_get_peak_data`, `ENCODE_search_experiments`, `ENCODE_get_experiment`. These tell you whether the variant falls in a known TF-binding peak, enhancer, or open-chromatin region.
+
+**Prediction — what the variant *does* to regulation:** annotation says an element is present, not whether this specific allele disrupts it. Sequence-based deep-learning models answer that directly: they read the reference and alternate DNA windows and predict the change in regulatory signal. This is what turns "the variant is in an enhancer" into "the variant is predicted to reduce accessibility/expression in the relevant tissue" — the mechanistic evidence ACMG PS3/PP3 actually needs for a non-coding variant, where SIFT/PolyPhen/AlphaMissense do not apply.
+
+| Tool | Predicts | Context | Access |
+|---|---|---|---|
+| `AlphaGenome_score_variant` | Δ across RNA-seq / ATAC / CAGE / splice tracks (frontier accuracy, single-base) | up to 1 Mb | hosted API — needs `ALPHA_GENOME_API_KEY` |
+| `run_enformer_variant_effect` | Δ across 5,313 human tracks (expression, chromatin, TF binding) | 196 kb | remote MCP server |
+| `run_borzoi_variant_effect` | Δ in RNA-seq coverage (expression / polyA / splicing emphasis) | 524 kb | remote MCP server |
+| `run_chrombpnet_variant_effect` | Δ in chromatin accessibility (ATAC / DNase), base-resolution | ~2 kb | remote MCP server |
+| `Evo2_score_variant` | Genome-foundation-model delta log-likelihood; covers coding **and** non-coding | up to 1 Mb | hosted NIM — needs `NVIDIA_API_KEY` |
+
+**Reading the score:** these return Δ (alt − ref) effect sizes, *not* calibrated pathogenicity probabilities. A large predicted disruption in a tissue-relevant track is mechanistic support (PS3_supporting / PP3) for a non-coding variant; near-zero across tracks supports BP4. Rank or calibrate against known regulatory variants rather than applying an absolute cutoff.
+
+**Which to pick:** start with `AlphaGenome_score_variant` (broadest readout, longest context, frontier accuracy) when its key is set; `run_enformer_variant_effect` / `run_borzoi_variant_effect` are the named, self-hostable equivalents (Enformer for general regulation, Borzoi when expression/splicing is the question); `run_chrombpnet_variant_effect` when the hypothesis is specifically chromatin accessibility; `Evo2_score_variant` as a sequence-only check that also works on coding variants. If no key/server is provisioned, fall back to the ChIPAtlas/ENCODE annotation above and note the predictive gap rather than guessing.
+
+**Inputs:** `AlphaGenome_score_variant` takes `chromosome` + `position` + `reference_bases`/`alternate_bases` (+ `output_type`, `sequence_length`); `Evo2_score_variant` takes a DNA window as `sequence` + `position` + `alternate` (point substitution) or `ref_sequence`/`alt_sequence`, plus optional `model` (`evo2-40b` default, `evo2-7b` faster); the Enformer/Borzoi/ChromBPNet remote tools take the variant locus and score the change over their output tracks.
 
 ## Phase 2.9: Short-Circuit Check
 

upstream/mims-harvard-ToolUniverse/skills/tooluniverse-variant-interpretation/TOOLS_REFERENCE.md

@@ -2,9 +2,9 @@
 title: "Clinical Variant Interpreter - Tool Reference"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-variant-interpretation/TOOLS_REFERENCE.md
-upstream_sha: e2520a96
-imported_at: 2026-06-26
+upstream_source: https://github.com/mims-harvard/ToolUniverse/blob/3038dcbe/skills/tooluniverse-variant-interpretation/TOOLS_REFERENCE.md
+upstream_sha: 3038dcbe
+imported_at: 2026-06-30
 prompt_class: prompt
 upstream_changes: accepted
 author: upstream
@@ -626,6 +626,20 @@ peaks = tu.tools.ChIPAtlas_get_peak_data(
 | `ENCODE_get_experiment` | Experiment details | `accession` |
 | `ENCODE_get_biosample` | Sample annotations | `accession` |
 
+### Sequence Deep-Learning Variant-Effect Predictors
+
+Predict the functional impact of a non-coding (and, for Evo 2, any) variant directly from sequence — the mechanistic evidence (PS3_supporting / PP3) that SIFT/PolyPhen/AlphaMissense cannot give for non-coding loci. Outputs are Δ (alt − ref) effect sizes, not calibrated probabilities.
+
+| Tool | Predicts | Access |
+|------|----------|--------|
+| `AlphaGenome_score_variant` | RNA-seq/ATAC/CAGE/splice track Δ (1 Mb, single-base) | hosted API — `ALPHA_GENOME_API_KEY` |
+| `run_enformer_variant_effect` | Δ across 5,313 human tracks (196 kb) | remote MCP server |
+| `run_borzoi_variant_effect` | RNA-seq coverage Δ (expression/splicing, 524 kb) | remote MCP server |
+| `run_chrombpnet_variant_effect` | chromatin accessibility Δ (ATAC/DNase, base-res) | remote MCP server |
+| `Evo2_score_variant` | genome-LM delta log-likelihood; coding + non-coding | hosted NIM — `NVIDIA_API_KEY` |
+
+**Inputs**: `AlphaGenome_score_variant` → `chromosome`,`position`,`reference_bases`,`alternate_bases`,`output_type`,`sequence_length`. `Evo2_score_variant` → `sequence`+`position`+`alternate` (or `ref_sequence`/`alt_sequence`), optional `model` (`evo2-40b`/`evo2-7b`). The Enformer/Borzoi/ChromBPNet remote tools take the variant locus. See SKILL.md Phase 2.5 for selection guidance.
+
 **Example - Get regulatory annotations**:
 ```python
 # Search for regulatory data near variant

upstream/yboulaamane-awesome-drug-discovery/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Awesome Drug Discovery [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/b8fbd716/README.md
-upstream_sha: b8fbd716
-imported_at: 2026-06-26
+upstream_source: https://github.com/yboulaamane/awesome-drug-discovery/blob/475719b9/README.md
+upstream_sha: 475719b9
+imported_at: 2026-06-27
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -247,6 +247,7 @@ A meticulously curated resource list focused on computational methods for drug d
 
 ## Interaction Analysis and Visualization
 - [PLIP](https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index) - Protein-ligand interaction profiling.
+- [posecheck-fast](https://github.com/LigandPro/posecheck-fast) - High-throughput docking pose validation with symmetry-corrected RMSD and lightweight distance and clash filters.
 - [GetContacts](https://getcontacts.github.io/index.html) - Compute and visualize noncovalent interactions from structures and MD trajectories.
 - [LigPlot+](https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) - 2D interaction diagrams.
 - [Discovery Studio Visualizer](https://discover.3ds.com/discovery-studio-visualizer-download) - Advanced visualization.
@@ -369,6 +370,7 @@ A meticulously curated resource list focused on computational methods for drug d
 - [Click2Drug](https://www.click2drug.org/) - CADD software and databases directory.
 - [Galaxy Europe](https://usegalaxy-eu.github.io/index-cheminformatics.html) - Galaxy instance for cheminformatics.
 - [CADD Vault](https://drugbud-suite.github.io/CADD_Vault/) - CADD resources repository.
+- [HEDGEHOG](https://github.com/LigandPro/hedgehog) - Stage-based evaluation pipeline for generative molecular design with filters, retrosynthesis checks, docking, pose validation, and reports.
 - [BioMoDes](https://abeebyekeen.com/biomodes-biomolecular-structure-prediction/) - Biomolecular structure prediction and modeling tools.
 - [PlayMolecule](https://open.playmolecule.org/landing) - Interactive molecular modeling and simulation platform.
 - [Ertl Molecular](https://ertlmolecular.com/) - Cheminformatics tools for medicinal chemists, including scaffold analysis, ring replacement, and property calculators.