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 VincentChuWaiChow/vanguard-frontier-agentic --skill data-classification-to-dlp-protocolgit clone --depth 1 https://github.com/VincentChuWaiChow/vanguard-frontier-agenticWrote 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/vincentchuwaichow/vanguard-frontier-agentic/data-classification-to-dlp-protocol)<a href="https://agentmods.dev/skills/vincentchuwaichow/vanguard-frontier-agentic/data-classification-to-dlp-protocol"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/data-classification-to-dlp-protocol/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/vincentchuwaichow/vanguard-frontier-agentic/data-classification-to-dlp-protocol"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/data-classification-to-dlp-protocol.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00131 | $0.01875 |
| Opus 5 | $0.00066 | $0.00937 |
| Sonnet 5 | $0.00026 | $0.00375 |
| Haiku 4.5 | $0.00013 | $0.00187 |
Grade A, and why
data-classification-to-dlp-protocol 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 7d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Classification to DLP Protocol
Purpose
This skill defines how sensitive data is discovered, classified using Microsoft Purview sensitivity labels, protected by Data Loss Prevention policies, and monitored for classification coverage and DLP effectiveness across Microsoft 365, Power Platform, and Dataverse environments. It ensures classification decisions are driven by a defined taxonomy before policies are deployed, that DLP coverage matches the taxonomy, and that label adoption is tracked over time. No agent creates or modifies sensitivity labels or DLP policies; those are production-impacting actions requiring the Purview compliance administrator and the data owner.
When to use
- An organization needs to establish or review a sensitivity label taxonomy and map it to DLP policies across Microsoft 365 workloads.
- DLP policy coverage gaps are suspected (workloads, data types, or locations not yet covered).
- Label adoption rates need to be assessed and improvement actions identified.
- A Power Platform or Dataverse environment needs to be assessed for data classification and DLP policy alignment.
- A new data type (e.g., a new category of regulated personal data) needs to be classified and protected.
When NOT to use
- Sensitivity labels or DLP policies are already deployed and fully validated — hand off to the Purview admin for ongoing monitoring.
- The matter involves a suspected active data loss or exfiltration event — use the incident-to-remediation-protocol instead.
- The classification requirement involves health, financial, or regulated data requiring specialist legal interpretation — escalate to the appropriate compliance or legal counsel first.
- The Purview tenant is not yet configured — initial tenant configuration is a Purview admin task, not a protocol task.
Participating agents
m365-copilot-readiness-governance-agent— primary: assesses Microsoft 365 data classification readiness, sensitivity label taxonomy, and Copilot surface area data protection gapspower-platform-governance-dataverse-security-agent— secondary: assesses Dataverse environment data classification, column-level security, and Power Platform DLP policy alignmentm365-maestro-agent— orchestrates cross-workload classification and DLP review; escalation path to Microsoft Purview information protection specialists (planned: purview-information-protection-specialist-agent)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 96 lines · 131 tokens per session scan A edc7d555b9b7
data-classification-to-dlp-protocol is a skill published in the GitHub repository VincentChuWaiChow/vanguard-frontier-agentic (22 stars, last pushed 3d ago), licensed Apache-2.0. It adds 131 tokens to every session and 1,875 once invoked, about $0.0007 per session on Opus 5. 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 2026-09-04.
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