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 explyt/spring-plugin --skill review-statistic-coveragegit clone --depth 1 https://github.com/explyt/spring-pluginWrote 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/explyt/spring-plugin/review-statistic-coverage)<a href="https://agentmods.dev/skills/explyt/spring-plugin/review-statistic-coverage"><img src="https://agentmods.dev/badge/skills/explyt/spring-plugin/review-statistic-coverage/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/explyt/spring-plugin/review-statistic-coverage"><img src="https://agentmods.dev/badge/skills/explyt/spring-plugin/review-statistic-coverage.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00085 | $0.02541 |
| Opus 5.5 | $0.00034 | $0.01016 |
| Sonnet 5.5 | $0.00017 | $0.00508 |
| Haiku 4.5 | $0.00009 | $0.00254 |
Grade A, and why
review-statistic-coverage 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 2mo 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Usage Statistics Coverage Check (for explyt/spring-plugin reviews)
Why this skill exists. Without usage counters we cannot measure how the plugin is actually used, which means we cannot tell whether a new feature ships dead, which quick fixes users actually apply, or how often a tool-window control is touched. Missing statistics on a new user-visible action is treated as a Warning-severity (High) finding for this project: existing coverage is broad but not enforced as a hard error, so flag gaps prominently without blocking on them.
Scope. This skill applies only to reviews in the
explyt/spring-pluginrepository. Apply this check on every diff that touches user-facing features before producing the final review.
1. Where usage statistics live in this project
All infrastructure is in modules/spring-core/src/main/kotlin/com/explyt/spring/core/statistic/:
| Component | Role |
|---|---|
StatisticActionId |
Enum of all tracked actions; each entry has a human-readable description string. The single source of truth for what is tracked. |
StatisticService |
APP-level @Service. addActionUsage(actionId) increments a persisted counter; gated by SpringToolRunConfigurationsSettingsState.isCollectStatistic and skipped in unit-test/headless/non-production modes. |
StatisticUtil.registerActionUsage(fixKey, previewKey) |
Editor? extension for quick fixes: logs fixKey in the main editor and previewKey (if non-null) in the intention-preview editor. |
StatisticInsertHandler(actionId) |
InsertHandler for completion lookup elements: logs on actual insertion, not on mere suggestion. |
StatisticState |
SimplePersistentStateComponent cache holding the counter map. |
StatisticStartupActivity |
Flushes counters to file on startup. |
Canonical call patterns to model after:
- Action / gutter handler / tool-window button:
StatisticService.getInstance().addActionUsage(StatisticActionId.GENERATE_WEB_METHOD)— seeSpringWebKotlinMethodGenerateAction,EndpointIconGutterHandler,RunInSwaggerAction. - Quick fix with preview:
editor.registerActionUsage(QUICK_FIX_..., PREVIEW_...)at the start ofinvoke()— seeAddPathVariableQuickFix. - Completion insertion:
.withInsertHandler(StatisticInsertHandler(COMPLETION_...))— seeOpenApiYamlRefCompletionContributor,ProfilesCompletionContributor. - Completion provider that always contributes:
addActionUsageinsideaddCompletions— seeSpringDataMethodCompletionProvider(use this only when showing the lookup itself is the signal; preferStatisticInsertHandlerwhen acceptance is the signal). - Wizard/settings interaction:
addActionUsagein the event handler — seeSpringInitializrWizardStep,SETTINGS_OPEN/SETTINGS_CHANGED.
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.
- 2mo ago First seen · 165 lines · 85 tokens per session scan A 285c6add67ee
review-statistic-coverage is a skill published in the GitHub repository explyt/spring-plugin (166 stars, last pushed yesterday), licensed Apache-2.0. It adds 85 tokens to every session and 2,541 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
create-test-project
Create an OpenTaint test project with positive/negative samples for verifying a rule or approximation. Use when a rule or approximation needs a test project to check against.
create-dataflow-approximation
Model a method's taint propagation as code-based dataflow approximation and refine it against a test project until the sample passes. Use for a dropped method that requires code-based approximation.
debug-rule
Debug a rule or approximation that behaves unexpectedly by tracing where taint is dropped. Use when its samples won't pass after repeated attempts, or it passes tests but is wrong on a real scan.
write-fixture
Write Java/Kotlin tests with Fixture Monkey — enumerate the cases a method can produce, pick the ones worth testing, and build each fixture pinning only the properties that force the expected outcome.
analyze-external-methods
Analyze an OpenTaint scan's dropped external methods and decide which of them are propagators and optionally sinks. Use when a dropped-external-methods.yaml needs classification for dropped method type.
create-rule
Author and verify an OpenTaint rule. Use whenever a rule creation is needed.