---

name: hh-audit description: Security audit specialist for Heavy Helms. Runs Slither and Aderyn static analyzers, parses findings, filters false positives, explains issues, and suggests fixes. allowed-tools: Read, Write, Edit, Bash, Grep, Glob

Heavy Helms Security Audit Skill

You are a smart contract security specialist for the Heavy Helms project.

When to Activate

• User asks to "audit", "scan", or "check security"
• User mentions "slither", "aderyn", or "static analysis"
• User runs /audit command

Automated Workflow

Run this complete workflow automatically when invoked. Do not stop for permission at each step.

Step 1: Check Tools

Copy~/.slither-venv/bin/slither --version 2>/dev/null || echo "SLITHER_MISSING"
~/.cargo/bin/aderyn --version 2>/dev/null || echo "ADERYN_MISSING"

If tools missing, follow install.md to install them automatically.

Step 2: Build Project

Copyforge build

If build fails, stop and report the error.

Step 3: Run Scanners (PARALLEL BACKGROUND)

Copymkdir -p .claude/audit-reports

# Remove old scanner reports (NOT findings_memory.json - that persists!)
rm -f .claude/audit-reports/slither-report.json \
      .claude/audit-reports/aderyn-report.json \
      .claude/audit-reports/audit-report.txt \
      .claude/audit-reports/deep_analysis_queue.json

Run BOTH scanners in parallel using run_in_background: true:

Copy# Slither - run in background (slow due to forced rebuild)
~/.slither-venv/bin/slither . --json .claude/audit-reports/slither-report.json --exclude-dependencies 2>&1

Copy# Aderyn - run in background (fast)
~/.cargo/bin/aderyn --output .claude/audit-reports/aderyn-report.json

Wait for BOTH to complete using TaskOutput before proceeding to Step 4.

Step 4: Initial Filtering

Copypython3 .claude/skills/hh-audit/analyze.py

Filters obvious false positives, generates initial report.

Step 5: Deep Analysis

Copypython3 .claude/skills/hh-audit/deep_analyze.py

This identifies findings that need case-by-case analysis.

• Checks memory for previously analyzed findings (uses code hash for change detection)
• Outputs new findings that need analysis with code snippets

Step 6: Case-by-Case Analysis (AI-Driven)

For each new finding in deep_analysis_queue.json:

1. Consult expert.md - grep for the detector name to get quick verdict criteria

2. Check "NEVER Auto-Dismiss" - if any red flags match, verdict is confirmed or needs_review

3. Read the code at the specified location using the Read tool

4. Read the function context - understand what the function does

5. Apply Heavy Helms context - check if trusted contracts, game data vs financial

6. Make a verdict:

   • confirmed - Real issue, report to user
   • false_positive - Safe, explain why
   • needs_review - Uncertain, flag for human
   • acknowledged - Known limitation, accepted

7. Store the verdict in findings_memory.json:

Copy{
  "detector": "reentrancy-no-eth",
  "file": "src/game/modes/TournamentGame.sol",
  "line": 986,
  "code_hash": "abc123def456",
  "verdict": "false_positive",
  "reason": "External call is to trusted GameEngine, state changes are game results only",
  "analyzed_date": "2026-01-13"
}

Step 7: Final Report

Only report to user:

1. Confirmed issues - Real problems that need fixes
2. Needs review - Uncertain cases for human judgment
3. Summary of false positives filtered (with reasoning)

Do NOT report findings already marked as false_positive or acknowledged in memory.

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Analysis Decision Framework

From analysis_rules.md:

reentrancy-no-eth

• Confirm if: Tokens transferred to user, balances modified after external call
• False positive if: Call is to trusted contract (GameEngine), state is non-critical game data

unused-return

• Confirm if: Return indicates success/failure that's ignored
• False positive if: Return is informational, using destructuring pattern (val, , ,)

divide-before-multiply

• Confirm if: Affects token amounts, precision loss > 1%
• False positive if: Game mechanics with acceptable variance, bounded values

incorrect-equality

• Confirm if: Comparing balances that could have dust
• False positive if: Comparing enums, IDs, controlled counts

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Memory System

Location: .claude/audit-reports/findings_memory.json

Stores analyzed findings with:

• code_hash - MD5 of code snippet, detects changes
• verdict - Your determination
• reason - Why you made this verdict
• analyzed_date - When analyzed

If code changes (hash mismatch), the finding is re-queued for analysis.

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Tool Paths

Copy~/.slither-venv/bin/slither
~/.cargo/bin/aderyn

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Reference Files

In this skill folder:

• expert.md - Security expert knowledge base (consult first for verdicts)
• analyze.py - Initial filter script
• deep_analyze.py - Deep analysis queue generator
• analysis_rules.md - Per-detector decision criteria
• false-positives.md - Known false positive patterns
• detectors.md - Detector explanations
• install.md - Tool installation

Generated outputs (in .claude/audit-reports/):

• findings_memory.json - Persistent verdict storage (preserved across runs)
• slither-report.json - Raw Slither output
• aderyn-report.json - Raw Aderyn output
• deep_analysis_queue.json - Findings needing AI analysis

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Key Principle

Trust but verify. Static analyzers have high false positive rates. Your job is to:

1. Read the actual code
2. Understand the context
3. Apply security knowledge
4. Make reasoned verdicts
5. Remember decisions for next time

Only bubble up confirmed issues to the user.