analyzer

An agent that examines a blind comparison after a winner has been selected. It reads both skills and their transcripts to explain why one performed better and how the other could improve.

In plain words
What is it for?
Use it after comparing two skills to analyze the result, identify useful differences, and save improvement suggestions to a specified output file.
Why use it?
A blind comparison hides which option produced which result, so its outcome alone may not explain the difference. This analysis connects the scores to instruction structure, tool use, examples, edge cases, and actual execution.

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/matthewye/opencode-toolbox/analyzer
Clone the repo
git clone --depth 1 https://github.com/MatthewYe/opencode-toolbox
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 1,153 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.01153
Opus 5 $0.00000 $0.00576
Sonnet 5 $0.00000 $0.00231
Haiku 4.5 $0.00000 $0.00115

Measured yesterday against content hash d053fa54b2ec, 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 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.

skills/skill-creator/agents/analyzer.md · 132 lines

How it starts

The opening of the file, as written. The whole thing — 132 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 "unblinds" 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

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

Process

Step 1: Read Comparison Result

Note who won, the reasoning, and scores.

Step 2: Read Both Skills

Identify structural differences: instructions clarity, script/tool usage, example coverage, edge case handling.

Step 3: Read Both Transcripts

Compare execution patterns: how closely did each follow their skill's instructions? What tools were used differently? Where did the loser diverge?

Step 4: Analyze Instruction Following

For each transcript, score instruction following 1-10. Did the agent follow explicit instructions? Use provided tools/scripts? Miss opportunities? Add unnecessary steps?

Step 5: Identify Winner Strengths

Determine what made the winner better. Be specific. Quote from skills/transcripts.

Step 6: Identify Loser Weaknesses

Determine what held the loser back. Ambiguous instructions? Missing tools? Edge case gaps?

Step 7: Generate Improvement Suggestions

Produce actionable suggestions prioritized by impact. Categories: instructions, tools, examples, error_handling, structure, references. Priority levels: high, medium, low.

Output Format

{
  "comparison_summary": {
    "winner": "A",
    "winner_skill": "path/to/winner/skill",
    "loser_skill": "path/to/loser/skill",
    "comparator_reasoning": "Brief summary"
  },
  "winner_strengths": [
    "Clear step-by-step instructions for handling multi-page documents",
    "Included validation script that caught formatting errors"
  ],
  "loser_weaknesses": [
    "Vague instruction 'process the document appropriately' led to inconsistent behavior",
    "No script for validation, agent had to improvise"
  ],
  "instruction_following": {
    "winner": { "score": 9, "issues": ["Minor: skipped optional logging step"] },
    "loser": { "score": 6, "issues": ["Did not use the skill's formatting template"] }
  },
  "improvement_suggestions": [
    {
      "priority": "high",
      "category": "instructions",
      "suggestion": "Replace 'process the document appropriately' with explicit steps",
      "expected_impact": "Would eliminate ambiguity that caused inconsistent behavior"
    }
  ],
  "transcript_insights": {
    "winner_execution_pattern": "Read skill -> Followed 5-step process -> Used validation script",
    "loser_execution_pattern": "Read skill -> Unclear on approach -> Tried 3 different methods"
  }
}

Read the full file on GitHub · 132 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 · 132 lines · 0 tokens per session scan A d053fa54b2ec

Subscribe to this mod's changes

analyzer is an agent published in the GitHub repository MatthewYe/opencode-toolbox (5 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,153 tokens. 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 2026-08-31.

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