analyzer

A post-result analysis agent for comparing two blind test outputs, where the evaluator chooses a winner without initially seeing which method produced which result. It then examines the methods and transcripts to explain the outcome.

In plain words
What is it for?
Use it after a blind comparison to read the result, both skills, and their execution transcripts, then save suggestions to an analysis file.
Why use it?
It turns a simple win or loss into concrete lessons about what worked and what should change. This helps improve the weaker method based on evidence from the comparison.

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/smixs/skill-conductor/analyzer
Clone the repo
git clone --depth 1 https://github.com/smixs/skill-conductor
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 3,784 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 $0.03784
Opus 5 $0.00000 $0.01892
Sonnet 5 $0.00000 $0.00757
Haiku 4.5 $0.00000 $0.00378

Measured 2d ago against content hash 55b5cba3699f, 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 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.

skills/skill-conductor/agents/analyzer.md · 407 lines

How it starts

The opening of the file, as written. The whole thing — 407 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 · 407 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. 2d ago First seen · 407 lines · 0 tokens per session scan A 55b5cba3699f

Subscribe to this mod's changes

analyzer is an agent published in the GitHub repository smixs/skill-conductor (163 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,784 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 2026-08-30.