frontierharness-eval
frontierharness-eval is an LLM evaluation & observability skill for AI agents, published by frontier-harness-eval in the eval repository on GitHub. Skillspace rates frontierharness-eval D (61/100) and it passed the safety scan; the rating is based on static analysis only.
Benchmark a third-party coding-agent harness against FrontierHarness Eval using Runta runtimes. Provisions a clean runtime bound to the GitHub repo under evaluation, installs the Harbor and Pier stacks needed for Terminal-Bench and DeepSWE tasks, freezes a golden checkpoint, runs tasks from identical fresh restores while saving trajectories as evidence, generates a comparison diagram, and builds a shareable report. Use when evaluating, benchmarking, scoring, or comparing a coding agent harness, or when the user mentions FrontierHarness, DeepSWE, Terminal-Bench, Harbor, Pier, golden checkpoints, or harness trajectories.
- Grade
- D
- Quality
- 61 / 100
- Safety
- Passed
- GitHub stars
- 117
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 95 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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