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 HCAI-Lab-GT/gt-hive --skill pace-icegit clone --depth 1 https://github.com/HCAI-Lab-GT/gt-hiveWrote 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/hcai-lab-gt/gt-hive/pace-ice)<a href="https://agentmods.dev/skills/hcai-lab-gt/gt-hive/pace-ice"><img src="https://agentmods.dev/badge/skills/hcai-lab-gt/gt-hive/pace-ice/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hcai-lab-gt/gt-hive/pace-ice"><img src="https://agentmods.dev/badge/skills/hcai-lab-gt/gt-hive/pace-ice.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00051 | $0.03031 |
| Opus 5 | $0.00026 | $0.01515 |
| Sonnet 5 | $0.00010 | $0.00606 |
| Haiku 4.5 | $0.00005 | $0.00303 |
Grade A, and why
pace-ice 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 11d 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PACE ICE Overlay
This skill is the ICE-specific overlay for Georgia Tech PACE. It adds site
facts that slurm-core deliberately does not know.
What this overlay adds
Load this skill when the user is working on (or asking about) the PACE ICE
cluster — the instructional PACE cluster at Georgia Tech for
credit-bearing coursework, TA grading workflows, and GT-hosted workshops.
ICE is free to GT students and instructors with a valid GT account; no
charge account is configured and no -A flag is required.
The layered model is: load slurm-core for portable Slurm patterns
(sbatch, srun, salloc, sacct, job arrays, dependencies, debugging
checklists), plus this overlay for ICE-specific account/QOS/storage/policy
decisions. Do not duplicate slurm-core content here; reference it
instead.
Ground truth for every claim in this skill is docs/PACE Documentation/
(an export of the official PACE knowledge base). When in doubt, re-verify
against those docs and cite the specific page used.
When to use ICE
Route to this overlay when any of the following apply:
- Credit-bearing course context — a course Slurm assignment, course project, or in-class lab.
- Teaching/grading workflows — instructor or TA grading scripts that need cluster compute.
- GT-hosted workshops or training — short-form classroom sessions using PACE.
- Explicit user mention of ICE,
<gt-login-host-redacted>, the instructional cluster, or OnDemand athttps://ondemand-ice.pace.gatech.edu/.
Do NOT use this overlay for:
- Production research workflows — those go to the
pace-phoenixoverlay (paid research cluster with charge accounts and explicit QOS selection). - Generic Slurm questions without a cluster named — those use
slurm-corealone, with no site overlay.
Routing
- Use this overlay alone for: ICE login/portal pointers, partition
auto-routing rules, college-priority and grading QOS choice, GPU type
selection, storage-tier guidance, semester-cleanup caveats, and the
no-
-A-flag rule. - Pair with
slurm-corefor: writing the actualsbatchscript, interactivesalloc/srunworkflows, job arrays, dependencies, sacct/squeue debugging — anything portable across Slurm clusters. ICE-specific values (no-A, optional-q coc-ice/coe-ice/pace-ice,--gres=gpu:<TYPE>:N,-C intel/-C amd/-C graniterapids) come from this overlay; the surrounding Slurm scaffolding comes fromslurm-core.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 254 lines · 51 tokens per session scan A 7a342d941096
pace-ice is a skill published in the GitHub repository HCAI-Lab-GT/gt-hive (4 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 3,031 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…