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Audit-grade transaction analysis skill: pull chain-specific artifacts (EVM/Solana), run a 6-phase root-cause forensic workflow, generate PoC/replay harnesses an
This skill automates end-to-end attack transaction analysis for EVM and Solana. It pulls traces and RPC artifacts, fetches contract sources, runs decompilation and fork-replay harnesses (Foundry/anvil), and produces audit-grade reports with root cause, PoC, and risk upper-bound measurements.
Use when a user provides a suspicious transaction hash and requests a forensic analysis or proof-of-concept reproduction. Recommended for auditors, incident responders, and researchers needing deterministic replay and evidence-backed conclusions.
pull_artifacts.py, Foundry test recipes, decompilation helpers).docs/ to ensure reproducible, gate-driven analysis.result.md with required sections.Designed for security-focused agent workflows that can orchestrate RPC calls, run Foundry/anvil locally, and manage large artifact sets; pairs well with agents that can run shell commands and interpret forensic outputs.
Blockchain attack transaction analysis skill for EVM and Solana. SKILL.md is extremely detailed with a rigorous 6-phase forensic methodology. All 5 scripts failed to run: two require CLI arguments (--tx), two crash on missing Python module imports (txanalyzer, pull_solana_artifacts sibling), and one can't find its dependency script. The curl|bash install patterns for Heimdall and Foundry in prerequisites are a security concern.
txanalyzer (Python module)pull_solana_artifacts (sibling module)The SKILL.md is one of the most thorough skill documents seen — structured forensic methodology with mandatory gates, evidence hierarchy, and stop conditions. However, the scripts cannot run without external dependencies (txanalyzer package) and proper project structure. The curl|bash patterns in install prerequisites and the OpenAI API data transmission in decompile.py are notable security concerns. The skill is niche (blockchain forensics) but well-designed for its domain.
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