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.
npx agentmods add agents/gokeshenzhen/gen-tb-skill/analyzergit clone --depth 1 https://github.com/gokeshenzhen/gen-tb-skillWrote 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.
[](https://agentmods.dev/agents/gokeshenzhen/gen-tb-skill/analyzer)<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>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.
| Model | Per session | Once 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 |
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.
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 analyzedcomparison_path: de-blindedcomparison.jsonfrom the Comparator (contains per-axis verdicts and the overall winner)winner_labelandloser_label: e.g."A"/"B", with each pointing at a real iteration directorywinner_dir,loser_dir: paths to the iteration-N directories. Each contains the eval'soutputs/,transcript.md,assertions_result.json, optionalgrading.jsonskill_root: absolute path to the gen-tb skill (containsSKILL.mdandreferences/). Use it to ground your suggestions — point at specific section headers or reference files where the skill could be tightenedanalysis_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)?
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.
- 4d ago First seen · 109 lines · 0 tokens per session scan A 1cc7220d5aa5
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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