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 skills add cxcscmu/SkillLearnBench --skill glm-runnergit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/glm-runner)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/glm-runner"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/glm-runner.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 5.1 | $0.00018 | $0.00258 |
| Opus 5 | $0.00009 | $0.00129 |
| Sonnet 5 | $0.00004 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
glm-runner 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 3d 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
GLM Runner
Overview
The GLM model is typically a standalone binary executable. Running it involves ensuring all input files (namelist, forcing, initialization) are correctly linked and accessible.
Setup
Ensure the glm binary is in your PATH.
Check its version with:
glm --version
Running the Simulation
GLM is usually run from the directory containing glm3.nml.
# Execute GLM
glm
Output Monitoring
GLM logs progress to standard output. Success is usually indicated by the model finishing its time steps.
Output is typically written to a NetCDF file, e.g., output.nc.
Troubleshooting
- Input Error: Check that all CSV forcing files (e.g., in
bcs/) have the columns specified inglm3.nml. - Config Error: Namelist errors in
glm3.nml(like typos or missing parameters) will cause GLM to crash or use default values. - Range Error: Some parameters have physical limits. If GLM crashes early, check your parameter values.
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.
- 3d ago First seen · 33 lines · 18 tokens per session scan A c2769d1a21ae
glm-runner is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 258 once invoked, about $0.0001 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 2026-09-03.
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