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.
git 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/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/commands/samibs/skillfoundry/privacy"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/privacy.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 | $0.00000 | $0.00429 |
| Opus 5.5 | $0.00000 | $0.00172 |
| Sonnet 5.5 | $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 1mo 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.
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.
- 1mo ago 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 17d ago), 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
act
Execute the implementation plan with TDD.
tailtest
Generate or update tests for $ARGUMENTS.
sessions
Generate implementation session prompts from a PRD or research doc.
deploy
Command "deploy" from PraveenJayaprakash-JP/repomemory, covering deployment workflow, 1. pre-deploy checks, 2. build verification, 3. environment validation and 4. deployment steps (platform detection).
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.