PluginEval quality methodology — dimensions, rubrics, statistical methods, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitnes

Evaluation methodology PluginEval scores a skill or a plugin from 0 to 100 by combining up to three layers. The static layer is a lint. It's fast and deterministic, and it's useful for checking structure. The LLM judge and Monte Carlo layers are experimental, not validated against human labels, so treat their numbers as rough signals. For the trace based eval program, see evals/README.md at the repository root. The judge rubric anchors are in [references/rubrics.md](references/rubrics.md). Fixes for each anti pattern and tips for each dimension are in [references/improving scores.md](references/improving scores.md). Evaluation depths Depth Layers Confidence label quick static Estimated standard (default for score ) static and judge Assessed deep (used by certify ) static, judge, and Monte Carlo with 50 runs Certified thorough static, judge, and Monte Carlo with 100 runs Certified+ The label names the depth that ran, not a check against human judgment. A plugin directory gets the static layer only, whatever depth you ask for. Static layer (lint) The static layer reads SKILL.md and makes no model calls. It computes seven sub scores, and the first six feed composite dimensions: frontmatter quality (feeds triggering accuracy ) orchestration wiring (feeds orchestration fitness ) progressive disclosure , structural completeness , token efficiency , ecosystem coherence harness portability harness portability maps to no dimension, so it doesn't change a skill's composite score. It does carry 6% of the static layer's own score. A plugin's score is built from that layer score, so portability findings can lower a plugin's score a little. Its findings are not counted as anti patterns. The static layer flags six anti patterns: OVER CONSTRAINED , EMPTY DESCRIPTION , MISSING TRIGGER , BLOATED SKILL , ORPHAN REFERENCE , and DEAD CROSS REF . Each flag cuts the score by 5%, down to a floor of 50%: The report prints a severity for each flag, but the penalty counts flags and ignores severity. LLM judge layer (experimental) The judge layer makes four model calls and returns four holistic scores from 0 to 1: triggering accuracy : Haiku reads the description and writes 10 test prompts, 5 that should trigger and 5 that should not. It predicts the outcome for each prompt and reports its own F1. Nothing checks those predictions against real triggering. orchestration fitness and scope calibration : Sonnet rates the skill on a five point rubric. output quality : Sonnet imagines three tasks and rates the output it expects. The three Sonnet calls see only the first 3,000 characters of SKILL.md. Only one judge runs, because nothing reads the judges setting. Monte Carlo layer (experimental) Haiku writes 15 prompts that should trigger the skill, and the layer repeats them to reach 50 runs (100 at thorough ). Each run sends the SKILL.md text and one prompt to the model, and the layer records four measures: Activation rate is the share of runs with any non empty reply, so it shows whether the model answered, not whether the skill should have fired. Output quality is reply length divided by 500, capped at 1.0. Failure rate is the share of runs that errored. Token efficiency is 1 median tokens / 8000 . Every prompt is one that should trigger, so the layer never checks that the skill stays out of unrelated requests. The layer's JSON includes Wilson, bootstrap, and Clopper Pearson intervals for its own measures. The composite ci lower and ci upper fields are always null. Composite score First, for each dimension, the engine blends the layer scores that exist, and it renormalizes the blend weights over those layers. Second, it sums the weighted dimension scores, and it renormalizes the dimension weights over the dimensions that have a score. Third, it multiplies the sum by 100 and by the anti pattern penalty. Dimension Weight Static Judge Monte Carlo triggering accuracy 0.25 0.15 0.25 0.60 orchestration fitness 0.20 0.10 0.70 none output quality 0.15 none 0.40 0.60 scope calibration 0.12 none 0.55 none progressive disclosure 0.10 0.80 none none token efficiency 0.06 0.40 none 0.50 robustness 0.05 none none 0.80 structural completeness 0.03 0.90 none none code template quality 0.02 none none none ecosystem coherence 0.02 0.85 none none A cell reads "none" when its layer produces no score for the dimension, even where LAYER BLENDS lists a weight. No layer produces code template quality , so it's always unmeasured. For a plugin directory, the composite is the static layer's mean score across the plugin's skills and agents, times 100, times the penalty for the plugin's anti pattern count. Badges and grades Badges come from the composite score alone. Platinum needs at least 90, Gold at least 80, Silver at least 70, and Bronze at least 60. Badge.from scores accepts an Elo rating, but no command computes one. Plugin level badges, including the ones in the weekly CI report, come from the static layer alone. Skill level badges at standard depth or deeper also include the experimental layers. Each measured dimension gets a letter grade on the 0 to 100 scale, from A+ at 97 down to D at 60, and F below 60. Usage At standard depth or deeper, score , certify , and compare print a note on stderr that the judge and Monte Carlo layers are experimental. For a plugin directory, the CLI prints a warning that only the static layer runs instead. With threshold , the command exits with code 1 when the composite is below the value. plugin eval init writes a corpus index, but no other command reads it. Examples An abridged example of the JSON output follows. Scripts can read composite.score from it: Troubleshooting When a score drops after you add content, check layers[0].anti patterns in the JSON. If triggering accuracy is low at quick depth, add a trigger phrase such as "Use this skill when" to the description, followed by several comma separated contexts. Judge scores change between runs, because the model writes new test prompts and tasks each time. Use the static layer for comparisons you want to repeat. If stderr says the judge could not measure some dimensions, install the LLM extra with uv sync extra llm . Related The eval judge agent scores the four judge dimensions inside Claude Code, and the eval orchestrator agent runs the CLI and merges the results.

evaluation-methodology