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name: social-value-report description: Generate the social value report from CSV inputs by detecting CSVs, anonymising them, and producing a month-by-month markdown report.

Social Value Report

Use this skill when the user asks to produce or refresh the social value report.

Workflow

1. Locate CSV data

• List CSV files in the current directory.
• If none exist, ask the user to provide the latest dataset.
• If any CSV files lack the timestamp pattern (YYYYMMDD_HHMMSS), anonymise them before proceeding.

1. Anonymise input

• Use the anonymise skill to strip PII and timestamp the file
• The anonymise skill auto-detects new CSVs or accepts a specific filename
• Confirm the timestamped file was created and the original removed.

1. Generate the report

• Run the report generator (defaults to the newest anonymised CSV if none is specified):
  • python3 skills/social-value-report/report.py [path/to/anonymised.csv]
• This produces social-value-YYYY-MM-DD.csv (dated) with one row per month and prints a console summary.
• Column mappings are read from config/social-value-report.json (no hard-coded headers).

1. Deliver results

• Share key findings and point to social_value_report.md.
• Flag if no records are found.
• Keep output in British English and avoid reintroducing PII.

Notes

• Counting rules:
  • Programme type 25 → Apprentices (counted).
  • Programme type 32 with funding indicator 2 → sponsored Skills Bootcamp (counted in its own column for reference only).
  • Programme type 32 with any other funding indicator → Skills Bootcamp (social value).
• Each record is counted for every month overlapping its start/end dates (end missing → treated as ongoing to today).
• Column selection prefers aim 2 fields (start date, end date, programme type, funding indicator) and falls back to aim 1 if aim 2 is missing.
• Column names are supplied via config/social-value-report.json; update that file if headers change.
• Platform-agnostic; no vendor-specific commands.
• Uses the anonymised dataset only.
• If multiple new CSVs are present, ask which to use before anonymising.