analyzer

analyzer is an agent for coding agents from Human-Agent-Society/CORAL. It costs 0 tokens per session (2,271 once invoked), scanned A, a copy of analyzer, Apache-2.0.

A post-run reviewer for blind comparisons, where two agent outputs are judged without initially revealing which agent made each one.

In plain words
What is it for?
Use it after a comparison to inspect the winner, loser, skills, transcripts, and comparison scores, then save improvement suggestions.
Why use it?
It explains why the selected result was better and identifies practical ways to improve the weaker result.

Agent

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/human-agent-society/coral/analyzer
Clone the repo
git clone --depth 1 https://github.com/Human-Agent-Society/CORAL

Wrote 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.

agentmods badge for analyzer

README.md
[![agentmods](https://agentmods.dev/badge/agents/human-agent-society/coral/analyzer.svg)](https://agentmods.dev/agents/human-agent-society/coral/analyzer)
Your own site
<a href="https://agentmods.dev/agents/human-agent-society/coral/analyzer"><img src="https://agentmods.dev/badge/agents/human-agent-society/coral/analyzer.svg" alt="Measured on agentmods" height="20"></a>
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 2,271 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 $0.02271
Opus 5 $0.00000 $0.01136
Sonnet 5 $0.00000 $0.00454
Haiku 4.5 $0.00000 $0.00227

Measured 5d ago against content hash bf68f4cac5a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analyzer 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.

Origin

This is a copy

100% identical to analyzer — 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.

coral/template/skills/skill-creator/agents/analyzer.md · 275 lines

How it starts

The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Post-hoc Analyzer Agent

Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.

Role

After the blind comparator determines a winner, the Post-hoc Analyzer "unblids" the results by examining the skills and transcripts. The goal is to extract actionable insights: what made the winner better, and how can the loser be improved?

Inputs

You receive these parameters in your prompt:

  • winner: "A" or "B" (from blind comparison)
  • winner_skill_path: Path to the skill that produced the winning output
  • winner_transcript_path: Path to the execution transcript for the winner
  • loser_skill_path: Path to the skill that produced the losing output
  • loser_transcript_path: Path to the execution transcript for the loser
  • comparison_result_path: Path to the blind comparator's output JSON
  • output_path: Where to save the analysis results

Process

Step 1: Read Comparison Result

  1. Read the blind comparator's output at comparison_result_path
  2. Note the winning side (A or B), the reasoning, and any scores
  3. Understand what the comparator valued in the winning output

Step 2: Read Both Skills

  1. Read the winner skill's SKILL.md and key referenced files
  2. Read the loser skill's SKILL.md and key referenced files
  3. Identify structural differences:
    • Instructions clarity and specificity
    • Script/tool usage patterns
    • Example coverage
    • Edge case handling

Step 3: Read Both Transcripts

  1. Read the winner's transcript
  2. Read the loser's transcript
  3. Compare execution patterns:
    • How closely did each follow their skill's instructions?
    • What tools were used differently?
    • Where did the loser diverge from optimal behavior?
    • Did either encounter errors or make recovery attempts?

Step 4: Analyze Instruction Following

For each transcript, evaluate:

  • Did the agent follow the skill's explicit instructions?
  • Did the agent use the skill's provided tools/scripts?
  • Were there missed opportunities to leverage skill content?
  • Did the agent add unnecessary steps not in the skill?

Read the full file on GitHub · 275 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. 5d ago First seen · 275 lines · 0 tokens per session scan A bf68f4cac5a5

Subscribe to this mod's changes

analyzer is an agent published in the GitHub repository Human-Agent-Society/CORAL (951 stars, last pushed 4d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,271 tokens. A static security scan graded it A with 0 findings. It is 100% identical to analyzer, differing in 0 lines, and is treated as a copy.

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