Agent skill
social-value-report
Generate the social value report from CSV inputs by detecting CSVs, anonymising them, and producing a month-by-month markdown report.
Install this agent skill to your Project
npx add-skill https://github.com/sofer/social-value/tree/main/skills/social-value-report
SKILL.md
Social Value Report
Use this skill when the user asks to produce or refresh the social value report.
Workflow
- 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.
- 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.
- 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).
- 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.
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