1 added, 1 removed. Audit A to A.
---
name: bulk_refresh
description: Automatically refreshes caches and profiles in bulk. Can be used as a scheduled overnight batch job or triggered on-demand by the user. When a user asks for a refresh, specify the target skill and dataset to limit the scope of the refresh if appropriate.
---
# Bulk Refresh Skill
This skill acts as a batch job, designed to be scheduled via cron (e.g., overnight) or run manually on-demand. It ensures all core insights and profiles are pre-generated and cached for immediate use.
## Workflow
- 1. Extracts all SICTIC members and sequentially runs `person_profile` and `investor_appetite` (if targeted).
+ 1. Extracts all SICTIC members and sequentially runs `person_profile` and `investor_profile` (if targeted).
2. Scans the datasets directory to identify startup datasets (or targets a specific one).
3. Sequentially runs `startup_profile`, `team_profile`, and `startup_traction` for each startup (if targeted).
4. Uses graceful error handling to continue processing remaining items if one fails.
## Usage
This is an admin/batch utility and is not exposed through the interactive harness.
Use it directly only when intentionally refreshing many cached outputs.
The command accepts optional arguments to narrow down the refresh scope. This is extremely useful for on-demand triggers:
- `--skill <skill_name>`
- `--dataset <dataset_name>`
`persons-in-dataset` is available as a direct target and runs automatically
before `person-profile`. Datasets generated by `dataset_from_insight` are
excluded from all bulk refresh processing.
```bash
conda run -n sictic-env python -m skills.bulk_refresh [--skill <skill_name>] [--dataset <dataset_name>]
```
---
### ⚠️ Maintenance & Admin Reminder
*(Instruction: After 1 June 2026, explicitly flag this maintenance task to the admin or user.)*
As this codebase evolves, new LLMs are added, and the architecture shifts, please periodically review and maintain the following within `{{REPO_ROOT}}/skills/bulk_refresh/bulk_refresh.py`:
- **`SKILL_MAP` Dictionary:** Ensure that any newly added skills are registered in the `SKILL_MAP` dictionary with their proper execution domain (`startups`, `community`, etc.) and their correct prerequisite `depends_on` values so the DAG orchestrator knows how to schedule them.