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/HermeticOrmus/LibreSecOps-Claude-CodeWrote 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/hermeticormus/libresecops-claude-code/data-flow-map)<a href="https://agentmods.dev/commands/hermeticormus/libresecops-claude-code/data-flow-map"><img src="https://agentmods.dev/badge/commands/hermeticormus/libresecops-claude-code/data-flow-map/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/hermeticormus/libresecops-claude-code/data-flow-map"><img src="https://agentmods.dev/badge/commands/hermeticormus/libresecops-claude-code/data-flow-map.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 | $0.01273 |
| Opus 5 | $0.00000 | $0.00636 |
| Sonnet 5 | $0.00000 | $0.00255 |
| Haiku 4.5 | $0.00000 | $0.00127 |
Grade A, and why
data-flow-map 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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/data-flow-map
Map personal data flows through a system, identifying collection points, processing activities, storage locations, sharing partners, and retention periods.
Trigger
Use when you need to understand and document how personal data moves through a system. Required for:
- GDPR Article 30 Records of Processing Activities
- Data Protection Impact Assessments (input)
- Privacy review foundation
- Data subject access request implementation
- Data breach impact assessment (what data was where)
- System migration planning (ensuring all personal data is identified)
Input
- System name and purpose: What the system does
- Architecture description: Components, databases, APIs, external services
- User types: Who interacts with the system (customers, employees, partners)
- Known data elements: Personal data types collected (if known; will be expanded during mapping)
- Third-party services: External services, APIs, analytics, payment processors
- Deployment: Cloud provider(s), regions, CDN
Process
-
Collection point identification -- Map every point where personal data enters the system:
- User registration forms
- Login/authentication
- Profile updates
- Content creation (posts, comments, uploads)
- Transaction processing
- Customer support interactions
- Automated collection (cookies, device fingerprinting, analytics, logging)
- Third-party data imports (social login, data enrichment)
-
Processing activity mapping -- For each data element, trace:
- What processing occurs (storage, analysis, transformation, profiling)
- Which system components handle the data
- What internal APIs transfer data between components
- Where data is transformed, aggregated, or derived
-
Storage identification -- Map all locations where personal data persists:
- Primary databases (relational, NoSQL, graph)
- Search indices (Elasticsearch, Algolia)
- Cache layers (Redis, Memcached)
- File storage (S3, local filesystem, CDN)
- Log aggregators (CloudWatch, ELK, Splunk)
- Analytics platforms (Google Analytics, Mixpanel, Amplitude)
- Email/notification services (SendGrid, Twilio)
- Backup systems (database backups, disaster recovery)
- Development/staging environments (often contain production data copies)
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.
- 9d ago First seen · 143 lines · 0 tokens per session scan A a747466d7d1c
data-flow-map is a command published in the GitHub repository HermeticOrmus/LibreSecOps-Claude-Code (4 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,273 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
scope
Mandatory pre-flight scope check — verify an asset is in scope BEFORE any HTTP touch. Deterministic (deny-wins, default-deny) via engine/scope.py against the engagement's scope.md. Blocks out-of-scope testing. Usage: /scope [ ...].
sicario.controls
Generate or update docs/compliance/control-applicability.md.
sicario.evidence
Generate evidence artifacts.
compliance-check
Check IaC files against a specific compliance framework.
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.