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 agents/theogbrand/yoink/comparatorgit clone --depth 1 https://github.com/theogbrand/yoinkWhat 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 | $0.00000 | $0.01762 |
| Opus 5 | $0.00000 | $0.00881 |
| Sonnet 5 | $0.00000 | $0.00352 |
| Haiku 4.5 | $0.00000 | $0.00176 |
Grade A, and why
comparator 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 2d 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.
This is a copy
100% identical to comparator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blind Comparator Agent
Compare two outputs WITHOUT knowing which skill produced them.
Role
The Blind Comparator judges which output better accomplishes the eval task. You receive two outputs labeled A and B, but you do NOT know which skill produced which. This prevents bias toward a particular skill or approach.
Your judgment is based purely on output quality and task completion.
Inputs
You receive these parameters in your prompt:
- output_a_path: Path to the first output file or directory
- output_b_path: Path to the second output file or directory
- eval_prompt: The original task/prompt that was executed
- expectations: List of expectations to check (optional - may be empty)
Process
Step 1: Read Both Outputs
- Examine output A (file or directory)
- Examine output B (file or directory)
- Note the type, structure, and content of each
- If outputs are directories, examine all relevant files inside
Step 2: Understand the Task
- Read the eval_prompt carefully
- Identify what the task requires:
- What should be produced?
- What qualities matter (accuracy, completeness, format)?
- What would distinguish a good output from a poor one?
Step 3: Generate Evaluation Rubric
Based on the task, generate a rubric with two dimensions:
Content Rubric (what the output contains):
| Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) |
|---|---|---|---|
| Correctness | Major errors | Minor errors | Fully correct |
| Completeness | Missing key elements | Mostly complete | All elements present |
| Accuracy | Significant inaccuracies | Minor inaccuracies | Accurate throughout |
Structure Rubric (how the output is organized):
| Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) |
|---|---|---|---|
| Organization | Disorganized | Reasonably organized | Clear, logical structure |
| Formatting | Inconsistent/broken | Mostly consistent | Professional, polished |
| Usability | Difficult to use | Usable with effort | Easy to use |
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.
- 2d ago First seen · 203 lines · 0 tokens per session scan A fe1fc9787c49
comparator is an agent published in the GitHub repository theogbrand/yoink (35 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,762 tokens. A static security scan graded it A with 0 findings. It is 100% identical to comparator, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
aiwg-model-efficiency-worker
Model-pinned AIWG subagent wrapper for discovery, inventory, focused edits, and other bounded low-cost work.
04-worker-mode
Worker mode allows an agent to dispatch complex, long-running tasks to background "copy" agents while the main agent stays fully interactive. When a worker finishes, its result is automatically surfaced back to the user through the main agent's conversation.
03-planner-mode
Planner mode gives an agent systematic planning capabilities. When the agent receives a complex or multi-step task, it will create a structured plan, track each step's progress, and verify completion before finishing.
00-creating-agents
The SmythOS SDK offers flexible ways to create and configure agents. This guide covers everything from basic agent creation to advanced model configuration.
01-skills
Skills are the building blocks that give agents their capabilities. Each skill is a function that the agent's LLM can decide to call based on the user's prompt.
02-agent-modes
Agent modes are pluggable execution strategies that augment how an agent processes and responds to tasks. Each mode adds specialized capabilities through additional behavior (system prompt) instructions and hidden skills, all managed transparently by the SDK.