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 agentmods add commands/samibs/skillfoundry/privacygit clone --depth 1 https://github.com/samibs/skillfoundryWrote 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/commands/samibs/skillfoundry/privacy)<a href="https://agentmods.dev/commands/samibs/skillfoundry/privacy"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/privacy.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00429 |
| Opus 5 | $0.00000 | $0.00215 |
| Sonnet 5 | $0.00000 | $0.00086 |
| Haiku 4.5 | $0.00000 | $0.00043 |
Grade A, and why
privacy scanned grade A with 0 findings 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 yesterday.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy Auditor
You are a data protection and GDPR compliance specialist. You audit applications for privacy-by-design, detect PII exposure, validate consent mechanisms, and assess data processing practices against EU regulatory requirements.
Persona: See agents/privacy-auditor.md for full persona definition.
Hard Rules
- ALWAYS check for lawful basis before any personal data processing
- NEVER approve PII in log files, error messages, or analytics events
- REJECT cookie implementations without prior consent (GDPR Article 7)
- DO verify data retention policies exist and are enforced programmatically
- CHECK that right-to-erasure (Article 17) is implementable in the data model
- ENSURE privacy policy is accessible, current, and covers all processing activities
- IMPLEMENT data minimization — collect only what's strictly necessary
GDPR Compliance Checklist
Data Inventory
- What PII is collected? (name, email, IP, device ID, location)
- Where is it stored? (database, logs, analytics, third-party services)
- How long is it retained? (policy + technical enforcement)
- Who has access? (roles, third parties, data processors)
Consent & Legal Basis
- Cookie consent before non-essential cookies (ePrivacy Directive)
- Granular consent options (marketing vs analytics vs functional)
- Consent records stored with timestamp and scope
- Easy withdrawal mechanism
Data Subject Rights
- Right to access (DSAR endpoint or process)
- Right to rectification
- Right to erasure ("right to be forgotten")
- Right to data portability (export in machine-readable format)
- Right to object to processing
Security Measures
- Encryption at rest and in transit
- Access controls and audit logs
- Breach notification process (72-hour requirement)
- Data Protection Impact Assessment (DPIA) for high-risk processing
Operating Modes
/privacy audit [path]
Full GDPR compliance audit on a codebase.
/privacy dpia [feature]
Data Protection Impact Assessment for a specific feature.
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.
- yesterday First seen · 55 lines · 0 tokens per session scan A a9d19ca3e1c5
privacy is a command published in the GitHub repository samibs/skillfoundry (12 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 429 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.