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Symbolic Execution Tools

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ptn1411
symbolic-execution-tools

Symbolic execution and constraint solving with angr, Z3, Unicorn, and Qiling. Activate when license logic, crackmes, or VM input puzzles need automated path exploration and input synthesis.

Overview

Publisherptn1411
Repositoryskill
Skill namesymbolic-execution-tools
Stars
275
Forks
133
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ptn1411 on GitHub. Read the source before you install it.

Installation

Install the Symbolic Execution Tools AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ptn1411/skill/tree/main/symbolic-execution-tools
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ptn1411/skill.git /tmp/skill
mkdir -p .claude/skills
cp -r /tmp/skill/symbolic-execution-tools .claude/skills/symbolic-execution-tools
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Symbolic Execution Tools in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Symbolic Execution Tools on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Symbolic Execution Tools is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Symbolic Execution Tools

Upstream reference: yaklang/hack-skills/symbolic-execution-tools. Final user-facing summary in Vietnamese.

0. Authorization & routing

Operates under MASTER_POLICY.md §1-§2.

Sibling skillWhen
anti-debugging-techniquesSymbolically skip debugger checks instead of patching
vm-and-bytecode-reverseSolve constraints inside a recovered VM ISA
binary-protection-bypassAuto-discover leak primitives or compute ROP chains
binary-patcherApply patches once correct branch / input is known
writerpro-pentestWhen license logic is amenable to constraint solving (e.g., HWID-derived check)

1. Tool selection

Use casePickReason
Pure equation / SAT-style problemZ3Direct constraint solver, no binary needed
Single binary, modest path countangrManages states + constraints; great find=/avoid= UX
High-speed CPU emulation, no symbolicsUnicornμs/instr, ideal for unpacking and tracing
Custom VM with native handlersangr (control) + Unicorn (handler emu)Combine path exploration with raw speed
OS / syscall / firmware awareQilingFull-system emu with OS personalities
Industrial-scale concolic on real targetsTriton, KLEEDynamic taint + concolic; mature solvers

2. angr — minimum viable script (crackme template)

python
# pip install angr claripy
import angr, claripy

proj = angr.Project('./crackme', auto_load_libs=False)

# Symbolic input — 32 printable bytes
flag_len = 32
flag = claripy.BVS('flag', 8 * flag_len)

state = proj.factory.entry_state(
    args=['./crackme'],
    stdin=flag,
)
# Constrain to printable ASCII
for i in range(flag_len):
    b = flag.get_byte(i)
    state.solver.add(b >= 0x20, b <= 0x7e)

simgr = proj.factory.simgr(state)
simgr.explore(
    find=lambda s: b'Correct' in s.posix.dumps(1),     # stdout sentinel
    avoid=lambda s: b'Wrong'  in s.posix.dumps(1),
)
if simgr.found:
    sol = simgr.found[0].solver.eval(flag, cast_to=bytes)
    print('flag =', sol)
else:
    print('no solution')

3. State factories — when to use which

FactoryStarts atGood for
entry_state()binary entryMost CTF crackmes
full_init_state()with libc initWhen constructors matter
blank_state(addr=…)arbitrary addressSkip unwanted setup; reach a check directly
call_state(addr, *args)function with concrete argsPer-function analysis, e.g. just the validator

4. Hooking & SimProcedures

python
# Replace strcmp with a fast SimProcedure
import angr
proj.hook_symbol('strcmp', angr.SIM_PROCEDURES['libc']['strcmp']())

# Custom hook — return value forced to 0
@proj.hook(0x401234, length=5)        # length must equal patched insn bytes
def force_ok(state):
    state.regs.eax = 0

Use this to:

  • skip anti-debug calls (ptrace, IsDebuggerPresent)
  • model scanf, printf, malloc cleanly
  • short-circuit slow / opaque loops

5. Path-explosion taming

SymptomLever
Memory blowup at branchy loopssimgr.use_technique(angr.exploration_techniques.LoopSeer(bound=10))
Stuck in libc functionsHook them with SimProcedures (§4)
Too many parallel statesLengthLimiter(max_length=2000) or DFS()
Unhelpful states lingerexplore(num_find=1), then drop the rest
Mass irrelevant branchesBuild a CFG, restrict simgr to addresses on the path to target
Each step very slowSwitch to Unicorn-engine backend: state.options.add(angr.options.UNICORN)

6. Z3 — direct constraint examples

python
# pip install z3-solver
from z3 import *

# Toy: find 8-byte input where xor(in, key) == out
key = bytes.fromhex('aabbccdd' * 2)
out = bytes.fromhex('11223344' * 2)

s = Solver()
inp = [BitVec(f'b{i}', 8) for i in range(8)]
for i in range(8):
    s.add(inp[i] ^ key[i] == out[i])
print(s.check(), [s.model()[b].as_long() for b in inp] if s.check() == sat else '-')

Use Z3 when the binary is already reduced to a system of equations (after VM lifting, or after a manual transcription of a small validator).

7. Unicorn for fast handler emulation

python
# pip install unicorn capstone
from unicorn import Uc, UC_ARCH_X86, UC_MODE_64
mu = Uc(UC_ARCH_X86, UC_MODE_64)

CODE_BASE = 0x400000
mu.mem_map(CODE_BASE, 0x10000)
mu.mem_write(CODE_BASE, open('handler.bin','rb').read())

mu.reg_write(UC_X86_REG_RDI, 0x41424344)    # arg1
mu.emu_start(CODE_BASE, CODE_BASE + 0x80)
print(hex(mu.reg_read(UC_X86_REG_RAX)))

Pair with vm-and-bytecode-reverse: each handler is a small native blob → emulate with Unicorn, drive PC manually.

8. Qiling — when you need syscall/OS awareness

python
# pip install qiling
from qiling import Qiling
ql = Qiling(['./linux_x86_64_target'], rootfs='./rootfs/x8664_linux')
ql.os.set_api('ptrace', lambda *a, **k: 0)   # hide debugger
ql.run()

Good for malware/firmware where OS calls matter and you don't want to keep porting symbolic stubs.

9. Methodology

  1. Identify the target check function (string ref to "Wrong"/"Correct"; license error message; etc.).
  2. Decide where to start: entry_state is safest; call_state(check_func, …) when you trust the rest.
  3. Symbolize only what needs to be: keep concretes for everything else.
  4. Stub expensive externals; cap loops; pin to relevant addresses.
  5. explore(find=, avoid=); verify the recovered input on the real binary.
  6. Hand off the discovered branch / value to binary-patcher for permanent patch, or to writerpro-pentest for keygen synthesis.

10. Anti-patterns

❌✅
Run simgr.explore() with no boundsAlways set find= and avoid=, plus loop/length limiters
Symbolize the whole address spaceOnly the bytes that drive the decision
Trust default libc model on every callHook scanf/printf/malloc with SimProcedures
Re-run from entry_state for each tweakCache proj/CFG; reuse blank_state(addr=…)

11. Final report (Vietnamese)

🧮 Symbolic execution hoàn tất:
  🛠️ Công cụ      : <angr | Z3 | Unicorn | Qiling | combo>
  🎯 Hàm mục tiêu : <addr / symbol>
  🔣 Input solved : <bytes hoặc số>
  ⏱️ Thời gian    : <s>
  📂 Script       : solve.py

▶  Tiếp:
   binary-patcher (vá vĩnh viễn) hoặc writerpro-pentest (sinh keygen từ ràng buộc).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Symbolic Execution Tools AI skill do?

Symbolic execution and constraint solving with angr, Z3, Unicorn, and Qiling. Activate when license logic, crackmes, or VM input puzzles need automated path exploration and input synthesis.

Why use Symbolic Execution Tools on TypingMind?

Because you install it once and use it with any model. Symbolic Execution Tools is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Symbolic Execution Tools in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ptn1411/skill/tree/main/symbolic-execution-tools. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Symbolic Execution Tools?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Symbolic Execution Tools?

As many as you like. As long as a model supports skills, you can use Symbolic Execution Tools with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Symbolic Execution Tools AI skill free?

It is published on GitHub by ptn1411. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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