HackSkills is an organized knowledge base of installable skills that gives AI agents practical security knowledge across areas such as web security, privilege escalation, reverse engineering, and digital forensics. It is intended for bug bounty work, penetration testing, CTF competitions, and authorized security research. The catalogue entries are the project's own master, category, and topic skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yaklang/hack-skills --skill symbolic-execution-toolsgit clone --depth 1 https://github.com/yaklang/hack-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yaklang/hack-skills/symbolic-execution-tools)<a href="https://agentmods.dev/skills/yaklang/hack-skills/symbolic-execution-tools"><img src="https://agentmods.dev/badge/skills/yaklang/hack-skills/symbolic-execution-tools.svg" alt="Measured on agentmods" height="20"></a>- Socket warn
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.03401 |
| Opus 5 | $0.00023 | $0.01700 |
| Sonnet 5 | $0.00009 | $0.00680 |
| Haiku 4.5 | $0.00005 | $0.00340 |
Grade A, and why
symbolic-execution-tools scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- symbolic-execution-tools — 100% identical, 0 lines differ
- symbolic-execution-tools — 100% identical, 0 lines differ
- symbolic-execution-tools — 97% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: Symbolic Execution Tools — Expert Analysis Playbook
AI LOAD INSTRUCTION: Expert symbolic execution techniques using angr, Z3, and Unicorn Engine. Covers CTF challenge automation, constraint solving patterns, function hooking, SimProcedure replacement, and emulation-based unpacking. Base models often produce broken angr scripts due to incorrect state initialization or missing hooks for libc functions.
0. RELATED ROUTING
- anti-debugging-techniques when anti-debug checks need to be symbolically bypassed
- code-obfuscation-deobfuscation when using symbolic execution for deobfuscation
- vm-and-bytecode-reverse when applying angr to custom VM challenges
Advanced Reference
Also load ANGR_COOKBOOK.md when you need:
- 15+ ready-to-use angr script patterns for common CTF challenges
- Hook templates for scanf, printf, malloc, strcmp
- Symbolic file input, stdin, argv patterns
- Optimization tricks for path explosion management
When to use which tool
| Scenario | Best Tool | Why |
|---|---|---|
| Pure math / equation system | Z3 | Direct constraint solving, no binary needed |
| Binary with control flow | angr | Explores paths, manages constraints automatically |
| Emulate specific code region | Unicorn | Fast, no symbolic overhead, good for unpacking |
| Complex binary + custom VM | angr + Unicorn (combo) | angr for control flow, Unicorn for VM handlers |
| Kernel / firmware code | Qiling | Full system emulation with OS awareness |
1. ANGR — CORE CONCEPTS
1.1 Pipeline
Project(binary)
→ Factory.entry_state() / blank_state(addr=)
→ SimulationManager(state)
→ explore(find=target, avoid=bad)
→ found[0].solver.eval(symbolic_var)
1.2 Essential Setup
import angr
import claripy
proj = angr.Project('./challenge', auto_load_libs=False)
# Entry state: start from program entry point
state = proj.factory.entry_state()
# Blank state: start from arbitrary address
state = proj.factory.blank_state(addr=0x401000)
# Full init state: with command-line args
state = proj.factory.full_init_state(args=['./challenge', arg1_sym])
simgr = proj.factory.simulation_manager(state)
simgr.explore(find=0x401234, avoid=[0x401300])
if simgr.found:
found = simgr.found[0]
solution = found.solver.eval(symbolic_input, cast_to=bytes)
print(f"Solution: {solution}")
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 420 lines · 45 tokens per session scan A cc2de553d2f1
symbolic-execution-tools is a skill published in the GitHub repository yaklang/hack-skills (2,120 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 3,401 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
byted-util-volcengine-detect-retry
An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.