Catch and fix LLM 'style smells' in non-fiction prose — quality signals, not an AI-detection verdict.
Plugin plainly from marketplace mapika in Mapika/claude-plugins. Commands read from the repo's manifest — 1 month ago.
This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.
writing-clean-proseThis skill should be used when writing or editing non-fiction prose for a human reader — essays, blog posts, documentation, READMEs, marketing copy, release notes — so the output avoids LLM "style smells" and follows a positive prose standard. It applies when drafting, rewriting, or polishing any su
/plainly:checkAudit prose for LLM style smells and report tiered findings with rewrites (no edits).
deslopperUse this agent when the user wants an AI-sounding non-fiction draft rewritten into clean human prose, not just diagnosed. Typical triggers include "deslop this", "fix the AI tells in this draft", "make this read more human", and "rewrite this so it doesn't sound like ChatGPT". See "When to invoke" i
prose-judgeUse this agent to blindly compare two versions of a piece of non-fiction prose on the same topic and decide which reads more like a human genuinely wrote it. Typically dispatched by the deslopper agent after editing, to confirm a rewrite actually reads more human — not just that its metrics moved. T
tell-matcherUse this agent to check a piece of non-fiction prose against the plainly "constitution" of LLM writing tells and return the semantic ones a regex cannot catch — manufactured antithesis (including contractions like "isn't just X — it's Y"), inflated boosters, pervasive hedging, copula-avoidance, form
python3 ${CLAUDE_PLUGIN_ROOT}/hooks/on_write.py
Teach appropriately-expressive backend code (Python/TS/Go): when to comment, abstract, validate, and handle errors. Every rule has a when-NOT guard, so it curbs LLM over-commenting/over-abstraction without overcorrecting into cryptic code. Bundles real before/after examples and a tripwire scanner.
2 skills
Generate matplotlib figures in a soft-pastel, warm-earth, research-blog visual register — bold display titles, smoothed trends with shaded bands, rounded bars, minimal axes. Conference-ready white background by default.
1 skill
Read research papers with strict per-claim citations. Fetches PDF + tex source + real BibTeX from open sources (OpenAlex, Crossref, DBLP, ACL, Semantic Scholar, arXiv). Never fabricates citations.
1 skill
Security review for Claude-generated code. Pattern-based warnings on edits, LLM-powered diff review on Stop, and an agentic commit reviewer that catches injection, XSS, SSRF, hardcoded secrets, and 25+ other vulnerability classes.
hooks
AgentDB (external: maintained in ruvnet/agentdb, pinned to commit 27678c3, plugin 3.6.0): agentdb's own standalone MCP server pinned to [email protected] (vector memory with get/list/update/delete for records, patterns and skills, hybrid keyword + vector episode search and an opt-in local cross-encoder, an opt-in aidefence injection guard on memory writes, reflexion, skills, outcome feedback, capabilities report), a memory-specialist agent, 3 skills, /agentdb-status, and the agentdb mod guard that keeps secrets out of memory and refuses delete/reset unless allowed. Complements ruflo-agentdb, which reaches AgentDB through ruflo's server
3 skills · 1 command · 1 agent · 1 MCP server
Generate natively editable PPTX from PDF / DOCX / URL / Markdown — real DrawingML shapes, text boxes, charts, and animations. Note: run `pip install -r requirements.txt` inside the installed plugin directory for the Python post-processing scripts to work.
1 skill
Rewrite AI-sounding text so it reads like the writer without changing what it says.
1 skill
5 skills + 35 modes + Material Passport pipeline. Includes v3.6.7 cross-model audit gate and v3.6.8 generator-evaluator contract.
5 skills · 16 commands · 4 agents · hooks
OpenAPI specification generation, Mermaid diagram creation, tutorial writing, API reference documentation
3 skills · 1 command · 5 agents
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