PluginEval quality methodology, covering dimensions, rubrics, 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 fitness, when setting score thresholds for your marketplace, or when explaining quality badges to external partners like Neon.
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SKILL.md
evaluation-methodology
PluginEval quality methodology, covering dimensions, rubrics, 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 fitness, when setting score thresholds for your marketplace, or when explaining quality badges to external partners like Neon.
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. Fixes for each
anti-pattern and tips for each dimension are in
references/improving-scores.md.
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
plugin-eval score ./path/to/skill --depth quick # static lint only
plugin-eval score ./path/to/skill # static and judge
plugin-eval certify ./path/to/skill # deep depth
plugin-eval compare ./skill-a ./skill-b # quick depth by default
plugin-eval score ./path/to/skill --depth quick --output json --threshold 70
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:
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.
