61 lines
2.5 KiB
Markdown
61 lines
2.5 KiB
Markdown
---
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name: security:clean
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description: Scan and remediate security findings — auto-fixes deterministic issues, confirms semi-auto with user, reports manual findings
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allowed-tools: Read, Glob, Grep, Bash, Write, Edit, Agent, AskUserQuestion
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model: sonnet
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---
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# /security clean [path] [--dry-run]
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Scan, classify findings by remediability, auto-fix deterministic issues, propose semi-auto fixes, report manual. Goal: `/security scan` yields zero findings after clean.
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## Step 1: Setup
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- Parse `$ARGUMENTS`: extract path (default `.`), `--dry-run` flag. Resolve to absolute.
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- Plugin root = parent of this `commands/` folder.
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- Unless dry-run: create backup via `node <plugin-root>/scanners/lib/fs-utils.mjs backup "<target>"`. Record backup path.
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## Step 2: Pre-Clean Scan
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```bash
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node <plugin-root>/scanners/lib/fs-utils.mjs tmppath clean-findings.json
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node <plugin-root>/scanners/scan-orchestrator.mjs "<target>" --output-file "<findings_file>"
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```
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Show banner: Verdict, Risk Score, Finding counts. If 0 findings → stop.
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## Step 3: Auto-Fix
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```bash
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node <plugin-root>/scanners/auto-cleaner.mjs "<target>" --findings "<findings_file>" [--dry-run]
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```
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Report: Applied/Skipped/Failed counts + list of fixes.
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## Step 4: Semi-Auto Proposals
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Collect `semi_auto` findings from auto-cleaner output. If any, spawn `subagent_type: "llm-security:cleaner-agent"`, `model: "sonnet"`:
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> Here are semi-auto findings: \<JSON\>. Target: \<target\>.
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> Read: \<plugin-root\>/knowledge/secrets-patterns.md
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> Return remediation proposals as JSON.
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Present each proposal group via AskUserQuestion: "Apply all" / "Review individually" / "Skip". Apply approved fixes with Edit tool. Skip if dry-run.
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## Step 5: LLM Threat Scan
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Spawn `subagent_type: "llm-security:skill-scanner-agent"`, `model: "sonnet"`:
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> Scan target: \<target\>. Read: \<plugin-root\>/knowledge/skill-threat-patterns.md, \<plugin-root\>/knowledge/secrets-patterns.md
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> Return findings with severity, category, file, line, remediation.
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Auto-fix deterministic LLM findings (injection comments, spoofed headers, exfil URLs). Present semi-auto via AskUserQuestion. Report manual findings.
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## Step 6: Validate + Re-Scan
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Validate modified files (JSON parse, frontmatter, `node --check`). Restore from backup on failure. Re-run orchestrator to measure improvement.
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## Step 7: Report
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Output: Pre/post comparison, all fix summaries, remaining manual findings, rollback instructions.
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- Dry-run: show "DRY-RUN" mode, list proposed changes without applying.
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