spending-effort-with-jev
claudeJudges each prompt's /effort level with TypeSafe's Jev and switches on your OK
Elizabeth (Gintama) walks a context-window progress bar above the prompt, with usage breakdown, turns to auto-compact, and rate limits
Plugin elizabeth-progress from marketplace spending-effort-with-jev in Yaxin9Luo/spending-effort-with-jev. Commands read from the repo's manifest — 29 days ago.
No skills, commands, agents, hooks or MCP servers were found in the standard plugin folders.
Judges each prompt's /effort level with TypeSafe's Jev and switches on your OK
Appends two or three short follow-up questions after a substantive answer to help users check key facts, probe the reasoning, and notice missing context
19 skills
Security default for organizations: seated outermost, it keeps the organization's classic hooks, prompt content, settings, tool policy and pinned environment out of reach of the plugins a person installs, and adds no policy of its own; one managed option, allowManagedModsOnly, limits mods to the organization's.
Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data
9 skills · 9 commands · 11 agents · 1 MCP server
Skill: add headroom-ai compress() to a Python or TypeScript app to cut LLM input tokens (tool outputs, logs, JSON, chat history). No API key.
1 skill
Up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your coding agent's context.
1 MCP server
Perform AI code review on Git diffs — supports workspace changes, branch ranges, and single commits with concurrent per-file analysis, codebase search, and deep context-aware review.
2 commands
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