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 skills/przeprogramowani/10x-bench/10x-eval-modelnpx skills add przeprogramowani/10x-bench --skill 10x-eval-modelgit clone --depth 1 https://github.com/przeprogramowani/10x-benchWrote 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/skills/przeprogramowani/10x-bench/10x-eval-model)<a href="https://agentmods.dev/skills/przeprogramowani/10x-bench/10x-eval-model"><img src="https://agentmods.dev/badge/skills/przeprogramowani/10x-bench/10x-eval-model.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 | $0.00106 | $0.02196 |
| Opus 5 | $0.00053 | $0.01098 |
| Sonnet 5 | $0.00021 | $0.00439 |
| Haiku 4.5 | $0.00011 | $0.00220 |
Grade C, and why
10x-eval-model scanned grade C with 1 finding 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
If runs fail due to credit limits ("requires more credits"), inform the user with a link to https://openrouter.ai/settings/credits and offer to re-run after they top up. Clean attempt directories before re-running (`rm - How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
10x-eval-model
Orchestrates the full setup and execution pipeline for evaluating a new LLM model in the 10xBench benchmark. This includes updating metadata, verifying pricing, preparing attempt directories, and launching evaluation runs via the appropriate harness.
Inputs
The user provides:
- Model ID — the short identifier used in directory names (e.g.,
glm-51,gemini-31-pro) - Display name — human-readable name (e.g.,
GLM-5.1,Gemini 3.1 Pro) - Harness — the coding agent environment used to run the model (e.g.,
opencode,cursor,claude-code,claude-desktop,codex-desktop) - Supersedes (optional) — the model ID this new model replaces (e.g.,
glm-5is superseded byglm-51)
If the user provides a natural model name like "GLM-5.1 via opencode", derive the model ID yourself (e.g., glm-51) and confirm with the user before proceeding.
Pipeline Steps
Step 1: Check if model already exists in metadata
Read eval-attempts/metadata.ts and check whether the model ID is already present. If it exists but is commented out, offer to uncomment it instead of adding new entries. If it already exists and is active, inform the user and skip to Step 4.
Step 2: Look up pricing
Search the web for current API pricing for the model (per 1M tokens, input and output, USD, no cache). Present findings to the user for confirmation before writing to metadata. Check multiple sources (official docs, OpenRouter, Artificial Analysis) to cross-reference.
Step 3: Update metadata.ts
Add the model to all relevant sections in eval-attempts/metadata.ts:
ModelIdtype union — add| "model-id"entryAGENT_NAMES— add"model-id": "Display Name"entryAGENT_ENV— add"model-id": AGENT_ENVIRONMENT.<harness>entry (see harness mapping below)MODEL_PRICING— add"model-id": {input: X, output: Y}with confirmed pricingSUPERSEDED_MODELS(if applicable) — add"old-model-id": "new-model-id"entry
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 · 189 lines · 106 tokens per session scan C 5496a43ed928
10x-eval-model is a skill published in the GitHub repository przeprogramowani/10x-bench (11 stars, last pushed 17d ago), licensed MIT. It adds 106 tokens to every session and 2,196 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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