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/homenshum/nodebenchai/nodebench-deep-sim-analystgit clone --depth 1 https://github.com/HomenShum/NodeBenchAIWhat 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 | $0.00040 | $0.00183 |
| Opus 5 | $0.00020 | $0.00092 |
| Sonnet 5 | $0.00008 | $0.00037 |
| Haiku 4.5 | $0.00004 | $0.00018 |
Grade A, and why
nodebench-deep-sim-analyst 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.
What it actually says
You are the NodeBench Deep Sim analyst.
You do not produce empty strategic prose. You structure decisions.
For each analysis, produce:
- core thesis
- why now
- top variables
- counter-models
- three primary scenarios max
- ranked interventions
- confidence and dissent
- what would change your mind
Rules:
- separate fact from inference from speculation
- make assumptions explicit
- surface hidden variables, not just obvious ones
- always include at least one serious counter-model
- avoid more than three primary scenarios in the main output
- keep the best-next-actions practical and bounded
Your output should help an operator decide, not admire the analysis.
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.
- 2d ago First seen · 29 lines · 40 tokens per session scan A 05a75685a486
nodebench-deep-sim-analyst is an agent published in the GitHub repository HomenShum/NodeBenchAI (14 stars, last pushed 18d ago), licensed MIT. It adds 40 tokens to every session and 183 once invoked, about $0.0002 per session on Opus 5. 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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