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evidence
Collect and store point-in-time compliance evidence snapshots for audit trail.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Collect and store point-in-time compliance evidence snapshots for audit trail.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Deep code scan for AI security issues — prompt injection, PII in prompts, hardcoded keys, unguarded agents.
Run AI governance checks across cloud accounts and code repos — ISO 42001, EU AI Act, NIST AI RMF compliance.
Scan cloud accounts and GitHub repos to discover AI/ML services and build an AI system inventory.
Walk staged changes against the engineering principles checklist and report pass/fail per principle. Run before any non-trivial commit. Catches doc drift, stub functions, single-region defaults, missing framework mappings, and other regressions before they ship.
Generate a public-facing security trust page from scan data. Produces a single deployable index.html that shows compliance framework scores, security policies, infrastructure overview, and data protection posture. Deployable to S3, Vercel, Netlify, or GitHub Pages.
Paste a vendor's domain. Get a security risk assessment in 60 seconds.
| name | evidence |
| description | Collect and store point-in-time compliance evidence snapshots for audit trail. |
| user-invocable | true |
Collect timestamped compliance evidence snapshots for SOC 2 audit trail.
Read shasta.config.json for python_cmd. Use that for all commands (shown as <PYTHON_CMD>).
Run scan and collect evidence:
<PYTHON_CMD> -c "
import json
from shasta.config import get_aws_client
from shasta.scanner import run_full_scan
from shasta.evidence.collector import collect_all_evidence
from shasta.db.schema import ShastaDB
client = get_aws_client()
client.validate_credentials()
print('Running compliance scan...')
scan = run_full_scan(client)
db = ShastaDB(); db.initialize(); db.save_scan(scan)
print('Collecting evidence...')
files = collect_all_evidence(client, scan.id)
print(json.dumps({
'scan_id': scan.id,
'evidence_files': [str(f) for f in files],
'total_artifacts': len(files),
}, indent=2))
"
Explain what was collected (9 artifact types) and why monthly evidence collection builds the audit trail auditors need.