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 pareto-frontiergit 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/pareto-frontier)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/pareto-frontier"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/pareto-frontier.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00020 | $0.00351 |
| Opus 5 | $0.00010 | $0.00176 |
| Sonnet 5 | $0.00004 | $0.00070 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
pareto-frontier 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.
What it actually says
Pareto Frontier Identification
A point is Pareto-optimal if no other point is better in all objectives. For this task, we want to maximize F1 and minimize Delta.
Logic
A solution A dominates B if:
A.F1 >= B.F1ANDA.Delta <= B.Delta- At least one inequality is strict.
Python Implementation
def is_pareto_efficient(costs):
"""
Find the pareto-efficient points
:param costs: An (n_points, n_costs) array where costs are to be MINIMIZED.
:return: A boolean array of length n_points indicating efficiency.
"""
is_efficient = np.ones(costs.shape[0], dtype=bool)
for i, c in enumerate(costs):
if is_efficient[i]:
# Keep any point that is better than 'c' in at least one attribute
# OR equal in all attributes (to handle duplicates)
is_efficient[is_efficient] = np.any(costs[is_efficient] < c, axis=1) | \
np.all(costs[is_efficient] == c, axis=1)
is_efficient[i] = True # And keep self
return is_efficient
# For Max F1 and Min Delta, transform F1:
# costs = np.array([[-f1, delta] for f1, delta in results])
# efficient_mask = is_pareto_efficient(costs)
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 · 37 lines · 20 tokens per session scan A 6cbadbd656a4
pareto-frontier is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 351 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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