Agent skill

Symbolic Execution Tools

by yaklang in yaklang/hack-skills

Symbolic execution and constraint solving playbook. An agent skill from yaklang/hack-skills.

MITAuto-check passedSecurity

Install Symbolic Execution Tools

skills CLI
$ npx skills add yaklang/hack-skills --skill symbolic-execution-tools -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install yaklang/hack-skills symbolic-execution-tools --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/yaklang/hack-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/symbolic-execution-tools .claude/skills/symbolic-execution-tools && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
symbolic-execution-tools
GitHub stars
2.4k
Token cost
~3k tokens
SKILL.md length
470 words
Files
2
Skills in repo
27
Repo updated
First seen
Licence
MIT

At a glance

Symbolic execution and constraint solving playbook. An agent skill from yaklang/hack-skills.

  • Works in 9 steps: RELATED ROUTING → ANGR — CORE CONCEPTS → Z3 CONSTRAINT SOLVING → …
  • Solving CTF reversing challenges
  • SKILL.md covers 0. RELATED ROUTING, 1. ANGR — CORE CONCEPTS, 2. Z3 CONSTRAINT SOLVING and 3. UNICORN ENGINE — CODE…, plus 5 more sections
  • Calls pip

What it does

Symbolic Execution Tools is an agent skill from yaklang/hack-skills. Symbolic execution and constraint solving playbook. Use when solving CTF reversing challenges, recovering keys, bypassing checks, or automating binary analysis with angr, Z3, or Unicorn Engine.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `ANGR_COOKBOOK.md`).

It sits in Security, covering Capture the flag and Reverse engineering and malware. The repository describes itself as: Helping AI Agent become an awesome practical hacker! The licence is MIT.

When your agent uses it

  • Solving CTF reversing challenges
  • Recovering keys
  • Bypassing checks
  • Automating binary analysis with angr

Example prompts

  • “/symbolic-execution-tools”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. RELATED ROUTING
  2. ANGR — CORE CONCEPTS
  3. Z3 CONSTRAINT SOLVING
  4. UNICORN ENGINE — CODE EMULATION
  5. ANGR EXPLORATION STRATEGIES
  6. PRACTICAL WORKFLOW
  7. DECISION TREE
  8. COMMON PITFALLS & FIXES
  9. TOOL VERSIONS & INSTALLATION

What it can do on your machine

Read from SKILL.md and the folder at commit 6fbf0bc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Symbolic Execution Tools loads about 3k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 470 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from yaklang/hack-skills at commit 6fbf0bc, republished under its MIT licence (© yaklang). 470 words, ~3,031 tokens.

Download SKILL.mdSave it as .claude/skills/symbolic-execution-tools/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
symbolic-execution-tools
description
Symbolic execution and constraint solving playbook. Use when solving CTF reversing challenges, recovering keys, bypassing checks, or automating binary analysis with angr, Z3, or Unicorn Engine.

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.

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
ScenarioBest ToolWhy
Pure math / equation systemZ3Direct constraint solving, no binary needed
Binary with control flowangrExplores paths, manages constraints automatically
Emulate specific code regionUnicornFast, no symbolic overhead, good for unpacking
Complex binary + custom VMangr + Unicorn (combo)angr for control flow, Unicorn for VM handlers
Kernel / firmware codeQilingFull 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
python
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}")
1.3 Symbolic Variables (claripy)
python
# Bitvector (fixed-size integer)
sym_input = claripy.BVS("input", 64)        # 64-bit symbolic
sym_byte = claripy.BVS("byte", 8)           # 8-bit symbolic
sym_buf = claripy.BVS("buffer", 8 * 32)     # 32-byte buffer

# Concrete bitvector
concrete = claripy.BVV(0x41, 8)             # concrete value 0x41

# Constraints
state.solver.add(sym_input > 0)
state.solver.add(sym_input < 100)
state.solver.add(sym_byte >= 0x20)           # printable ASCII
state.solver.add(sym_byte <= 0x7e)

# Evaluate
value = state.solver.eval(sym_input)
all_values = state.solver.eval_upto(sym_input, 10)  # up to 10 solutions
1.4 Symbolic stdin
python
flag_len = 32
sym_stdin = claripy.BVS("stdin", 8 * flag_len)

state = proj.factory.entry_state(stdin=sym_stdin)

# Constrain to printable ASCII
for i in range(flag_len):
    byte = sym_stdin.get_byte(i)
    state.solver.add(byte >= 0x20)
    state.solver.add(byte <= 0x7e)
1.5 Hooking Functions
python
# Hook by address (skip N bytes of original code)
@proj.hook(0x401100, length=5)
def skip_check(state):
    state.regs.eax = 1  # force success

# SimProcedure: replace library function
class MyStrcmp(angr.SimProcedure):
    def run(self, s1, s2):
        return claripy.If(
            self.state.memory.load(s1, 32) == self.state.memory.load(s2, 32),
            claripy.BVV(0, 32),
            claripy.BVV(1, 32)
        )

proj.hook_symbol('strcmp', MyStrcmp())

# Hook common problematic functions
proj.hook_symbol('printf', angr.SIM_PROCEDURES['libc']['printf']())
proj.hook_symbol('scanf', angr.SIM_PROCEDURES['libc']['scanf']())
proj.hook_symbol('puts', angr.SIM_PROCEDURES['libc']['puts']())
1.6 Memory Operations
python
# Read memory (symbolic-aware)
data = state.memory.load(addr, size)          # returns BV
data_concrete = state.solver.eval(data, cast_to=bytes)

# Write memory
state.memory.store(addr, claripy.BVV(0x41, 8))
state.memory.store(addr, sym_buf)

# Read/write registers
rax = state.regs.rax
state.regs.rdi = claripy.BVV(0x1000, 64)

2. Z3 CONSTRAINT SOLVING

2.1 Core API
python
from z3 import *

# Sorts
x = BitVec('x', 32)    # 32-bit bitvector
y = Int('y')             # arbitrary precision integer
b = Bool('b')            # boolean

# Solver
s = Solver()
s.add(x + y == 42)
s.add(x > 0)
s.add(y > 0)

if s.check() == sat:
    m = s.model()
    print(f"x = {m[x]}, y = {m[y]}")
2.2 Common CTF Patterns
python
# Serial key validation: each char satisfies constraints
key = [BitVec(f'k{i}', 8) for i in range(16)]
s = Solver()
for k in key:
    s.add(k >= 0x30, k <= 0x7a)  # alphanumeric-ish

# XOR key recovery
plaintext = b"known_plaintext"
ciphertext = b"\x12\x34..."
key_byte = BitVec('key', 8)
s = Solver()
for p, c in zip(plaintext, ciphertext):
    s.add(p ^ key_byte == c)

# System of linear equations (modular)
a, b, c = BitVecs('a b c', 32)
s = Solver()
s.add(3*a + 5*b + 7*c == 0x12345678)
s.add(2*a + 4*b + 6*c == 0xDEADBEEF)
s.add(a ^ b ^ c == 0xCAFEBABE)
2.3 Optimization
python
from z3 import Optimize

opt = Optimize()
x = BitVec('x', 32)
opt.add(x > 0)
opt.add(x < 1000)
opt.minimize(x)  # find smallest satisfying value
opt.check()
print(opt.model())

3. UNICORN ENGINE — CODE EMULATION

3.1 Basic Setup
python
from unicorn import *
from unicorn.x86_const import *
from capstone import Cs, CS_ARCH_X86, CS_MODE_64

mu = Uc(UC_ARCH_X86, UC_MODE_64)

CODE_ADDR = 0x400000
STACK_ADDR = 0x7fff0000
STACK_SIZE = 0x10000

mu.mem_map(CODE_ADDR, 0x10000)
mu.mem_map(STACK_ADDR, STACK_SIZE)

mu.mem_write(CODE_ADDR, code_bytes)
mu.reg_write(UC_X86_REG_RSP, STACK_ADDR + STACK_SIZE - 0x1000)
mu.reg_write(UC_X86_REG_RBP, STACK_ADDR + STACK_SIZE - 0x1000)

mu.emu_start(CODE_ADDR, CODE_ADDR + len(code_bytes))

result = mu.reg_read(UC_X86_REG_RAX)
3.2 Hooking Memory & Instructions
python
# Hook memory access
def hook_mem(uc, access, address, size, value, user_data):
    if access == UC_MEM_WRITE:
        print(f"Write {value:#x} to {address:#x}")
    elif access == UC_MEM_READ:
        print(f"Read from {address:#x}")

mu.hook_add(UC_HOOK_MEM_READ | UC_HOOK_MEM_WRITE, hook_mem)

# Hook specific instruction (for tracing)
def hook_code(uc, address, size, user_data):
    code = uc.mem_read(address, size)
    md = Cs(CS_ARCH_X86, CS_MODE_64)
    for insn in md.disasm(bytes(code), address):
        print(f"  {insn.address:#x}: {insn.mnemonic} {insn.op_str}")

mu.hook_add(UC_HOOK_CODE, hook_code)
3.3 Use Cases
Use CaseApproach
Unpack shellcodeMap shellcode, emulate, dump decoded payload
Decrypt stringsEmulate decryption function with controlled inputs
Brute-force short keysLoop emulation with different key inputs
Analyze obfuscated functionEmulate function, observe register/memory state
Firmware code emulationMap firmware memory layout, emulate routines

4. ANGR EXPLORATION STRATEGIES

Show full SKILL.md (189 more words)Show less
4.1 find/avoid
python
simgr.explore(
    find=lambda s: b"Correct" in s.posix.dumps(1),   # stdout contains "Correct"
    avoid=lambda s: b"Wrong" in s.posix.dumps(1)      # avoid "Wrong" output
)
4.2 Managing Path Explosion
StrategyImplementation
Constrain input spaceAdd constraints (printable, length limits)
Avoid dead-end pathsUse avoid= for known failure addresses
Hook complex functionsReplace with simplified SimProcedure
Limit loop iterationsstate.options.add(angr.options.LAZY_SOLVES)
Use veritestingsimgr.explore(..., technique=angr.exploration_techniques.Veritesting())
DFS instead of BFSsimgr.use_technique(angr.exploration_techniques.DFS())
Timeout per pathsimgr.explore(..., num_find=1) + timeout wrapper
4.3 Concrete + Symbolic Hybrid
python
state = proj.factory.entry_state(
    add_options={angr.options.UNICORN}  # use Unicorn for concrete regions
)

This dramatically speeds up execution: concrete code runs natively via Unicorn, switching to symbolic only when symbolic variables are involved.


5. PRACTICAL WORKFLOW

5.1 CTF Binary Solving Workflow
1. Static analysis: identify input method, success/fail conditions
   └─ Find "Correct" / "Wrong" strings → get their xref addresses

2. Choose tool:
   ├─ Pure math (no binary needed) → Z3
   ├─ Small binary, clear success/fail → angr explore
   └─ Specific function to emulate → Unicorn

3. Set up symbolic input:
   ├─ stdin → claripy.BVS + entry_state(stdin=)
   ├─ argv → full_init_state(args=[...])
   ├─ file input → SimFile
   └─ specific memory → state.memory.store(addr, sym)

4. Hook problematic functions:
   ├─ printf/puts → SimProcedure or no-op
   ├─ scanf → custom handler
   ├─ time/random → return concrete value
   └─ anti-debug → skip entirely

5. Explore and extract:
   └─ simgr.explore(find=, avoid=) → solver.eval()

6. DECISION TREE

Need to solve a reversing challenge?
│
├─ Is the challenge pure math / equations?
│  └─ Yes → Z3
│     ├─ Linear equations → BitVec + Solver
│     ├─ Modular arithmetic → BitVec (natural mod 2^n)
│     ├─ Boolean logic → Bool + Solver
│     └─ Optimization → Optimize + minimize/maximize
│
├─ Is it a compiled binary with clear success/fail?
│  └─ Yes → angr
│     ├─ Input via stdin → symbolic stdin
│     ├─ Input via argv → full_init_state with symbolic args
│     ├─ Input via file → SimFile
│     ├─ Path explosion → add constraints, avoid paths, hook loops
│     └─ Complex library calls → hook with SimProcedure
│
├─ Need to emulate a specific function/region?
│  └─ Yes → Unicorn Engine
│     ├─ Decryption routine → map code + data, emulate, read result
│     ├─ Shellcode analysis → map shellcode, hook syscalls
│     └─ Key schedule → emulate with different inputs
│
├─ Need to analyze firmware / exotic arch?
│  └─ Yes → Qiling (full system emulation with OS support)
│
├─ Binary has VM protection?
│  └─ angr for handler analysis + Z3 for bytecode constraints
│
└─ None of the above working?
   ├─ Combine: Unicorn for concrete regions + Z3 for constraints
   ├─ Manual reverse engineering with debugger
   └─ Side-channel approach (timing, power analysis for hardware)

7. COMMON PITFALLS & FIXES

ProblemCauseFix
angr hangs foreverPath explosion in loopsAdd avoid= for loop-back edges, or hook the loop
Z3 returns unknownNon-linear constraints too complexSimplify, split into sub-problems, use set_param("timeout", 5000)
Unicorn crashes on syscallSyscall not handledHook syscall interrupt, handle or skip
angr wrong resultIncorrect state initializationVerify initial memory layout matches actual binary
Symbolic memory too largeUnbounded symbolic readsConcretize array indices where possible
SimProcedure wrong typesArgument type mismatchCheck calling convention (cdecl vs fastcall)
angr can't load binaryMissing librariesUse auto_load_libs=False + hook needed symbols

8. TOOL VERSIONS & INSTALLATION

bash
# angr (Python 3.8+)
pip install angr

# Z3
pip install z3-solver

# Unicorn Engine
pip install unicorn

# Capstone (disassembly, pairs with Unicorn)
pip install capstone

# Keystone (assembly)
pip install keystone-engine

© yaklang, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/symbolic-execution-tools of yaklang/hack-skills.

  • SKILL.md
  • ANGR_COOKBOOK.md

Open the folder on GitHubat commit 6fbf0bc

Compare with similar skills

Symbolic Execution Tools next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Symbolic Execution Tools compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Symbolic Execution Tools this skillyaklang/hack-skills2.4k—~3kAutomated safety check: PassMIT
Reverse Flowlingbol088-spec/reverse-flow-skill940—~2.4kAutomated safety check: PassMIT
Penetration Flowlingbol088-spec/ReiPenFlow222—~1.8kAutomated safety check: PassMIT
Ctf Malwareljagiello/ctf-skills3.4k—~2.1kAutomated safety check: NotesMIT
Anti Reversing Techniqueswshobson/agents40k—~980Automated safety check: PassMIT
Analyzing Binariestrilwu/secskills157—~2.9kAutomated safety check: PassMIT

Similar skills

  • Reverse Flow

    lingbol088-spec/reverse-flow-skill

    Guided reverse engineering workflow for binaries, firmware, mobile apps, scripts, document samples, protocol captures, and unknown artifacts.

    940 GitHub stars~2.4k tokensUpdated 2 mo ago
    SecurityAuto-check passed
  • Penetration Flow

    lingbol088-spec/ReiPenFlow

    Guided workflow for authorized penetration testing, vulnerability validation, security reporting, CTF/local sandbox reverse engineering, and user-directed vulnerability research.

    222 GitHub stars~1.8k tokensUpdated 2 mo ago
    SecurityAuto-check passed
  • Ctf Malware

    ljagiello/ctf-skills

    Provides malware analysis and network traffic techniques for CTF challenges.

    3.4k GitHub stars~2.1k tokensUpdated 26 days ago
    SecurityAuto-check: notes
  • Understand anti-reversing, obfuscation, and protection techniques encountered during software analysis.

    40k GitHub stars~980 tokensUpdated 5 days ago
    SecurityAuto-check passed
  • Analyzing Binaries

    trilwu/secskills

    Reverse engineer compiled binaries, firmware, and mobile app packages using triage, static disassembly, decompilation, and dynamic instrumentation.

    157 GitHub stars~2.9k tokensUpdated 1 mo ago
    SecurityAuto-check passed
  • Looks up symbols and addresses in vphone600 release and research kernel datasets, and cross-references XNU source, with findings that separate fact from inference.

    15k GitHub stars~530 tokensUpdated today
    SecurityAuto-check passed

More from yaklang/hack-skills

All 27 skills in this repo
  • Anti Debugging Techniques

    yaklang/hack-skills

    Anti-debugging detection and bypass playbook. An agent skill from yaklang/hack-skills.

    2.4k GitHub stars~3.4k tokensUpdated 26 days ago
    Auto-check passed
  • API Auth And JWT Abuse

    yaklang/hack-skills

    API authentication and JWT abuse playbook. An agent skill from yaklang/hack-skills.

    2.4k GitHub stars~567 tokensUpdated 26 days ago
    Auto-check passed
  • API Authorization And Bola

    yaklang/hack-skills

    API authorization and BOLA testing playbook. An agent skill from yaklang/hack-skills.

    2.4k GitHub stars~449 tokensUpdated 26 days ago
    Auto-check passed
  • API Recon And Docs

    yaklang/hack-skills

    API reconnaissance and documentation review playbook. An agent skill from yaklang/hack-skills.

    2.4k GitHub stars~456 tokensUpdated 26 days ago
    Auto-check passed
  • Attack Surface Mapping

    yaklang/hack-skills

    Draw a testable attack surface from one authorized target URL or one application.

    2.4k GitHub stars~2.6k tokensUpdated 26 days ago
    Auto-check passed
  • Classical Cipher Analysis

    yaklang/hack-skills

    Classical cipher analysis playbook. An agent skill from yaklang/hack-skills.

    2.4k GitHub stars~4.8k tokensUpdated 26 days ago
    Auto-check passed

Categories

Questions about Symbolic Execution Tools

What does Symbolic Execution Tools do?

Symbolic execution and constraint solving playbook. An agent skill from yaklang/hack-skills. Symbolic Execution Tools is an agent skill from yaklang/hack-skills. Symbolic execution and constraint solving playbook.

When should I use Symbolic Execution Tools?

Symbolic Execution Tools fits situations like: solving CTF reversing challenges; recovering keys; bypassing checks; automating binary analysis with angr.

How do I install Symbolic Execution Tools in Claude Code?

Run `npx skills add yaklang/hack-skills --skill symbolic-execution-tools -a claude-code`. Or copy the skill folder (skills/symbolic-execution-tools in yaklang/hack-skills) into .claude/skills/symbolic-execution-tools in your project. Claude Code loads it when a task matches its description.

How do I install Symbolic Execution Tools in Codex?

Run `npx skills add yaklang/hack-skills --skill symbolic-execution-tools -a codex`. Or copy the skill folder (skills/symbolic-execution-tools in yaklang/hack-skills) into .agents/skills/symbolic-execution-tools in your project. Codex loads it when a task matches its description.

Can I use Symbolic Execution Tools in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add yaklang/hack-skills --skill symbolic-execution-tools -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/symbolic-execution-tools, .gemini/skills/symbolic-execution-tools, .github/skills/symbolic-execution-tools and .opencode/skills/symbolic-execution-tools in your project.

What does Symbolic Execution Tools need to run?

Going by SKILL.md and its folder, Symbolic Execution Tools needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Symbolic Execution Tools access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Symbolic Execution Tools safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Symbolic Execution Tools use?

Symbolic Execution Tools is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Symbolic Execution Tools use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Symbolic Execution Tools?

Skills that share tags, products or a category with Symbolic Execution Tools: Reverse Flow (lingbol088-spec/reverse-flow-skill, 940 stars), Penetration Flow (lingbol088-spec/ReiPenFlow, 222 stars), Ctf Malware (ljagiello/ctf-skills, 3.4k stars) and Anti Reversing Techniques (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Symbolic Execution Tools?

yaklang (a GitHub organization) maintains it in yaklang/hack-skills, which has 2,418 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on September 13, 2026.

Source: yaklang/hack-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.