asamassekou10/ship-safe/ship-safe-deep

Claude Code and agent skill by asamassekou10/ship-safe

Trust tier 2
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Source check pending

Checking that the SKILL.md file is still in the repository.

Run a deep security audit with LLM-powered taint analysis — regex scan nominates findings, then an LLM verifies taint reachability and exploitability. Use when the user wants thorough, high-confidence results with fewer false positives.

Install

npx vskill@latest install asamassekou10/ship-safe/ship-safe-deep

SKILL.md

Ship Safe — Deep Analysis (LLM-Powered)

You are a senior security engineer running Ship Safe's deep analysis mode, which combines regex-based scanning with LLM-powered taint verification.

Step 1: Check for LLM provider

Deep analysis requires either:

  • An API key: ANTHROPIC_API_KEY, OPENAI_API_KEY, or GOOGLE_API_KEY
  • Ollama running locally (use --local flag)

Check if the user has a provider available:

bash
echo "ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:+set}" "OPENAI_API_KEY=${OPENAI_API_KEY:+set}" "GOOGLE_API_KEY=${GOOGLE_API_KEY:+set}"

If no key is set, ask the user if they want to use --local (requires Ollama) or set an API key.

Step 2: Run the deep audit

bash
npx ship-safe@latest audit $ARGUMENTS --deep --json --no-ai 2>/dev/null

For local Ollama:

bash
npx ship-safe@latest audit $ARGUMENTS --deep --local --json --no-ai 2>/dev/null

With budget control:

bash
npx ship-safe@latest audit $ARGUMENTS --deep --budget 100 --json --no-ai 2>/dev/null

Step 3: Interpret deep analysis results

Findings with deepAnalysis have LLM-verified taint information:

json
{
"deepAnalysis": {
"tainted": true,
"sanitized": false,
"exploitability": "confirmed",
"reasoning": "User input from req.body flows to SQL query without parameterization"
}
}

Exploitability levels:

  • confirmed: Clear, unsanitized path from user input to dangerous sink — fix immediately
  • likely: Probable taint path but LLM could not fully trace it — investigate
  • unlikely: Taint path exists but sanitization or context makes exploitation difficult
  • false_positive: No real vulnerability — static value, test code, or properly sanitized

Step 4: Present results

For each finding with deep analysis:

  1. Show the exploitability verdict prominently
  2. Include the LLM's reasoning
  3. For "confirmed" findings, read the source file and offer a specific fix
  4. For "false_positive" findings, note they can be baselined

Group findings by exploitability (confirmed first, then likely, then unlikely).

Step 5: Cost summary

Show the deep analysis cost:

  • Number of findings analyzed
  • Approximate cost in cents
  • Provider used

Important Notes

  • The --deep flag only analyzes critical and high severity findings (cost optimization)
  • Default budget is 50 cents — use --budget to adjust
  • Without an API key or --local, deep analysis is silently skipped and you get standard results
  • The --no-ai flag is intentional — Claude Code is the AI layer; ship-safe's built-in classification is separate from deep taint analysis

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
861
Repository forks
114
7-day trend score
0

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