git:20260816.f4f071d to git:20260908.42a154e

24 added, 7 removed. Audit A to A.

---

name: public-data-access

- description: "Plan, configure, validate, and document portable public-bioinformatics data acquisition. Use for GEO/GSE/GDS, SRA/ENA, TCGA/GDC, GTEx, DepMap, public expression matrices, raw reads, release files, manifests, resumable downloads, and reusable local caches. Keep the workflow provider-neutral: DepMap is one optional source, never the default architecture."

+ description: "Plan, validate, and document public-bioinformatics data acquisition for GEO/GSE/GSM/GPL/GDS, SRA/ENA, TCGA/GDC, GTEx, and DepMap. Covers expression matrices, raw reads, download manifests, caches, and optional geokit SOFT/Series Matrix acquisition for R workflows."

---

# Public Bioinformatics Data Access

Build a reproducible acquisition plan before downloading. Treat GEO, SRA/ENA,

GDC, GTEx, and DepMap as independent providers behind one provider-neutral

workflow. Do not require a provider-specific toolkit or machine-specific checkout.

## Workflow

1. **Clarify the dataset contract.** Identify the provider, accession/project,

release, modality, smallest useful data product, filters, target directory,

expected scale, and downstream analysis.

2. **Inspect before transfer.** Use available MCP/connectors or official

metadata endpoints to list releases, files, samples, sizes, and checksums.

Do not start a bulk transfer during discovery.

3. **Write a provider-neutral plan.** Run `scripts/public_data_plan.py init`.

Store the plan next to the future dataset as `download-plan.json`.

4. **Validate and review.** Run `validate`, show the user the resolved provider,

- transport, filters, limits, output location, and known size. Wait for explicit

- confirmation before a large, paid, authenticated, or overwrite-capable job.

+ transport, filters, limits, output location, and known size. For a large,

+ paid, authenticated, or overwrite-capable job, confirm that the user's

+ authorization covers this concrete transfer; ask only when it does not.

5. **Select the adapter at runtime.** Prefer an already available Wisp MCP tool

for metadata and small queries. Prefer official HTTPS/FTP or provider clients

for bulk files. Use an external project only when it is installed and record

its version in the plan/manifest.

6. **Acquire safely.** Reuse existing valid files, resume partial transfers when

supported, keep raw files immutable, and never place credentials in the plan.

7. **Verify and hand off.** Check expected files, byte sizes, checksums when

available, and sample/file counts. Generate `manifest.json` with the script.

## Provider routing

|---|---|---|---|

Read `references/provider-routing.md` before implementing or changing a

provider adapter. DepMap-specific flags or release semantics must stay inside

the DepMap adapter; they must not shape the common plan schema.

+ For GEO SOFT/Series Matrix parsing, sample metadata preparation, or

+ ExpressionSet acquisition in an R workflow, read

+ [references/geokit.md](references/geokit.md). geokit is optional; ordinary GEO

+ discovery does not require R or package installation.

## Create and validate a plan

+ Resolve `scripts/public_data_plan.py` against this skill's directory (the

+ `use_skill` result lists its path), and invoke that resolved script with a

+ Python 3.10+ interpreter. Keep the working directory at the project root so

+ relative plan/output paths belong to the project. The examples below abbreviate

+ the script path; quote the resolved path when it contains spaces. In an SSH/WSL

+ context, stage the helper there or use an existing copy in that context; a

+ desktop skill path is not automatically available remotely.

```bash

python scripts/public_data_plan.py init \

--provider geo \

--identifier GSE12345 \

--data-type series-matrix \

--output-dir data/public/geo/GSE12345 \

--plan data/public/geo/GSE12345/download-plan.json

python scripts/public_data_plan.py validate \

data/public/geo/GSE12345/download-plan.json

```

Filters are provider-specific but encoded uniformly as repeated `key=value`

pairs:

```bash

python scripts/public_data_plan.py init \

--provider gdc \

--identifier TCGA-BRCA \

--data-type expression \

--filter workflow_type="STAR - Counts" \

--filter sample_type="Primary Tumor" \

--max-files 20 \

--transport gdc-client \

--plan data/public/gdc/TCGA-BRCA/download-plan.json

```

The planner does not download data. It produces a reviewable contract. See

- `references/download-plan-schema.md` for the complete schema.

+ `references/download-plan-schema.md` for the complete schema. Validation checks

+ the plan structure; it does not probe URLs, enforce transfer limits, verify

+ installed packages, or approve a pending transfer. The selected adapter must

+ honor the plan's limits and resume behavior.

## Generate a manifest

After acquisition:

```bash

python scripts/public_data_plan.py manifest \

data/public/geo/GSE12345/download-plan.json \

--scan-dir data/public/geo/GSE12345 \

--output data/public/geo/GSE12345/manifest.json

```

Use SHA-256 for modest datasets and provider checksums for large archives. For

very large datasets, `--checksum none` is acceptable only when official

checksums or immutable object identifiers are recorded elsewhere.

## Safety and reproducibility rules

- Default to `overwrite=false`, `resume=true`, and the minimum useful subset.

- Never translate an exploratory request into “download everything.”

- Keep provider metadata, query/filter payloads, release/version, transport,

tool version, URLs/object identifiers, and validation results.

- Separate immutable source files from normalized/derived outputs.

- Do not treat a successful HTTP response as a valid dataset; verify content.

- Do not embed API keys, cookies, signed URLs, SSH keys, or bearer tokens.

- Use structured runs or a remote execution context for long transfers rather

than extending an interactive shell timeout.

- If an adapter or connector cannot perform the requested transfer, stop after

producing the validated plan and report the missing capability explicitly.

## Wisp Science integration

- Discover the live connector/tool catalog instead of assuming exact MCP tool

names; installations can expose different provider adapters.

- Use connectors for discovery and bounded queries, then official transfer

mechanisms for large files.

- Keep outputs under the active project, normally `data/public/<provider>/...`.

- - The optional `kernel.py` sidecar exposes plan creation and validation helpers

- in Wisp's persistent Python runtime.

+ - Invoke the planner as a standalone CLI; no Python REPL helper loading is

+ required. R-based acquisition can use geokit independently of the planner.

- Treat this skill as an acquisition/orchestration layer. Downstream QC,

statistics, annotation, and visualization belong to other skills.