Concurrency Fuzzing Testing
dzhalaevd/Donatello
Use as the lead skill when Python tests must expose scheduler/interleaving bugs in asyncio, threading, queues, workers, databases, caches, or mixed-concurrency code
Generate randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing.
$ npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ArabelaTso/Skills-4-SE fuzzing-input-generator --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fuzzing-input-generator .claude/skills/fuzzing-input-generator && rm -rf skills-srcUse ~/.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/
Install the "fuzzing-input-generator" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/fuzzing-input-generator into .claude/skills/fuzzing-input-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzing-input-generator", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/fuzzing-input-generatorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ArabelaTso/Skills-4-SE fuzzing-input-generator --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/fuzzing-input-generator .agents/skills/fuzzing-input-generator && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "fuzzing-input-generator" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/fuzzing-input-generator into .agents/skills/fuzzing-input-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzing-input-generator", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ArabelaTso/Skills-4-SE fuzzing-input-generator --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/fuzzing-input-generator .cursor/skills/fuzzing-input-generator && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "fuzzing-input-generator" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/fuzzing-input-generator into .cursor/skills/fuzzing-input-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzing-input-generator", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ArabelaTso/Skills-4-SE.git --path skills/fuzzing-input-generator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ArabelaTso/Skills-4-SE fuzzing-input-generator --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/fuzzing-input-generator .gemini/skills/fuzzing-input-generator && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "fuzzing-input-generator" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/fuzzing-input-generator into .gemini/skills/fuzzing-input-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzing-input-generator", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ArabelaTso/Skills-4-SE fuzzing-input-generatorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/fuzzing-input-generator .github/skills/fuzzing-input-generator && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "fuzzing-input-generator" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/fuzzing-input-generator into .github/skills/fuzzing-input-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzing-input-generator", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ArabelaTso/Skills-4-SE fuzzing-input-generator --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/fuzzing-input-generator .opencode/skills/fuzzing-input-generator && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "fuzzing-input-generator" agent skill from https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/fuzzing-input-generator into .opencode/skills/fuzzing-input-generator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fuzzing-input-generator", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
fuzzing-input-generatorGenerate randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing.
Fuzzing Input Generator is an agent skill from ArabelaTso/Skills-4-SE. Generate randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing. Use when creating test cases for robustness testing, generating adversarial inputs, testing error handling, finding edge cases, or security testing. Produces Python test code with fuzzing inputs for strings, numbers, and structured data focusing on edge cases, invalid inputs, and random valid inputs. Triggers when users ask to generate fuzz tests, create randomized test inputs, test…
Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/fuzzing-patterns.md`).
It sits in Security, covering Fuzzing and Schema markup. It works with Python. The repository describes itself as: A curated list of 180+ useful Claude Skills for Software Engineering and resources for customizing AI for SE workflows. The licence is Apache-2.0.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 4f38503. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
pytestFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Fuzzing Input Generator loads about 4.9k tokens when it runs, and up to ~6.8k if it reads all its reference files. Until then it costs about 153 tokens; SKILL.md has 438 words of instructions outside code blocks.
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.
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.
The full file from ArabelaTso/Skills-4-SE at commit 4f38503, republished under its Apache-2.0 licence (© ArabelaTso). 438 words, ~4,882 tokens.
.claude/skills/fuzzing-input-generator/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Generate comprehensive fuzz testing inputs to uncover bugs, crashes, and security vulnerabilities by systematically testing functions with edge cases, invalid inputs, and randomized data.
Understand what needs to be fuzzed:
Identify input types:
Understand expected behavior:
Extract function signature:
def process_user_input(name: str, age: int, email: str) -> dict:
"""Process user registration data."""
# Analyze: expects string, int, string
# Constraints: name non-empty, age > 0, email formatChoose appropriate fuzzing approaches:
Test boundary conditions and special values:
Test with malformed or incorrect data:
Generate random but technically valid inputs:
Test for vulnerabilities:
Create Python test functions with fuzzing inputs.
import pytest
import random
import string
def fuzz_<function_name>():
"""Fuzz test for <function_name>."""
# Edge cases
edge_cases = [
# Add specific edge case inputs
]
# Invalid inputs
invalid_inputs = [
# Add invalid inputs
]
# Random valid inputs
def generate_random_valid():
# Generate random but valid input
pass
# Test edge cases
for input_data in edge_cases:
try:
result = function_under_test(input_data)
# Check result or at least that it doesn't crash
except Exception as e:
# Document or assert expected exceptions
pass
# Test invalid inputs
for input_data in invalid_inputs:
# Similar testing pattern
pass
# Test random inputs
for _ in range(100):
random_input = generate_random_valid()
# Test with random inputCreate comprehensive input sets for each parameter type. See fuzzing-patterns.md for extensive patterns.
def generate_string_fuzz_inputs():
"""Generate fuzz inputs for string parameters."""
return [
# Empty and whitespace
"",
" ",
" ",
"\t",
"\n",
"\r\n",
# Length edge cases
"a", # Single char
"a" * 100, # Medium
"a" * 10000, # Long
"a" * 1000000, # Very long
# Special characters
"!@#$%^&*()",
"'",
"\"",
"\\",
"<script>alert(1)</script>",
# Unicode
"🔥",
"你好",
"مرحبا",
# Injection patterns
"'; DROP TABLE users--",
"../../../etc/passwd",
"${var}",
# Format strings
"%s%s%s",
"{0}{1}{2}",
# Null bytes
"\x00",
"test\x00test",
]def generate_number_fuzz_inputs():
"""Generate fuzz inputs for numeric parameters."""
return [
# Integers
0,
1,
-1,
2**31 - 1, # Max 32-bit int
-2**31, # Min 32-bit int
2**63 - 1, # Max 64-bit int
-2**63, # Min 64-bit int
# Floats
0.0,
-0.0,
float('inf'),
float('-inf'),
float('nan'),
1e308, # Near max float
1e-308, # Near min float
0.1 + 0.2, # Precision issue
# Edge cases
None,
"123", # String number
"not a number",
[],
{},
]def generate_json_fuzz_inputs():
"""Generate fuzz inputs for JSON/dict parameters."""
return [
# Empty
{},
[],
None,
# Type confusion
{"number": "123"},
{"bool": "true"},
{"array": "[]"},
# Deep nesting
{"a": {"b": {"c": {"d": {"e": "deep"}}}}},
[[[[["nested"]]]]],
# Large structures
{f"key{i}": i for i in range(1000)},
[i for i in range(10000)],
# Special keys
{"": "empty key"},
{"key with spaces": "value"},
{"key.with.dots": "value"},
# Mixed types
{"str": "text", "num": 123, "bool": True, "null": None, "arr": [1, 2]},
# Invalid JSON strings
"{invalid}",
'{"unclosed": ',
'{"key": undefined}',
]Generate executable test code:
import pytest
import random
import string
def test_fuzz_process_username():
"""Fuzz test for username processing."""
def process_username(username: str) -> str:
"""Function under test."""
if not username:
raise ValueError("Username cannot be empty")
if len(username) > 50:
raise ValueError("Username too long")
return username.strip().lower()
# Edge case inputs
edge_cases = [
"", # Empty
" ", # Space only
"a", # Single char
"A" * 50, # Max length
"A" * 51, # Over max
" user ", # Surrounding spaces
"User123", # Mixed case
"user@name", # Special chars
"user\nname", # Newline
"🔥user", # Unicode
"\x00user", # Null byte
]
# Invalid inputs
invalid_inputs = [
None,
123,
[],
{},
True,
]
# Test edge cases
for username in edge_cases:
try:
result = process_username(username)
assert isinstance(result, str)
assert len(result) <= 50
except ValueError as e:
# Expected for empty or too long
assert "empty" in str(e) or "too long" in str(e)
except Exception as e:
pytest.fail(f"Unexpected exception for '{username}': {e}")
# Test invalid types
for username in invalid_inputs:
try:
result = process_username(username)
pytest.fail(f"Should reject invalid type: {type(username)}")
except (TypeError, AttributeError):
pass # Expected
# Random fuzzing
for _ in range(100):
length = random.randint(0, 100)
chars = string.ascii_letters + string.digits + " !@#$"
random_username = ''.join(random.choice(chars) for _ in range(length))
try:
result = process_username(random_username)
# Verify properties that should always hold
if random_username.strip():
assert result.islower()
assert len(result) <= 50
except ValueError:
# Expected for empty or too long
passimport pytest
import math
def test_fuzz_validate_age():
"""Fuzz test for age validation."""
def validate_age(age: int) -> bool:
"""Function under test."""
return 0 <= age <= 150
# Edge case inputs
edge_cases = [
0, # Min valid
1,
150, # Max valid
-1, # Just below min
151, # Just above max
18, # Common value
65, # Another common
2**31 - 1, # Max int
-2**31, # Min int
]
# Invalid/special inputs
special_inputs = [
None,
"25", # String
25.5, # Float
float('inf'),
float('-inf'),
float('nan'),
[],
{},
True, # 1 in Python
False, # 0 in Python
]
# Test edge cases
for age in edge_cases:
try:
result = validate_age(age)
assert isinstance(result, bool)
if 0 <= age <= 150:
assert result is True
else:
assert result is False
except Exception as e:
pytest.fail(f"Unexpected exception for {age}: {e}")
# Test invalid types
for age in special_inputs:
try:
result = validate_age(age)
# Document behavior with non-int types
except (TypeError, ValueError):
pass # May be expected
# Random fuzzing
for _ in range(100):
random_age = random.randint(-1000, 1000)
try:
result = validate_age(random_age)
assert result == (0 <= random_age <= 150)
except Exception as e:
pytest.fail(f"Failed for random age {random_age}: {e}")import pytest
import json
import random
def test_fuzz_parse_user_data():
"""Fuzz test for JSON user data parsing."""
def parse_user_data(data: dict) -> dict:
"""Function under test."""
name = data["name"]
age = int(data["age"])
email = data.get("email", "")
if not name:
raise ValueError("Name required")
if age < 0:
raise ValueError("Age must be non-negative")
return {"name": name.strip(), "age": age, "email": email}
# Edge case inputs
edge_cases = [
{"name": "John", "age": 25}, # Valid
{"name": "John", "age": 25, "email": "j@e.com"}, # With optional
{"name": " John ", "age": 0}, # Whitespace
{"name": "A" * 1000, "age": 150}, # Long name
{"name": "🔥", "age": 1}, # Unicode
{}, # Empty
{"name": ""}, # Empty name
{"name": "John", "age": -1}, # Negative age
{"name": "John", "age": "25"}, # String age
{"name": None, "age": 25}, # None value
{"extra": "field", "name": "John", "age": 25}, # Extra fields
]
# Test edge cases
for data in edge_cases:
try:
result = parse_user_data(data)
assert isinstance(result, dict)
assert "name" in result
assert "age" in result
assert isinstance(result["age"], int)
assert result["age"] >= 0
except (KeyError, ValueError, TypeError) as e:
# Expected for invalid inputs
pass
except Exception as e:
pytest.fail(f"Unexpected exception for {data}: {e}")
# Random fuzzing
name_chars = string.ascii_letters + " "
for _ in range(100):
random_data = {
"name": ''.join(random.choice(name_chars) for _ in range(random.randint(0, 50))),
"age": random.randint(-10, 200),
"email": f"test{random.randint(0, 1000)}@example.com"
}
try:
result = parse_user_data(random_data)
# Verify invariants
if random_data["name"].strip() and random_data["age"] >= 0:
assert result["name"] == random_data["name"].strip()
assert result["age"] == random_data["age"]
except (KeyError, ValueError, TypeError):
# Expected for invalid random inputs
passCreate a complete test suite:
# test_fuzz_suite.py
import pytest
import random
import string
from your_module import function_to_test
class TestFuzzSuite:
"""Comprehensive fuzz testing suite."""
def test_edge_cases(self):
"""Test boundary conditions and special values."""
# Edge case tests
pass
def test_invalid_inputs(self):
"""Test with malformed and invalid inputs."""
# Invalid input tests
pass
def test_random_valid(self):
"""Test with randomized valid inputs."""
# Random fuzzing tests
pass
def test_security(self):
"""Test for injection and security issues."""
# Security tests
pass
@pytest.mark.slow
def test_extensive_fuzzing(self):
"""Extended fuzzing with many iterations."""
# Run 10000+ iterations
passRun the tests:
# Run all fuzz tests
pytest test_fuzz_suite.py -v
# Run with coverage
pytest test_fuzz_suite.py --cov=your_module
# Run only fast tests
pytest test_fuzz_suite.py -m "not slow"
# Run extensive fuzzing
pytest test_fuzz_suite.py -m slowWhen fuzzing finds failures:
Document the failure:
Create regression tests:
def test_regression_issue_123():
"""Regression test for crash with null byte."""
# This input was found by fuzzing
problematic_input = "test\x00data"
# Should handle gracefully
with pytest.raises(ValueError, match="null byte"):
process_input(problematic_input)Fix the code:
def process_input(data: str) -> str:
# Add validation found necessary by fuzzing
if "\x00" in data:
raise ValueError("null byte not allowed")
# ... rest of functionUser request:
"Generate fuzz tests for this function"
def divide(a: float, b: float) -> float:
return a / bResponse:
import pytest
import math
def test_fuzz_divide():
"""Fuzz test for divide function."""
# Edge cases
edge_cases = [
(10, 2), # Normal
(0, 1), # Zero numerator
(1, 0), # Division by zero
(-10, 2), # Negative
(10, -2), # Negative divisor
(float('inf'), 1), # Infinity
(1, float('inf')), # Divide by infinity
(float('nan'), 1), # NaN
(1, float('nan')), # Divide by NaN
(1e308, 1e-308), # Extreme values
]
for a, b in edge_cases:
try:
result = divide(a, b)
# Check result properties
if b == 0:
pytest.fail(f"Should raise ZeroDivisionError for b=0")
if not math.isnan(result):
assert math.isclose(result, a / b, rel_tol=1e-9)
except ZeroDivisionError:
assert b == 0 # Expected
except Exception as e:
pytest.fail(f"Unexpected exception for ({a}, {b}): {e}")
# Random fuzzing
for _ in range(1000):
a = random.uniform(-1e10, 1e10)
b = random.uniform(-1e10, 1e10)
try:
result = divide(a, b)
if abs(b) > 1e-10: # Avoid near-zero denominators
expected = a / b
if not math.isnan(expected):
assert math.isclose(result, expected, rel_tol=1e-6)
except ZeroDivisionError:
assert abs(b) < 1e-10 # Expected for small denominatorsUser request:
"Create fuzz tests to find path traversal vulnerabilities"
def test_fuzz_file_path_security():
"""Fuzz test for path traversal vulnerabilities."""
def safe_read_file(filename: str) -> str:
"""Function under test - should prevent path traversal."""
# Implementation would validate filename
pass
# Path traversal attacks
path_traversal_inputs = [
"../../../etc/passwd",
"..\\..\\..\\windows\\system32\\config\\sam",
"....//....//etc/passwd",
"%2e%2e%2f%2e%2e%2fetc%2fpasswd",
"..%252f..%252fetc%252fpasswd",
"file://etc/passwd",
"/etc/passwd",
"C:\\Windows\\System32",
"~/../../etc/passwd",
".",
"..",
"/",
"\\",
"",
"\x00",
"file\x00.txt",
"con", # Windows reserved
"nul",
"prn",
"a" * 1000, # Very long path
"a/" * 500 + "file.txt", # Very deep path
]
for path in path_traversal_inputs:
try:
result = safe_read_file(path)
# Should either reject or sanitize
assert not any(danger in path.lower() for danger in ["etc/passwd", "system32", ".."])
except (ValueError, PermissionError, FileNotFoundError):
# Expected rejection
pass
except Exception as e:
pytest.fail(f"Unexpected exception for path '{path}': {e}")Start with known edge cases:
Think like an attacker:
Use property-based testing:
decode(encode(x)) == xf(f(x)) == f(x)f(x, y) == f(y, x)Monitor coverage:
# Use coverage.py to find untested code paths
pytest --cov=module --cov-report=html test_fuzz.pyIterate based on findings:
Balance breadth and depth:
For comprehensive fuzzing patterns and edge cases, see fuzzing-patterns.md.
© ArabelaTso, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file (references) in skills/fuzzing-input-generator of ArabelaTso/Skills-4-SE.
Open the folder on GitHubat commit 4f38503
Fuzzing Input Generator 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Fuzzing Input Generator this skillArabelaTso/Skills-4-SE | 253 | — | ~4.9k | Automated safety check: Pass | Apache-2.0 | |
| Concurrency Fuzzing Testingdzhalaevd/Donatello | 135 | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Simplified Python Fuzzeropensage-agent/opensage-adk | 127 | — | ~228 | Automated safety check: Pass | Apache-2.0 | |
| Fuzzing Pythonbenchflow-ai/skillsbench | 1.8k | — | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Gaik ToolkitGAIK-project/gaik-toolkit | 100 | — | ~5.7k | Automated safety check: Pass | MIT | |
| Security Auditoreigent-ai/eigent | 15k | — | ~1.8k | Automated safety check: Notes | Apache-2.0 |
dzhalaevd/Donatello
Use as the lead skill when Python tests must expose scheduler/interleaving bugs in asyncio, threading, queues, workers, databases, caches, or mixed-concurrency code
opensage-agent/opensage-adk
Run a Python fuzzer script (provided as a string) for a fixed duration.
benchflow-ai/skillsbench
Creating fuzz driver for Python libraries using LibFuzzer. An agent skill from benchflow-ai/skillsbench.
GAIK-project/gaik-toolkit
GAIK toolkit overview and reference. An agent skill from GAIK-project/gaik-toolkit.
eigent-ai/eigent
Audits source code, dependencies and config files for vulnerabilities and hardcoded secrets, using two bundled Python scanners and an OWASP Top 10 checklist.
pashov/skills
Generate Echidna/Medusa-compatible Solidity fuzz suites from Foundry or Hardhat projects.
ArabelaTso/Skills-4-SE
Generate prioritized CVE watchlists and actionable security recommendations for repositories.
ArabelaTso/Skills-4-SE
Automatically migrate Python web applications between frameworks (Flask → FastAPI, Django → FastAPI).
ArabelaTso/Skills-4-SE
Generate test cases using metamorphic testing by applying transformations based on metamorphic properties.
ArabelaTso/Skills-4-SE
Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures.
ArabelaTso/Skills-4-SE
Automatically migrate Spring MVC applications to Spring Boot.
ArabelaTso/Skills-4-SE
Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks.
Works with
Categories
Generate randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing. Fuzzing Input Generator is an agent skill from ArabelaTso/Skills-4-SE. Generate randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing.
Fuzzing Input Generator fits situations like: creating test cases for robustness testing; generating adversarial inputs; testing error handling; finding edge cases.
Run `npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a claude-code`. Or copy the skill folder (skills/fuzzing-input-generator in ArabelaTso/Skills-4-SE) into .claude/skills/fuzzing-input-generator in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a codex`. Or copy the skill folder (skills/fuzzing-input-generator in ArabelaTso/Skills-4-SE) into .agents/skills/fuzzing-input-generator in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArabelaTso/Skills-4-SE --skill fuzzing-input-generator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fuzzing-input-generator, .gemini/skills/fuzzing-input-generator, .github/skills/fuzzing-input-generator and .opencode/skills/fuzzing-input-generator in your project.
Going by SKILL.md and its folder, Fuzzing Input Generator needs the command-line tools its instructions call (pytest). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
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.
Fuzzing Input Generator is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.9k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Fuzzing Input Generator: Concurrency Fuzzing Testing (dzhalaevd/Donatello, 135 stars), Simplified Python Fuzzer (opensage-agent/opensage-adk, 127 stars), Fuzzing Python (benchflow-ai/skillsbench, 1.8k stars) and Gaik Toolkit (GAIK-project/gaik-toolkit, 100 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ArabelaTso (a GitHub user) maintains it in ArabelaTso/Skills-4-SE, which has 253 GitHub stars. The repository holds 151 skills in this directory. The repository was last updated on August 21, 2026.
Source: ArabelaTso/Skills-4-SE on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.