Claude Code Skills & Agents is a collection of reusable skills, sub-agents, and configuration templates that extend Claude Code with workflows such as research, image generation, translation, and GitHub automation. It is for Claude Code users who want to install these capabilities individually or as a marketplace plugin. The catalogue entries are components from this collection.
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/feiskyer/claude-code-settingsWrote 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/feiskyer/claude-code-settings/comparator)<a href="https://agentmods.dev/agents/feiskyer/claude-code-settings/comparator"><img src="https://agentmods.dev/badge/agents/feiskyer/claude-code-settings/comparator/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/feiskyer/claude-code-settings/comparator"><img src="https://agentmods.dev/badge/agents/feiskyer/claude-code-settings/comparator.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 | $0.01671 |
| Opus 5.5 | $0.00012 | $0.00668 |
| Sonnet 5.5 | $0.00006 | $0.00334 |
| Haiku 4.5 | $0.00003 | $0.00167 |
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 19d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- skill-creator_comparator — 95% identical, 29 lines differ
- comparator — 92% identical, 26 lines differ
- comparator — 92% identical, 26 lines differ
- comparator — 92% identical, 26 lines differ
- comparator — 92% identical, 26 lines differ
- comparator — 92% identical, 26 lines differ
- comparator — 92% identical, 26 lines differ
- comparator — 92% identical, 26 lines differ
How it starts
The opening of the file, as written. The whole thing — 197 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.
- 19d ago Changed · -11 lines 2507840c8be3
- 1mo ago First seen · 208 lines · 31 tokens per session scan A 0163e7c285cf
comparator is an agent published in the GitHub repository feiskyer/claude-code-settings (1,661 stars, last pushed 20d ago), licensed MIT. It adds 31 tokens to every session and 1,671 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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