llm-artifacts-detection
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/existential-birds/beagle --skill llm-artifacts-detection- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads llm-artifacts-detection automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
LLM Artifacts Detection
Detect and flag common patterns introduced by LLM coding agents that reduce code quality.
Detection Categories
| Category | Reference | Key Issues |
|---|---|---|
| Tests | references/tests-criteria.md | DRY violations, library testing, mock boundaries |
| Dead Code | references/dead-code-criteria.md | Unused code, TODO/FIXME, backwards compat cruft |
| Abstraction | references/abstraction-criteria.md | Over-abstraction, copy-paste drift, over-configuration |
| Style | references/style-criteria.md | Obvious comments, defensive overkill, unnecessary types |
Agent Prompts
Use these prompts to spawn focused detection agents:
Tests Agent
Analyze the test files for LLM-introduced test quality issues:
1. **DRY Violations**: Look for setup/teardown code repeated across multiple test functions instead of using fixtures or shared helpers. Flag patterns like:
- Identical object creation in multiple tests
- Repeated mock configurations
- Copy-pasted database setup
2. **Library Testing**: Identify tests that validate standard library or framework behavior rather than application code. Signs:
- No imports from the application codebase
- Testing built-in functions or third-party library methods
- Assertions about stdlib behavior
3. **Mock Boundaries**: Flag mocking that's too deep or too shallow:
- Too deep: Mocking internal implementation details, private methods
- Too shallow: Mocking at the wrong layer, missing integration points
- Wrong level: Unit test mocks in integration tests or vice versa
For each issue found, report: [FILE:LINE] ISSUE_TITLE
Dead Code Agent
Scan the codebase for dead code and cleanup opportunities:
1. **Unused Code**: Find functions, classes, and variables with no references:
- Functions never called
- Classes never instantiated
- Module-level variables never read
- Unreachable code after returns
2. **TODO/FIXME Comments**: Flag all TODO, FIXME, HACK, XXX comments that indicate incomplete work
3. **Backwards Compat Cruft**: Look for patterns suggesting removed features:
- Variables renamed with _unused, _old, _deprecated suffixes
- Re-exports only for backwards compatibility
- Comments like "# removed", "# legacy", "# deprecated"
- Empty functions/classes kept "for compatibility"
4. **Orphaned Tests**: Tests for code that no longer exists:
- Test files with no corresponding source
- Test functions testing deleted features
For each issue found, report: [FILE:LINE] ISSUE_TITLE
Abstraction Agent
Review the codebase for over-engineering introduced by LLM agents:
1. **Over-Abstraction**: Identify unnecessary abstraction layers:
- Wrapper classes that just delegate to one method
- Interfaces/protocols with only one implementation
- Abstract base classes with single concrete class
- Factory functions that always return the same type
2. **Copy-Paste Drift**: Find 3+ similar code blocks that should be parameterized:
- Nearly identical functions with minor variations
- Repeated patterns that could be a single function with parameters
- Similar class methods across multiple classes
3. **Over-Configuration**: Flag configuration for non-configurable things:
- Feature flags that are never toggled
- Environment variables always set to one value
- Config options with no production variation
- Overly generic code for single use case
For each issue found, report: [FILE:LINE] ISSUE_TITLE
Style Agent
Check for verbose LLM-style patterns that reduce code clarity:
1. **Obvious Comments**: Comments that restate what the code clearly does:
- "# increment counter" above counter += 1
- "# return the result" above return result
- Docstrings that repeat the function name
2. **Over-Documentation**: Excessive documentation on trivial code:
- Full docstrings on simple getters/setters
- Parameter descriptions for obvious args
- Return value docs for self-evident returns
3. **Defensive Overkill**: Unnecessary defensive programming:
- try/except around code that cannot fail
- Null checks on values that can't be null
- Type checks after type hints guarantee the type
- Validation of already-validated inputs
4. **Unnecessary Type Hints**: Type hints that add no value:
- Type hints on obvious literal assignments
- Redundant hints on variables immediately clear from context
- Over-annotated internal/local variables
For each issue found, report: [FILE:LINE] ISSUE_TITLE
Gates (reporting)
Run these in order so findings are evidence-bound, not inferred. This is the detection-side instance of the Anti-confabulation gate in the review-verification-protocol skill: every [FILE:LINE] must be echoed from a freshly read buffer in this turn, never inferred from the branch name, directory, or memory.
- Anchor — Set
FILEandLINEfrom an opened buffer,read_file, or equivalent; do not rely only on stale search snippets. Pass:LINEis in range forFILE, and the described issue is visible on that line or its immediate neighbors. - Title —
ISSUE_TITLEstates the defect in plain language (about one short sentence), not a proposed fix. Pass: someone openingFILEatLINEcan see why the title applies. - Dedup — Before final output, merge rows that share the same
FILE:LINEand root cause. Pass: at most one[FILE:LINE] ISSUE_TITLEper distinct defect at that anchor.
Usage
- Load this skill when reviewing AI-generated code
- If the agent supports subagents, dispatch one per detection category in parallel; otherwise work through the categories sequentially yourself, producing the same
[FILE:LINE] ISSUE_TITLEfindings. - Use reference files for detailed criteria and examples
- Apply Gates (reporting) above, then emit findings as
[FILE:LINE] ISSUE_TITLE
When to Apply
- Cleaning up code written by AI coding agents
- Post-generation code review
- Reducing code bloat from iterative AI generation
- Identifying patterns that reduce maintainability
FAQ
What does the llm-artifacts-detection skill do?
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.
How do I install llm-artifacts-detection?
Run npx skills add https://github.com/existential-birds/beagle --skill llm-artifacts-detection in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/llm-artifacts-detection/ (personal) or .claude/skills/llm-artifacts-detection/ (project).
Which AI agents can use llm-artifacts-detection?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is llm-artifacts-detection safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains llm-artifacts-detection?
It is published by existential-birds in existential-birds/beagle. It has 140 recorded installs.
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