Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add obielin/responsible-ai-skills --skill responsible-data-handlinggit clone --depth 1 https://github.com/obielin/responsible-ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/obielin/responsible-ai-skills/responsible-data-handling)<a href="https://agentmods.dev/skills/obielin/responsible-ai-skills/responsible-data-handling"><img src="https://agentmods.dev/badge/skills/obielin/responsible-ai-skills/responsible-data-handling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/obielin/responsible-ai-skills/responsible-data-handling"><img src="https://agentmods.dev/badge/skills/obielin/responsible-ai-skills/responsible-data-handling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.01553 |
| Opus 5 | $0.00018 | $0.00776 |
| Sonnet 5 | $0.00007 | $0.00311 |
| Haiku 4.5 | $0.00004 | $0.00155 |
Grade B, and why
responsible-data-handling scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
chmod 640 data/sensitive_dataset.csv # owner + group read, no world access How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Responsible Data Handling
Data is the foundation of AI. How you handle it determines whether the system is lawful, fair, and trustworthy. Before writing a single line of data pipeline code, complete this skill.
Step 1: Classify Your Data
Classify every data source before touching it:
| Class | Description | Examples | Requirements |
|---|---|---|---|
| Public | No personal data, publicly available | ONS statistics, OpenStreetMap | Standard care |
| Internal | Organisation data, no personal info | Product logs (anonymised), config | Access controls |
| Personal | Identifies or can identify individuals | Names, emails, IP addresses | UK GDPR applies |
| Sensitive Personal | Special category under UK GDPR | Health, ethnicity, religion, biometrics | Explicit legal basis required |
| High Risk | Personal + significant impact decisions | Benefits data, criminal records | DPA 2018 Schedule 1 + DPIA |
Action: Label every dataset in your code:
# DATA CLASSIFICATION: Sensitive Personal (health records)
# LEGAL BASIS: Article 9(2)(h) — medical treatment purposes
# RETENTION: 8 years per NHS Records Management Code
# ACCESS: Restricted to authorised clinical staff only
df_patients = load_patient_records(...)
Step 2: Apply Data Minimisation
Load only what you need. Every unnecessary field is a liability:
# BAD: Loading everything and filtering later
df = pd.read_csv('patients.csv')
df = df[['age', 'diagnosis']] # the name and postcode were loaded into memory
# GOOD: Select columns at load time
df = pd.read_csv('patients.csv', usecols=['age', 'diagnosis'])
# GOOD: SQL — select only what's needed
query = "SELECT age, diagnosis FROM patients WHERE cohort = 'study_group'"
df = pd.read_sql(query, conn)
Rule: If a column is not in your model's feature set or your analysis plan, do not load it.
Step 3: Handle Personal Identifiers
Anonymisation vs Pseudonymisation
| Technique | What It Does | Still Personal Data? |
|---|---|---|
| Anonymisation | Irreversibly removes all identifiers | No |
| Pseudonymisation | Replaces identifiers with tokens | Yes — UK GDPR applies |
| Aggregation | Reports only group statistics | No (if k≥5) |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 219 lines · 37 tokens per session scan B c1359bca9ef7
responsible-data-handling is a skill published in the GitHub repository obielin/responsible-ai-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 1,553 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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temporal
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Run the full Semantica semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings.