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 skills/daffy0208/ai-dev-standards/forensic-data-engineernpx skills add daffy0208/ai-dev-standards --skill forensic-data-engineergit clone --depth 1 https://github.com/daffy0208/ai-dev-standardsWrote 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/daffy0208/ai-dev-standards/forensic-data-engineer)<a href="https://agentmods.dev/skills/daffy0208/ai-dev-standards/forensic-data-engineer"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/forensic-data-engineer.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 5.1 | $0.00025 | $0.07282 |
| Opus 5 | $0.00013 | $0.03641 |
| Sonnet 5 | $0.00005 | $0.01456 |
| Haiku 4.5 | $0.00003 | $0.00728 |
Grade A, and why
forensic-data-engineer 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 6d 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 — 1,185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forensic Data Engineer Skill
I help you investigate data anomalies, detect fraud, analyze audit trails, and ensure data integrity and compliance.
What I Do
Forensic Analysis:
- Anomaly detection and pattern recognition
- Fraud detection and prevention
- Breach investigation and root cause analysis
- Data integrity verification
Audit & Compliance:
- Audit trail analysis and reconstruction
- Chain of custody maintenance
- Regulatory compliance (GDPR, SOC2, HIPAA)
- Access control auditing
Data Recovery:
- Forensic recovery of deleted data
- Historical data reconstruction
- Change detection and unauthorized modifications
- Data lineage and provenance tracking
Forensic Patterns
Pattern 1: Audit Trail Implementation
Use case: Track all data changes for compliance and investigation
// lib/forensics/audit-trail.ts
interface AuditEntry {
id: string
timestamp: Date
userId: string
action: 'CREATE' | 'UPDATE' | 'DELETE' | 'READ'
tableName: string
recordId: string
oldValue?: any
newValue?: any
ipAddress: string
userAgent: string
sessionId: string
}
export async function createAuditLog(entry: Omit<AuditEntry, 'id' | 'timestamp'>) {
return await db.auditLog.create({
data: {
...entry,
timestamp: new Date()
}
})
}
// Middleware for automatic audit logging
export function withAudit<T extends (...args: any[]) => Promise<any>>(
operation: T,
metadata: { tableName: string; action: AuditEntry['action'] }
): T {
return (async (...args: any[]) => {
const startTime = Date.now()
const { tableName, action } = metadata
try {
// Capture before state for UPDATE/DELETE
let oldValue
if (action === 'UPDATE' || action === 'DELETE') {
oldValue = await captureCurrentState(tableName, args[0])
}
// Execute operation
const result = await operation(...args)
// Capture after state
const newValue = action !== 'DELETE' ? result : null
// Log audit entry
await createAuditLog({
userId: getCurrentUser().id,
action,
tableName,
recordId: args[0],
oldValue,
newValue,
ipAddress: getClientIp(),
userAgent: getClientUserAgent(),
sessionId: getSessionId()
})
return result
} catch (error) {
// Log failed attempt
await createAuditLog({
userId: getCurrentUser().id,
action,
tableName,
recordId: args[0],
ipAddress: getClientIp(),
userAgent: getClientUserAgent(),
sessionId: getSessionId()
})
throw error
}
}) as T
}
// Usage
const updateUser = withAudit(
async (userId: string, data: any) => {
return await db.user.update({
where: { id: userId },
data
})
},
{ tableName: 'users', action: 'UPDATE' }
)
What ships with it
1 file 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.
- 6d ago First seen · 1,185 lines · 25 tokens per session scan A 5757fc9fa21a
forensic-data-engineer is a skill published in the GitHub repository daffy0208/ai-dev-standards (36 stars, last pushed 8mo ago), licensed MIT. It adds 25 tokens to every session and 7,282 once invoked, about $0.0001 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 2026-08-30.
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