Dingdawg Compliance — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Dingdawg Compliance (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Colorado SB 205 AI Act compliance scanner. Run it in 60 seconds. Get your score. Know your gaps before June 30, 2026.
pip install dingdawg-compliance
python3 -m dingdawg_compliance scanColorado SB 205 requires any company using AI for consequential decisions (employment, housing, credit, insurance, healthcare, education) to:
This tool scores your readiness across all 25 SB 205 controls. Free. No signup. Runs locally.
pip install dingdawg-complianceRequires Python 3.9+. No external dependencies — stdlib only.
python3 -m dingdawg_compliance scanWalk through all 25 controls. Answer y/n/skip for each. Get your score at the end.
Example output:
──────────────────────────────────────────────────────
Overall Score: 44/100 [████████░░░░░░░░░░░░] NEEDS WORK
──────────────────────────────────────────────────────
Category Scores:
~ scope 100%
✗ impact_assessment 0%
✗ transparency 33%
✗ appeal 0%
~ governance 50%
✗ bias_testing 0%
✗ data_governance 0%
✗ incident_response 50%
✓ audit 100%
⚠ Critical gaps (2) — mandatory under SB 205:
• CO-3 Pre-Deployment Impact Assessment
• CO-6 Consumer Disclosure at Point of Decision
Need the full remediation report?
→ dingdawg.com/compliance (CO SB 205 gap report — $199)python3 -m dingdawg_compliance score responses.jsonFormat for responses.json:
{
"CO-1": true,
"CO-2": true,
"CO-3": false,
"CO-4": null
}true = implemented, false = not implemented, null = unknown (scored as not implemented).
python3 -m dingdawg_compliance controlsfrom dingdawg_compliance import calculate_co_sb205_score, CO_SB_205_CONTROLS
# Score a self-assessment
responses = {
"CO-1": True, # scope: identified consequential decisions
"CO-3": False, # impact_assessment: no pre-deployment assessment yet
"CO-6": True, # transparency: consumer disclosure implemented
# ... rest of controls
}
result = calculate_co_sb205_score(responses)
print(result["score"]) # 0-100
print(result["gaps"]) # list of unimplemented controls
print(result["critical_gaps"]) # CO-3, CO-6, CO-10, CO-14 if missingfrom dingdawg_compliance import ComplianceStore, ComplianceScorer, ComplianceFramework
store = ComplianceStore() # stored at ~/.dingdawg/compliance/compliance.db
# Register and assess a control
store.assess_control("CO-3", status="COMPLIANT", assessor="legal-team", notes="Completed Q1 2026")
# Score
scorer = ComplianceScorer(store)
print(scorer.overall_posture_score()) # e.g. 72.0
print(scorer.per_framework_score()) # per-framework breakdown
print(scorer.gap_analysis()) # prioritized gap listfrom dingdawg_compliance import AutoAssessor
from pathlib import Path
assessor = AutoAssessor(
base_dir=Path("./src"),
db_paths=[Path("./data/app.db")]
)
results = assessor.run_all_checks()
print(results["checks"]["access_controls"]["summary"])
print(results["checks"]["audit_logging"]["summary"])| ID | Category | Control | Critical |
|---|---|---|---|
| CO-1 | scope | Consequential Decision Identification | |
| CO-2 | scope | High-Risk AI System Classification | |
| CO-3 | impact_assessment | Pre-Deployment Impact Assessment | ★ |
| CO-4 | impact_assessment | Annual Impact Assessment Review | |
| CO-5 | impact_assessment | Impact Assessment Documentation | |
| CO-6 | transparency | Consumer Disclosure at Point of Decision | ★ |
| CO-7 | transparency | Disclosure Timing | |
| CO-8 | transparency | Disclosure Content — AI Role | |
| CO-9 | transparency | Disclosure Content — Data Used | |
| CO-10 | appeal | Appeal Mechanism | ★ |
| CO-11 | appeal | Human Review Option | |
| CO-12 | appeal | Opt-Out Mechanism | |
| CO-13 | appeal | Appeal Response Timeline | |
| CO-14 | governance | Responsible AI Officer Designation | ★ |
| CO-15 | governance | AI Inventory | |
| CO-16 | governance | Vendor Due Diligence | |
| CO-17 | governance | Policy Documentation | |
| CO-18 | bias_testing | Pre-Deployment Bias Testing | |
| CO-19 | bias_testing | Ongoing Bias Monitoring | |
| CO-20 | bias_testing | Protected Class Analysis | |
| CO-21 | data_governance | Training Data Documentation | |
| CO-22 | data_governance | Data Minimization | |
| CO-23 | incident_response | AI Incident Response Plan | |
| CO-24 | incident_response | Error Notification | |
| CO-25 | audit | Third-Party Audit Trail |
★ Critical — mandatory remediation required before June 30, 2026.
This scanner shows what to check and where your gaps are. It does not generate remediation plans, regulatory citations, evidence templates, or audit-ready documentation.
For the full gap report with remediation guidance → dingdawg.com/compliance
Apache 2.0 — free to use, fork, and contribute.
PRs welcome for new indicators, additional frameworks, or CLI improvements. Open an issue first for anything structural.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.