git:20260906.f9cef19 to git:20260915.5e28404

9 added, 1 removed. Audit A to A.

---

name: bulk_refresh

description: Run registered insight workflows over selected startup and community datasets with prerequisite ordering. Use for an intentional batch refresh or an externally scheduled run.

---

# Bulk refresh

Prepare selected datasets and execute their applicable registry workflows.

## Operations and effects

- The async `bulk_refresh(datasets=None, skills=None)` returns `None` on success.

+ The async `bulk_refresh(datasets=None, skills=None, exclude=None)` returns `None` on success.

Selectors are comma-separated strings or `all`. No dataset selector means

active startup/community datasets; `all` includes inactive ones. Named datasets

may also be inactive. Generated datasets are excluded. Omitted skills select

the full registry; named skills expand their prerequisites.

+ `exclude` / `--exclude` accepts comma-separated source dataset names using the

+ same normalization as `--datasets`. With exclusions and no dataset selector,

+ start from all startup/community datasets, including inactive ones. Otherwise

+ subtract exclusions from the explicit selection. Unknown excluded source names

+ raise before preparation; known names outside the selection have no effect.

+ Excluding every selected dataset logs and returns without doing work.

Prepare and synchronize every selected dataset before running skills.

Independent ready jobs run concurrently. Cross-domain dependencies cover

prerequisite nodes within the selected dataset scope; they do not add datasets.

The [registry](../skill_registry.py) owns supported domains and dependencies.

Pre-ingestion failures skip affected jobs and their dependants. Skill failures

also propagate skips, while independent work continues. Completed artifacts

remain saved. After runnable work finishes, problems raise `BulkRefreshError`;

an empty dataset selection logs and returns.

Side effects are those of preparation and selected skills, including source

imports, discovery, enrichment, conversion, indexing and report generation.

Each skill owns artifact reuse. The command does not itself install a schedule.

## Usage

```bash

conda run -n sictic-env python -m skills.bulk_refresh --datasets example-startup,sictic-members --skills expert-search

conda run -n sictic-env python -m skills.bulk_refresh --datasets all --skills startup-profile

+ conda run -n sictic-env python -m skills.bulk_refresh --exclude sictic-members --skills persons-in-dataset

```

This operational tool has no harness command.

## References

- [Implementation](bulk_refresh.py), [CLI](__main__.py)

- [Registry contract](../standards_and_architecture/SKILL.md#bulk-refresh-registry)