ghidra-rpc: A Ghidra agentic reverse engineering skill

by cellebrite-labs · Agent Tool · ★ 380

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 63/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars380
Forks34
LanguagePython
CategoryAgent Tool
Quality Score63/100
Last Updated2026-09-17
Created2026-06-04
Platformspython
Est. Tokens~14k

About ghidra-rpc

ghidra-rpc An agentic skill that gives an LLM access to Ghidra so it can perform reverse engineering tasks autonomously: decompile functions, trace call graphs, rename and annotate symbols, define data structures, diff binary versions, and more — all without human intervention. runs Ghidra as a persistent background daemon and exposes its capabilities through a CLI that returns structured JSON.

Compatible Skills

These tools work well together with ghidra-rpc for enhanced workflows:

  • open-reverselab — semantic(0.54)+complementary+same_lang+similar_pop+shared_platform (69%)
  • ghidra-re-skill — semantic(0.54)+complementary+same_lang+similar_pop+shared_platform (69%)
  • ghidra-headless-mcp — semantic(0.48)+complementary+same_lang+similar_pop+shared_platform (67%)
  • auto-re-agent — semantic(0.47)+complementary+same_lang+similar_pop+shared_platform (67%)
  • ablation — semantic(0.44)+complementary+same_lang+similar_pop+shared_platform (66%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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