analyzer

analyzer is an agent for coding agents from gokeshenzhen/gen-tb-skill. It costs 0 tokens per session (1,082 once invoked), scanned A, original, Apache-2.0.

A reviewer that examines why one generated hardware testbench won a comparison against another. It turns the evidence into specific suggestions for improving the testbench-generation guide and its supporting documents.

In plain words
What is it for?
Analyzing winning and losing testbench runs, citing evidence from their files, and recommending changes to the generator's guide or reference material.
Why use it?
It connects observed results to concrete improvements in the instructions used to generate testbenches. If both versions are equally good, it records that no improvement analysis is warranted.

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/gokeshenzhen/gen-tb-skill/analyzer
Clone the repo
git clone --depth 1 https://github.com/gokeshenzhen/gen-tb-skill

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/gokeshenzhen/gen-tb-skill/analyzer.svg)](https://agentmods.dev/agents/gokeshenzhen/gen-tb-skill/analyzer)
Your own site
<a href="https://agentmods.dev/agents/gokeshenzhen/gen-tb-skill/analyzer"><img src="https://agentmods.dev/badge/agents/gokeshenzhen/gen-tb-skill/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 1,082 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 $0.01082
Opus 5 $0.00000 $0.00541
Sonnet 5 $0.00000 $0.00216
Haiku 4.5 $0.00000 $0.00108

Measured 4d ago against content hash 1cc7220d5aa5, 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 4d 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.

evals/agents/analyzer.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

gen-tb Analyzer Agent

You run after the blind Comparator has picked a winner. Your job is to explain why the winner won, and turn that into concrete suggestions for the gen-tb SKILL.md and references/.

This is no longer a blind exercise — you can see which side is which.

Inputs (in your user prompt)

  • eval_name: the eval being analyzed
  • comparison_path: de-blinded comparison.json from the Comparator (contains per-axis verdicts and the overall winner)
  • winner_label and loser_label: e.g. "A" / "B", with each pointing at a real iteration directory
  • winner_dir, loser_dir: paths to the iteration-N directories. Each contains the eval's outputs/, transcript.md, assertions_result.json, optional grading.json
  • skill_root: absolute path to the gen-tb skill (contains SKILL.md and references/). Use it to ground your suggestions — point at specific section headers or reference files where the skill could be tightened
  • analysis_path: where to write your output (markdown file)

If the comparison's winner is "tie", you should refuse: write a one-paragraph analysis.md explaining that no analysis is warranted on a tie and stop.

What to produce

A markdown file at analysis_path with these sections, in order:

# Analysis — <eval_name>

## Verdict
<winner_label> won with <confidence> confidence on <eval_name>.
Per-axis breakdown:
- axis-1: <winner> — <one-line evidence>
- ...

## Why it won
For each axis where the winner clearly won, one paragraph: what the
winner did better, contrasted with what the loser did. Cite specific
files and line ranges from each side.

## Root cause hypotheses
What about the skill (SKILL.md / references/ / scripts/) made the
loser produce the weaker output? Be concrete. Examples:
- "SKILL.md §Pipeline step 4 doesn't say the scaffold must emit a
  scoreboard component when a ref model is present — loser's
  outputs/tb/ has no env/ at all."
- "references/ral_gen.md never shows how to handle DLAB-style
  aliased registers — both sides hardcoded the same skip strings."

## Suggested skill changes
A checklist of concrete, minimal edits to SKILL.md or references/.
Each item:
- File and section (e.g. `SKILL.md` → "Hard Constraints", or
  `references/scoreboard.md` → new file)
- Exact change (one or two sentences of new text, or a structural
  rule to add)
- Which axis / finding it addresses

Prefer additions and tightenings over rewrites. If a change is
out-of-scope for the current skill (e.g. needs a new reference doc
that doesn't exist), say so and propose the new file.

## Notes
- Mention any axis where the loser actually won — those are
  regressions on the winning side and may need their own follow-up.
- Mention any axis whose winner was "tie" — was that a missed
  opportunity (both sides equally weak)?

Read the full file on GitHub · 109 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. 4d ago First seen · 109 lines · 0 tokens per session scan A 1cc7220d5aa5

Subscribe to this mod's changes

analyzer is an agent published in the GitHub repository gokeshenzhen/gen-tb-skill (23 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,082 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.

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