Omh Data Share Review
[omh] Before data leaves the team -- a dataset, log, ticket export or transcript...
Install
npx skills add rlaope/oh-my-hermes --skill omh-data-share-review -g -a claude-code -y- Repository
- rlaope/oh-my-hermes5k
- Category
- Data and ML
- Updated
- 3d ago
- License
- MIT
- Context cost
- Mediumabout 2,460 tokens
- Runs scripts
- No, instructions only
What it does
[omh] Before data leaves the team -- a dataset, log, ticket export or transcript excerpt bound for a vendor, a public issue, a model provider or a training corpus: classify every field, minimize first, pseudonymize with the mapping kept local, and call it redacted only after an observed residual scan. Use when the user says: data-share-review, data share review, pii redaction, redact pii, pseudonymize, de-identify, anonymize the dataset, residual pii.
Data Share Review
This is an OMH data-share-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
data-share-review exists because data leaving the team had no owner: Hermes redacts identifiers in gateway chat and secrets in tool output, omh-security-safety-review reviews an agent's own exposure, and omh-legal-compliance-review reads the obligations, while nothing minimized a file's fields, kept its pseudonym mapping local, and refused to call it redacted before a scan of what actually leaves.
First Steps
- Ask where the file goes and what the destination needs it for before touching a field.
- Inventory the fields from the header and the schema before planning any masking.
Do Not Use When
- Whether the data may lawfully leave at all -- the lawful basis, a DPA, consent, or a cross-border transfer -- is a contract and compliance question; use
omh-legal-compliance-review, then return here for the fields. - The exposure is an agent's tool permissions, a secret in code, or a prompt-injection path rather than a file about to leave; use
omh-security-safety-review. - The ask is analysing the dataset itself -- trends, a cleanup, a chart; use
omh-data-analysis. - Filing the issue once its attachment is clean -- the title, labels, and reproduction steps; use
omh-github-issue-intake. - Building the training set or deciding whether to train once the corpus is clean; use
omh-model-finetuning. - Working the support queue -- triaging or answering tickets rather than exporting them; use
omh-support-operations.
Examples
Good example:
