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 ucsandman/dashclaw-skills --skill compliance-drift-evalsgit clone --depth 1 https://github.com/ucsandman/dashclaw-skillsWrote 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/ucsandman/dashclaw-skills/compliance-drift-evals)<a href="https://agentmods.dev/skills/ucsandman/dashclaw-skills/compliance-drift-evals"><img src="https://agentmods.dev/badge/skills/ucsandman/dashclaw-skills/compliance-drift-evals/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/ucsandman/dashclaw-skills/compliance-drift-evals"><img src="https://agentmods.dev/badge/skills/ucsandman/dashclaw-skills/compliance-drift-evals.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 | $0.00022 | $0.02034 |
| Opus 5 | $0.00011 | $0.01017 |
| Sonnet 5 | $0.00004 | $0.00407 |
| Haiku 4.5 | $0.00002 | $0.00203 |
Grade A, and why
compliance-drift-evals scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST "$DASHCLAW_BASE_URL/api/compliance/exports" \ This is a copy
100% identical to compliance-drift-evals — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compliance, Drift, Evaluations & Learning
DashClaw's analytical capabilities for governance evidence, behavioral monitoring, and agent quality tracking.
Compliance Exports
Generate audit-ready evidence bundles for regulatory frameworks.
Supported Frameworks
| Framework | ID | Description |
|---|---|---|
| SOC 2 | soc2 |
Service Organization Control |
| NIST AI RMF | nist-ai-rmf |
AI Risk Management Framework |
| EU AI Act | eu-ai-act |
European AI regulation |
| ISO 42001 | iso42001 |
AI Management System |
Create an Export
// V1 SDK
const exp = await claw.createComplianceExport({
name: 'Q1 2026 SOC 2 Audit',
frameworks: ['soc2'],
format: 'json', // or 'md'
window_days: 90,
include_evidence: true,
include_remediation: true,
include_trends: true
});
# API
curl -X POST "$DASHCLAW_BASE_URL/api/compliance/exports" \
-H "x-api-key: $DASHCLAW_API_KEY" \
-H "Content-Type: application/json" \
-d '{"name":"Q1 Audit","frameworks":["soc2"],"window_days":90}'
Scheduled Exports
await claw.createComplianceSchedule({
name: 'Weekly SOC 2',
frameworks: ['soc2'],
cron_expression: '0 9 * * 1', // Every Monday at 9am
window_days: 7,
include_evidence: true
});
Gap Analysis
const gaps = await claw.analyzeGaps('soc2');
// Returns: missing controls, partial coverage, recommendations
Coverage Trends
const trends = await claw.getComplianceTrends({ framework: 'soc2', limit: 12 });
// Monthly coverage scores over time
Drift Detection
Statistical behavioral drift detection using z-scores. Pure math — no LLM required.
6 Tracked Metrics
| Metric | What It Measures |
|---|---|
risk_score |
Are actions getting riskier? |
confidence |
Is agent confidence dropping? |
duration_ms |
Are actions taking longer? |
cost_estimate |
Are costs increasing? |
tokens_total |
Is token usage growing? |
learning_score |
Is the agent learning? |
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.
- 11d ago First seen · 322 lines · 22 tokens per session scan A 1bdcfbf46d62
compliance-drift-evals is a skill published in the GitHub repository ucsandman/dashclaw-skills (2 stars, last pushed 3d ago), licensed MIT. It adds 22 tokens to every session and 2,034 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to compliance-drift-evals, differing in 0 lines, and is treated as a copy.
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