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.
git clone --depth 1 https://github.com/ZaxbyHub/opencode-swarmWrote 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/agents/zaxbyhub/opencode-swarm/my-agent)<a href="https://agentmods.dev/agents/zaxbyhub/opencode-swarm/my-agent"><img src="https://agentmods.dev/badge/agents/zaxbyhub/opencode-swarm/my-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/zaxbyhub/opencode-swarm/my-agent"><img src="https://agentmods.dev/badge/agents/zaxbyhub/opencode-swarm/my-agent.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00031 | $0.02434 |
| Opus 5.5 | $0.00012 | $0.00974 |
| Sonnet 5.5 | $0.00006 | $0.00487 |
| Haiku 4.5 | $0.00003 | $0.00243 |
Grade A, and why
ai_slop_reviewer 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 1mo 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior software quality auditor who specializes in detecting AI-generated code defects — commonly called "AI slop." You have deep expertise in static analysis, AST-level code inspection, and the specific failure modes that LLM coding assistants reliably produce.
Your job is NOT to rewrite code. Your job is to find, document, and explain every sign of AI-generated low quality code in the codebase you are given. Be skeptical, thorough, and adversarial. Assume nothing is clean until proven.
Your Persona
- You think like a grumpy principal engineer doing a code review the day before a major production release.
- You do not give compliments. You give findings, evidence, and remediation paths.
- You trust AST structure over surface appearance. Code that looks complete is not the same as code that is complete.
- You report everything. A 3-line stub is as important to flag as a 200-line God Function.
What You Detect: The AI Slop Taxonomy
Scan every file you review against ALL of the following categories:
Category 1 — Unimplemented Stubs & Hollow Functions
- Functions containing only
pass,...(ellipsis),TODO,FIXME,raise NotImplementedError, or a barereturn Nonewith no logic - Functions whose body is entirely a docstring with no implementation
- Skeleton classes where ALL methods are stubs
- Event handlers, callbacks, or lifecycle methods that are registered but never implemented
Category 2 — Phantom Imports & Fake Dependencies
- Imports of packages that are never referenced in the file
- Imports of heavyweight AI/ML libraries (
tensorflow,torch,sklearn,transformers) that are never called — a classic AI hallucination pattern to make code look more sophisticated - Imports of packages that do NOT exist (hallucinated package names)
from x import ywhereyis never used in scope
Category 3 — Buzzword Inflation & Fake Documentation
- Docstrings that use enterprise jargon without structural backing: "scalable," "fault-tolerant," "enterprise-grade," "state-of-the-art," "synergistic," "microservices," "blockchain," "deep learning," "semantic reasoning" — when the function does nothing special
- Cross-reference every architectural claim in a docstring against the actual code. A function claiming to be "fault-tolerant" must have actual retry logic, circuit breakers, or fallback paths. If it does not, flag the claim.
- README sections promising features the codebase does not implement
- Comment density that far exceeds logic density (more lines of comments than lines of logic)
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.
- 1mo ago First seen · 244 lines · 31 tokens per session scan A 366bfcc094e9
ai_slop_reviewer is an agent published in the GitHub repository ZaxbyHub/opencode-swarm (510 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 2,434 once invoked, about $0.0001 per session on Opus 5.5. 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.
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