comparator

An agent that compares two task outputs without knowing which skill produced each one. It judges them against the original task and any stated expectations.

In plain words
What is it for?
Use it to compare files or directories from two runs, assess quality and completeness, and choose the better output.
Why use it?
Blind comparison reduces bias toward a particular skill and makes it easier to identify which result better meets the requirements.

Agent for Codex

Install

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.

agentmods
npx agentmods add agents/av/harbor/comparator
Clone the repo
git clone --depth 1 https://github.com/av/harbor

Made for: Codex.

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,762 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured yesterday against content hash fe1fc9787c49, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

Origin

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.

.agents/skills/skill-creator/agents/comparator.md · 203 lines

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

  1. Examine output A (file or directory)
  2. Examine output B (file or directory)
  3. Note the type, structure, and content of each
  4. If outputs are directories, examine all relevant files inside

Step 2: Understand the Task

  1. Read the eval_prompt carefully
  2. 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

Read the full file on GitHub · 203 lines

Changes

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.

  1. yesterday First seen · 203 lines · 0 tokens per session scan A fe1fc9787c49

Subscribe to this mod's changes

comparator is an agent published in the GitHub repository av/harbor (3,198 stars, last pushed 2d ago), licensed Apache-2.0. 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.

Related

Other agents, from other repositories

autopilot

Autonomous hunt loop agent. Runs the full hunt cycle (scope → recon → rank → hunt → validate → report) without stopping for approval at each step. Configurable checkpoints (--paranoid, --normal, --yolo). Uses scopechecker.py for deterministic scope safety on every outbound request. Logs all requests to audit.jsonl.…

guib1/red-team-docker · 84 tokens

report-writer

Bug bounty report writer. Generates professional H1/Bugcrowd/Intigriti/Immunefi reports. Impact-first writing, human tone, no theoretical language, CVSS 3.1 calculation included. Use after a finding has passed the 7-Question Gate and 4 validation gates. Never generates reports with "could potentially" language.

guib1/red-team-docker · 75 tokens

validator

Finding validator. Runs the 7-Question Gate and 4-gate checklist on a described finding. Kills weak/theoretical findings fast before report writing. Prevents N/A submissions. Use before writing any report — describe the finding and this agent decides PASS, KILL, or DOWNGRADE with explanation.

guib1/red-team-docker · 64 tokens

web3-auditor

Smart contract security auditor. Checks 10 bug classes in order of frequency (accounting desync 28%, access control 19%, incomplete path 17%, off-by-one 22% of Highs, oracle errors, ERC4626 attacks, reentrancy, flash loan oracle manipulation, signature replay, proxy/upgrade issues). Applies pre-dive kill signals…

guib1/red-team-docker · 101 tokens

recon-agent

Subdomain enumeration and live host discovery specialist. Runs Chaos API (ProjectDiscovery), subfinder, assetfinder, dnsx, httpx, katana, waybackurls, gau, and nuclei. Produces prioritized attack surface for a target. Use when starting recon on a new target domain.

guib1/red-team-docker · 63 tokens

recon-ranker

Attack surface ranking agent. Takes recon output and hunt memory, produces a prioritized attack plan. Ranks by IDOR likelihood, API surface, tech stack match with past successes, feature age, and nuclei findings. Use after recon to decide what to test first.

guib1/red-team-docker · 57 tokens