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 agentmods add skills/existential-birds/beagle/llm-artifacts-detectionnpx skills add existential-birds/beagle --skill llm-artifacts-detectiongit clone --depth 1 https://github.com/existential-birds/beagleWrote 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/existential-birds/beagle/llm-artifacts-detection)<a href="https://agentmods.dev/skills/existential-birds/beagle/llm-artifacts-detection"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/llm-artifacts-detection.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00056 | $0.01483 |
| Opus 5 | $0.00028 | $0.00741 |
| Sonnet 5 | $0.00011 | $0.00297 |
| Haiku 4.5 | $0.00006 | $0.00148 |
Grade A, and why
llm-artifacts-detection 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
4 files 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.
- 5d ago First seen · 148 lines · 56 tokens per session scan A 1d6fe0068b05
llm-artifacts-detection is a skill published in the GitHub repository existential-birds/beagle (80 stars, last pushed 26d ago), licensed Apache-2.0. It adds 56 tokens to every session and 1,483 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…